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Optimización con algoritmos de templado cuántico (“quantum annealing”)
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Hernández Caceres, J. M.; Fernández Rúa, I.; Fernández-Combarro Álvarez, E.
Efficient quantum algorithms to find substructures on finite algebras Artículo de revista
En: Quantum Information and Computation , vol. 23, no 15, 2024, ISBN: 1533-7146.
Resumen | Enlaces | BibTeX | Etiquetas: UNIOVI
@article{nokey,
title = {Efficient quantum algorithms to find substructures on finite algebras},
author = {Hernández Caceres, J. M. and Fernández Rúa, I. and Fernández-Combarro Álvarez, E. },
url = {https://www.rintonpress.com/xxqic23/qic-23-1516/1275-1290.pdf},
doi = {doi.org/10.26421/QIC23.15-16-2},
isbn = {1533-7146},
year = {2024},
date = {2024-12-16},
journal = {Quantum Information and Computation },
volume = {23},
number = {15},
abstract = {When classifying a collection of finite algebras (for instance, in the computational classification of finite semifields), an important task is the determination of substructures such as the right, middle, and left nuclei, the nucleus, and the center. Finding these structures may become computationally expensive when there is no additional information about the algebra properties. In this paper, we introduce quantum algorithms than solve this task efficiently, by formulating it as an instance of the Hidden Subgroup Problem (HSP) {over Abelian groups}. We give detailed constructions of the quantum circuits involved in the process and prove that the overall (quantum) complexity of our algorithm is polynomial in the dimension of the algebra, while a similar approach with classical computers would require an exponential number of queries to the HSP function},
keywords = {UNIOVI},
pubstate = {published},
tppubtype = {article}
}
When classifying a collection of finite algebras (for instance, in the computational classification of finite semifields), an important task is the determination of substructures such as the right, middle, and left nuclei, the nucleus, and the center. Finding these structures may become computationally expensive when there is no additional information about the algebra properties. In this paper, we introduce quantum algorithms than solve this task efficiently, by formulating it as an instance of the Hidden Subgroup Problem (HSP) {over Abelian groups}. We give detailed constructions of the quantum circuits involved in the process and prove that the overall (quantum) complexity of our algorithm is polynomial in the dimension of the algebra, while a similar approach with classical computers would require an exponential number of queries to the HSP function
Farreras, M.; Cervera-Lierta, A.
Simulation of the 1d XY model on a quantum computer Sin publicar
Preprint, 2024.
Enlaces | BibTeX | Etiquetas: quantic
@unpublished{nokey,
title = {Simulation of the 1d XY model on a quantum computer},
author = {Farreras, M. and Cervera-Lierta, A.},
url = {https://doi.org/10.48550/arXiv.2410.21143
},
doi = {doi.org/10.48550/arXiv.2410.21143},
year = {2024},
date = {2024-10-28},
howpublished = {Preprint},
keywords = {quantic},
pubstate = {published},
tppubtype = {unpublished}
}
Fanchiotti, H.; García-Canal, C. A.; Mayosky, M.; Pérez, A.; Veiga, A.
Quantum and classical dynamics correspondence and the brachistochrone problem Artículo de revista
En: Physical Review A, vol. 110, iss. 4, 2024, ISSN: 2469-9926 .
Resumen | Enlaces | BibTeX | Etiquetas: UV
@article{nokey,
title = {Quantum and classical dynamics correspondence and the brachistochrone problem},
author = {Fanchiotti, H. and García-Canal, C.A. and Mayosky, M. and Pérez, A. and Veiga, A. },
url = {https://journals.aps.org/pra/abstract/10.1103/PhysRevA.110.042219},
doi = {doi.org/10.1103/PhysRevA.110.042219},
issn = {2469-9926 },
year = {2024},
date = {2024-10-22},
journal = {Physical Review A},
volume = {110},
issue = {4},
abstract = {The decomplexification procedure, which allows showing mathematically the isomorphism between classical and quantum dynamics of systems with a finite number of basis states, is exploited to propose resonant electric circuits with gyrator-based couplings and to experimentally study the quantum brachistochrone problem, particularly the passage time in Hermitian and parity-time-symmetric cases.},
keywords = {UV},
pubstate = {published},
tppubtype = {article}
}
The decomplexification procedure, which allows showing mathematically the isomorphism between classical and quantum dynamics of systems with a finite number of basis states, is exploited to propose resonant electric circuits with gyrator-based couplings and to experimentally study the quantum brachistochrone problem, particularly the passage time in Hermitian and parity-time-symmetric cases.
Ugo Nzongani, U.; Eon, N.; Márquez-Martín, I.; Pérez, A.; Di Molfetta, G.; Arrighi, P.
Dirac quantum walk on tetrahedra Artículo de revista
En: Physical Review A, vol. 110, iss. 4, 2024, ISSN: 2469-9926 .
Resumen | Enlaces | BibTeX | Etiquetas: UV
@article{nokey,
title = {Dirac quantum walk on tetrahedra},
author = {Ugo Nzongani, U. and Eon, N. and Márquez-Martín, I. and Pérez, A. and Di Molfetta, G. and Arrighi, P.},
url = {https://journals.aps.org/pra/abstract/10.1103/PhysRevA.110.042418},
doi = {doi.org/10.1103/PhysRevA.110.042418},
issn = {2469-9926 },
year = {2024},
date = {2024-10-16},
journal = {Physical Review A},
volume = {110},
issue = {4},
abstract = {Discrete-time quantum walks (QWs) are transportation models of single quantum particles over a lattice. Their evolution is driven through causal and local unitary operators. QWs are a powerful tool for quantum simulation of fundamental physics, as some of them have a continuum limit converging to well-known physics partial differential equations, such as the Dirac or the Schrödinger equation. In this paper, we show how to recover the Dirac equation in (3+1) dimensions with a QW evolving in a tetrahedral space. This paves the way to simulate the Dirac equation on a curved space-time. This also suggests an ordered scheme for propagating matter over a spin network, of interest in loop quantum gravity, where matter propagation has remained an open problem.},
keywords = {UV},
pubstate = {published},
tppubtype = {article}
}
Discrete-time quantum walks (QWs) are transportation models of single quantum particles over a lattice. Their evolution is driven through causal and local unitary operators. QWs are a powerful tool for quantum simulation of fundamental physics, as some of them have a continuum limit converging to well-known physics partial differential equations, such as the Dirac or the Schrödinger equation. In this paper, we show how to recover the Dirac equation in (3+1) dimensions with a QW evolving in a tetrahedral space. This paves the way to simulate the Dirac equation on a curved space-time. This also suggests an ordered scheme for propagating matter over a spin network, of interest in loop quantum gravity, where matter propagation has remained an open problem.
deMarti iOlius, A.; Fuentes, P.; Orús, R.; Crespo, P.; Etxezarreta Martinez, J.
Decoding algorithms for surface codes Artículo de revista
En: Quantum, vol. 8, pp. 1498, 2024, ISBN: 2521-327X.
Resumen | Enlaces | BibTeX | Etiquetas: tecnun
@article{nokey,
title = {Decoding algorithms for surface codes},
author = {deMarti iOlius, A. and Fuentes, P. and Orús, R. and Crespo, P. and Etxezarreta Martinez, J. },
url = {https://quantum-journal.org/papers/q-2024-10-10-1498/pdf/},
doi = {doi.org/10.22331/q-2024-10-10-1498},
isbn = {2521-327X},
year = {2024},
date = {2024-10-10},
journal = {Quantum},
volume = {8},
pages = {1498},
abstract = {Quantum technologies have the potential to solve certain computationally hard problems with polynomial or super-polynomial speedups when compared to classical methods. Unfortunately, the unstable nature of quantum information makes it prone to errors. For this reason, quantum error correction is an invaluable tool to make quantum information reliable and enable the ultimate goal of fault-tolerant quantum computing. Surface codes currently stand as the most promising candidates to build near term error corrected qubits given their two-dimensional architecture, the requirement of only local operations, and high tolerance to quantum noise. Decoding algorithms are an integral component of any error correction scheme, as they are tasked with producing accurate estimates of the errors that affect quantum information, so that they can subsequently be corrected. A critical aspect of decoding algorithms is their speed, since the quantum state will suffer additional errors with the passage of time. This poses a connundrum, where decoding performance is improved at the expense of complexity and viceversa. In this review, a thorough discussion of state-of-the-art decoding algorithms for surface codes is provided. The target audience of this work are both readers with an introductory understanding of the field as well as those seeking to further their knowledge of the decoding paradigm of surface codes. We describe the core principles of these decoding methods as well as existing variants that show promise for improved results. In addition, both the decoding performance, in terms of error correction capability, and decoding complexity, are compared. A review of the existing software tools regarding surface codes decoding is also provided.},
keywords = {tecnun},
pubstate = {published},
tppubtype = {article}
}
Quantum technologies have the potential to solve certain computationally hard problems with polynomial or super-polynomial speedups when compared to classical methods. Unfortunately, the unstable nature of quantum information makes it prone to errors. For this reason, quantum error correction is an invaluable tool to make quantum information reliable and enable the ultimate goal of fault-tolerant quantum computing. Surface codes currently stand as the most promising candidates to build near term error corrected qubits given their two-dimensional architecture, the requirement of only local operations, and high tolerance to quantum noise. Decoding algorithms are an integral component of any error correction scheme, as they are tasked with producing accurate estimates of the errors that affect quantum information, so that they can subsequently be corrected. A critical aspect of decoding algorithms is their speed, since the quantum state will suffer additional errors with the passage of time. This poses a connundrum, where decoding performance is improved at the expense of complexity and viceversa. In this review, a thorough discussion of state-of-the-art decoding algorithms for surface codes is provided. The target audience of this work are both readers with an introductory understanding of the field as well as those seeking to further their knowledge of the decoding paradigm of surface codes. We describe the core principles of these decoding methods as well as existing variants that show promise for improved results. In addition, both the decoding performance, in terms of error correction capability, and decoding complexity, are compared. A review of the existing software tools regarding surface codes decoding is also provided.
Pranav, C.; Koushik, P.; Garcia-de-Andoin, M.; Ban, Y.; Sanz, M.; Chen, X.
Photonic counterdiabatic quantum optimization algorithm Artículo de revista
En: Communications Physics, no 315, 2024, ISBN: 2399-3650.
Resumen | Enlaces | BibTeX | Etiquetas: UPV/EHU
@article{nokey,
title = {Photonic counterdiabatic quantum optimization algorithm},
author = {Pranav, C. and Koushik, P. and Garcia-de-Andoin, M. and Ban, Y. and Sanz, M. and Chen, X.},
url = {https://www.nature.com/articles/s42005-024-01807-2.pdf},
doi = {doi.org/10.1038/s42005-024-01807-2},
isbn = {2399-3650},
year = {2024},
date = {2024-09-30},
journal = {Communications Physics},
number = {315},
abstract = {One of the key applications of near-term quantum computers has been the development of quantum optimization algorithms. However, these algorithms have largely been focused on qubit-based technologies. Here, we propose a hybrid quantum-classical approximate optimization algorithm for photonic quantum computing, specifically tailored for addressing continuous-variable optimization problems. Inspired by counterdiabatic protocols, our algorithm reduces the required quantum operations for optimization compared to adiabatic protocols. This reduction enables us to tackle non-convex continuous optimization within the near-term era of quantum computing. Through illustrative benchmarking, we show that our approach can outperform existing state-of-the-art hybrid adiabatic quantum algorithms in terms of convergence and implementability. Our algorithm offers a practical and accessible experimental realization, bypassing the need for high-order operations and overcoming experimental constraints. We conduct a proof-of-principle demonstration on Xanadu’s eight-mode nanophotonic quantum chip, successfully showcasing the feasibility and potential impact of the algorithm.},
keywords = {UPV/EHU},
pubstate = {published},
tppubtype = {article}
}
One of the key applications of near-term quantum computers has been the development of quantum optimization algorithms. However, these algorithms have largely been focused on qubit-based technologies. Here, we propose a hybrid quantum-classical approximate optimization algorithm for photonic quantum computing, specifically tailored for addressing continuous-variable optimization problems. Inspired by counterdiabatic protocols, our algorithm reduces the required quantum operations for optimization compared to adiabatic protocols. This reduction enables us to tackle non-convex continuous optimization within the near-term era of quantum computing. Through illustrative benchmarking, we show that our approach can outperform existing state-of-the-art hybrid adiabatic quantum algorithms in terms of convergence and implementability. Our algorithm offers a practical and accessible experimental realization, bypassing the need for high-order operations and overcoming experimental constraints. We conduct a proof-of-principle demonstration on Xanadu’s eight-mode nanophotonic quantum chip, successfully showcasing the feasibility and potential impact of the algorithm.
Reichert, M.; Zhuang, Q.; Sanz, M.
Heisenberg-Limited Quantum Lidar for Joint Range and Velocity Estimation Artículo de revista
En: Physical Review Letters (PRL), vol. 133, iss. 13, 2024, ISBN: 0031-9005.
Resumen | Enlaces | BibTeX | Etiquetas: UPV/EHU
@article{nokey,
title = {Heisenberg-Limited Quantum Lidar for Joint Range and Velocity Estimation},
author = {Reichert, M. and Zhuang, Q. and Sanz, M.
},
doi = {10.1103/PhysRevLett.133.130801},
isbn = {0031-9005},
year = {2024},
date = {2024-09-26},
journal = {Physical Review Letters (PRL)},
volume = {133},
issue = {13},
abstract = {We propose a quantum lidar protocol to jointly estimate the range and velocity of a target by illuminating it with a single beam of pulsed displaced squeezed light. In the lossless scenario, we show that the mean-squared errors of both range and velocity estimations are inversely proportional to the squared number of signal photons, simultaneously attaining the Heisenberg limit. This is achieved by engineering the multiphoton squeezed state of the temporal modes and adopting standard homodyne detection. To assess the robustness of the quantum protocol, we incorporate photon losses and detuning of the homodyne receiver. Our findings reveal a quantum advantage over the best-known classical strategy across a wide range of round-trip transmissivities. Particularly, the quantum advantage is substantial for sufficiently small losses, even when compared to the optimal—potentially unattainable—classical performance limit. The quantum advantage also extends to the practical case where quantum engineering is done on top of a strong classical coherent state with watts of power. This, together with the robustness against losses and the feasibility of the measurement with state-of-the-art technology, make the protocol highly promising for near-term implementation.},
keywords = {UPV/EHU},
pubstate = {published},
tppubtype = {article}
}
We propose a quantum lidar protocol to jointly estimate the range and velocity of a target by illuminating it with a single beam of pulsed displaced squeezed light. In the lossless scenario, we show that the mean-squared errors of both range and velocity estimations are inversely proportional to the squared number of signal photons, simultaneously attaining the Heisenberg limit. This is achieved by engineering the multiphoton squeezed state of the temporal modes and adopting standard homodyne detection. To assess the robustness of the quantum protocol, we incorporate photon losses and detuning of the homodyne receiver. Our findings reveal a quantum advantage over the best-known classical strategy across a wide range of round-trip transmissivities. Particularly, the quantum advantage is substantial for sufficiently small losses, even when compared to the optimal—potentially unattainable—classical performance limit. The quantum advantage also extends to the practical case where quantum engineering is done on top of a strong classical coherent state with watts of power. This, together with the robustness against losses and the feasibility of the measurement with state-of-the-art technology, make the protocol highly promising for near-term implementation.
Sannia, A.; Tacchino, F.; I. Giorgi Tavernelli, G. L.; Zambrini, R.
Engineered dissipation to mitigate barren plateaus Artículo de revista
En: NPJ/Quantum Information, vol. 10, iss. 81, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: csic1
@article{nokey,
title = {Engineered dissipation to mitigate barren plateaus},
author = { Sannia, A. and Tacchino, F. and Tavernelli, I. Giorgi, G.L. and Zambrini, R.},
url = {https://www.nature.com/articles/s41534-024-00875-0#citeas},
doi = {doi.org/10.1038/s41534-024-00875-0},
year = {2024},
date = {2024-09-04},
journal = {NPJ/Quantum Information},
volume = {10},
issue = {81},
abstract = {Variational quantum algorithms represent a powerful approach for solving optimization problems on noisy quantum computers, with a broad spectrum of potential applications ranging from chemistry to machine learning. However, their performances in practical implementations crucially depend on the effectiveness of quantum circuit training, which can be severely limited by phenomena such as barren plateaus. While, in general, dissipation is detrimental for quantum algorithms, and noise itself can actually induce barren plateaus, here we describe how the inclusion of properly engineered Markovian losses after each unitary quantum circuit layer allows for the trainability of quantum models. We identify the required form of the dissipation processes and establish that their optimization is efficient. We benchmark the generality of our proposal in both a synthetic and a practical quantum chemistry example, demonstrating its effectiveness and potential impact across different domains.},
keywords = {csic1},
pubstate = {published},
tppubtype = {article}
}
Variational quantum algorithms represent a powerful approach for solving optimization problems on noisy quantum computers, with a broad spectrum of potential applications ranging from chemistry to machine learning. However, their performances in practical implementations crucially depend on the effectiveness of quantum circuit training, which can be severely limited by phenomena such as barren plateaus. While, in general, dissipation is detrimental for quantum algorithms, and noise itself can actually induce barren plateaus, here we describe how the inclusion of properly engineered Markovian losses after each unitary quantum circuit layer allows for the trainability of quantum models. We identify the required form of the dissipation processes and establish that their optimization is efficient. We benchmark the generality of our proposal in both a synthetic and a practical quantum chemistry example, demonstrating its effectiveness and potential impact across different domains.
Xu, T. N.; Ding, Y.; Martín-Guerrero, J. D.; Chen, X.
Robust two-qubit gate with reinforcement learning and dropout Artículo de revista
En: Physical Review A, vol. 110, iss. 3, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: UV
@article{nokey,
title = {Robust two-qubit gate with reinforcement learning and dropout},
author = {Xu, T.N. and Ding, Y. and Martín-Guerrero, J.D. and Chen, X.},
url = {https://journals.aps.org/pra/abstract/10.1103/PhysRevA.110.032614},
doi = {doi.org/10.1103/PhysRevA.110.032614},
year = {2024},
date = {2024-08-13},
urldate = {2024-08-13},
journal = {Physical Review A},
volume = {110},
issue = {3},
abstract = {In the realm of quantum control, reinforcement learning, a prominent branch of machine learning, emerges as a competitive candidate for computer-assisted optimal experiment design. This paper investigates the extent to which guidance from human experts is necessary for effectively implementing reinforcement learning in the design of quantum control protocols. Specifically, our focus lies on engineering a robust two-qubit gate, utilizing a combination of analytical solutions as prior knowledge and techniques from computer science. Through thorough benchmarking of various models, we identify dropout—a widely used method for mitigating overfitting in machine learning—as a particularly robust approach. Our findings demonstrate the potential of integrating computer science concepts to propel the development of advanced quantum technologies.},
howpublished = {Preprint},
keywords = {UV},
pubstate = {published},
tppubtype = {article}
}
In the realm of quantum control, reinforcement learning, a prominent branch of machine learning, emerges as a competitive candidate for computer-assisted optimal experiment design. This paper investigates the extent to which guidance from human experts is necessary for effectively implementing reinforcement learning in the design of quantum control protocols. Specifically, our focus lies on engineering a robust two-qubit gate, utilizing a combination of analytical solutions as prior knowledge and techniques from computer science. Through thorough benchmarking of various models, we identify dropout—a widely used method for mitigating overfitting in machine learning—as a particularly robust approach. Our findings demonstrate the potential of integrating computer science concepts to propel the development of advanced quantum technologies.
Nokkala, J.; Giorgi, G. L.; Zambrini, R.
Retrieving past quantum features with deep hybrid classical-quantum reservoir computing Artículo de revista
En: Machine Learning: Science and Technology, vol. 5, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: csic1
@article{nokey,
title = {Retrieving past quantum features with deep hybrid classical-quantum reservoir computing},
author = {Nokkala, J. and Giorgi, G.L. and Zambrini, R. },
url = {https://iopscience.iop.org/article/10.1088/2632-2153/ad5f12},
doi = {10.1088/2632-2153/ad5f12},
year = {2024},
date = {2024-07-19},
journal = {Machine Learning: Science and Technology},
volume = {5},
abstract = {Machine learning techniques have achieved impressive results in recent years and the possibility of harnessing the power of quantum physics opens new promising avenues to speed up classical learning methods. Rather than viewing classical and quantum approaches as exclusive alternatives, their integration into hybrid designs has gathered increasing interest, as seen in variational quantum algorithms, quantum circuit learning, and kernel methods. Here we introduce deep hybrid classical-quantum reservoir computing for temporal processing of quantum states where information about, for instance, the entanglement or the purity of past input states can be extracted via a single-step measurement. We find that the hybrid setup cascading two reservoirs not only inherits the strengths of both of its constituents but is even more than just the sum of its parts, outperforming comparable non-hybrid alternatives. The quantum layer is within reach of state-of-the-art multimode quantum optical platforms while the classical layer can be implemented in silico.},
keywords = {csic1},
pubstate = {published},
tppubtype = {article}
}
Machine learning techniques have achieved impressive results in recent years and the possibility of harnessing the power of quantum physics opens new promising avenues to speed up classical learning methods. Rather than viewing classical and quantum approaches as exclusive alternatives, their integration into hybrid designs has gathered increasing interest, as seen in variational quantum algorithms, quantum circuit learning, and kernel methods. Here we introduce deep hybrid classical-quantum reservoir computing for temporal processing of quantum states where information about, for instance, the entanglement or the purity of past input states can be extracted via a single-step measurement. We find that the hybrid setup cascading two reservoirs not only inherits the strengths of both of its constituents but is even more than just the sum of its parts, outperforming comparable non-hybrid alternatives. The quantum layer is within reach of state-of-the-art multimode quantum optical platforms while the classical layer can be implemented in silico.
Dastbasteh, R.; Etxezarreta Martinez, J.; A. deMarti iOlius Nemec, A. Crespo Bofill.
Infinite class of quantum codes derived from duadic constacyclic codes Sin publicar
Preprint, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: tecnun
@unpublished{nokey,
title = {Infinite class of quantum codes derived from duadic constacyclic codes},
author = {Dastbasteh, R. and Etxezarreta Martinez, J. and Nemec, A. deMarti iOlius, A. Crespo Bofill.},
url = {https://arxiv.org/pdf/2312.06504},
doi = {doi.org/10.48550/arXiv.2312.06504},
year = {2024},
date = {2024-05-27},
abstract = {We present a family of quantum stabilizer codes using the structure of duadic constacyclic codes over F4. Within this family, quantum codes can possess varying dimensions, and their minimum distances are lower bounded by a square root bound. For each fixed dimension, this allows us to construct an infinite sequence of binary quantum codes with a growing minimum distance. Additionally, we prove that this family of quantum codes includes an infinite subclass of degenerate codes. We also introduce a technique for extending splittings of duadic constacyclic codes, providing new insights into the minimum distance and minimum odd-like weight of specific duadic constacyclic codes. Finally, we provide numerical examples of some quantum codes with short lengths within this family.},
howpublished = {Preprint},
keywords = {tecnun},
pubstate = {published},
tppubtype = {unpublished}
}
We present a family of quantum stabilizer codes using the structure of duadic constacyclic codes over F4. Within this family, quantum codes can possess varying dimensions, and their minimum distances are lower bounded by a square root bound. For each fixed dimension, this allows us to construct an infinite sequence of binary quantum codes with a growing minimum distance. Additionally, we prove that this family of quantum codes includes an infinite subclass of degenerate codes. We also introduce a technique for extending splittings of duadic constacyclic codes, providing new insights into the minimum distance and minimum odd-like weight of specific duadic constacyclic codes. Finally, we provide numerical examples of some quantum codes with short lengths within this family.
Gonzalez-Raya, T.; Pirandola, S.; Sanz, M.
Satellite-based entanglement distribution and quantum teleportation with continuous variables Artículo de revista
En: Communications Physics, vol. 7, no 126, 2024, ISBN: 2399-3650.
Resumen | Enlaces | BibTeX | Etiquetas: UPV/EHU
@article{nokey,
title = {Satellite-based entanglement distribution and quantum teleportation with continuous variables},
author = {Gonzalez-Raya, T. and Pirandola, S. and Sanz, M. },
url = {https://www.nature.com/articles/s42005-024-01612-x.pdf},
doi = {doi.org/10.1038/s42005-024-01612-x},
isbn = {2399-3650},
year = {2024},
date = {2024-04-11},
journal = {Communications Physics},
volume = {7},
number = {126},
abstract = {Advances in satellite quantum communications aim at reshaping the global telecommunication network by increasing the security of the transferred information. Here, we study the effects of atmospheric turbulence in continuous-variable entanglement distribution and quantum teleportation in the optical regime between a ground station and a satellite. More specifically, we study the degradation of entanglement due to various error sources in the distribution, namely, diffraction, atmospheric attenuation, turbulence, and detector inefficiency, in both downlink and uplink scenarios. As the fidelity of a quantum teleportation protocol using these distributed entangled resources is not sufficient, we include an intermediate station for either state generation, or beam refocusing, in order to reduce the effects of atmospheric turbulence and diffraction, respectively. The results show the feasibility of free-space entanglement distribution and quantum teleportation in downlink paths up to the LEO region, but also in uplink paths with the help of the intermediate station. Finally, we complete the study with microwave-optical comparison in bad weather situations, and with the study of horizontal paths in ground-to-ground and inter-satellite quantum communication.},
keywords = {UPV/EHU},
pubstate = {published},
tppubtype = {article}
}
Advances in satellite quantum communications aim at reshaping the global telecommunication network by increasing the security of the transferred information. Here, we study the effects of atmospheric turbulence in continuous-variable entanglement distribution and quantum teleportation in the optical regime between a ground station and a satellite. More specifically, we study the degradation of entanglement due to various error sources in the distribution, namely, diffraction, atmospheric attenuation, turbulence, and detector inefficiency, in both downlink and uplink scenarios. As the fidelity of a quantum teleportation protocol using these distributed entangled resources is not sufficient, we include an intermediate station for either state generation, or beam refocusing, in order to reduce the effects of atmospheric turbulence and diffraction, respectively. The results show the feasibility of free-space entanglement distribution and quantum teleportation in downlink paths up to the LEO region, but also in uplink paths with the help of the intermediate station. Finally, we complete the study with microwave-optical comparison in bad weather situations, and with the study of horizontal paths in ground-to-ground and inter-satellite quantum communication.
Martínez-Peña, R.; Soriano, M. C.; Zambrini, R.
Quantum fidelity kernel with a trapped-ion simulation platform Artículo de revista
En: Physical Review A, vol. 109, iss. 4, 2024, ISSN: 2469-9926.
Resumen | Enlaces | BibTeX | Etiquetas: csic1
@article{nokey,
title = {Quantum fidelity kernel with a trapped-ion simulation platform},
author = {Martínez-Peña, R. and Soriano, M.C. and Zambrini, R.
},
url = {https://journals.aps.org/pra/abstract/10.1103/PhysRevA.109.042612},
doi = {10.1103/PhysRevA.109.042612},
issn = {2469-9926},
year = {2024},
date = {2024-04-08},
urldate = {2024-04-08},
journal = {Physical Review A},
volume = {109},
issue = {4},
abstract = {Quantum kernel methods leverage a kernel function computed by embedding input information into the Hilbert space of a quantum system. However, large Hilbert spaces can hinder generalization capability, and the scalability of quantum kernels becomes an issue. To overcome these challenges, various strategies under the concept of inductive bias have been proposed. Bandwidth optimization is a promising approach that can be implemented using quantum simulation platforms. We propose trapped-ion simulation platforms as a means to compute quantum kernels, filling a gap in the previous literature and demonstrating their effectiveness for binary classification tasks. We compare the performance of the proposed method with an optimized classical kernel and evaluate the robustness of the quantum kernel against noise. The results show that ion trap platforms are well-suited for quantum kernel computation and can achieve high accuracy with only a few qubits.},
keywords = {csic1},
pubstate = {published},
tppubtype = {article}
}
Quantum kernel methods leverage a kernel function computed by embedding input information into the Hilbert space of a quantum system. However, large Hilbert spaces can hinder generalization capability, and the scalability of quantum kernels becomes an issue. To overcome these challenges, various strategies under the concept of inductive bias have been proposed. Bandwidth optimization is a promising approach that can be implemented using quantum simulation platforms. We propose trapped-ion simulation platforms as a means to compute quantum kernels, filling a gap in the previous literature and demonstrating their effectiveness for binary classification tasks. We compare the performance of the proposed method with an optimized classical kernel and evaluate the robustness of the quantum kernel against noise. The results show that ion trap platforms are well-suited for quantum kernel computation and can achieve high accuracy with only a few qubits.
Gonzalez-Conde, J.; Watts, T. W.; Rodriguez-Grasa, P.; Sanz, M.
Efficient quantum amplitude encoding of polynomial functions Artículo de revista
En: Quantum, vol. 8, pp. 1297, 2024, ISBN: 2521-327X.
Resumen | Enlaces | BibTeX | Etiquetas: UPV/EHU
@article{nokey,
title = {Efficient quantum amplitude encoding of polynomial functions},
author = {Gonzalez-Conde, J. and Watts, T.W. and Rodriguez-Grasa, P. and Sanz, M.},
url = {https://quantum-journal.org/papers/q-2024-03-21-1297/},
doi = {doi.org/10.22331/q-2024-03-21-1297},
isbn = {2521-327X},
year = {2024},
date = {2024-03-21},
urldate = {2024-03-21},
journal = {Quantum},
volume = {8},
pages = {1297},
abstract = {Loading functions into quantum computers represents an essential step in several quantum algorithms, such as quantum partial differential equation solvers. Therefore, the inefficiency of this process leads to a major bottleneck for the application of these algorithms. Here, we present and compare two efficient methods for the amplitude encoding of real polynomial functions on n qubits. This case holds special relevance, as any continuous function on a closed interval can be uniformly approximated with arbitrary precision by a polynomial function. The first approach relies on the matrix product state representation. We study and benchmark the approximations of the target state when the bond dimension is assumed to be small. The second algorithm combines two subroutines. Initially we encode the linear function into the quantum registers with a shallow sequence of multi-controlled gates that loads the linear function's Hadamard-Walsh series, exploring how truncating the Hadamard-Walsh series of the linear function affects the final fidelity. Applying the inverse discrete Hadamard-Walsh transform transforms the series coefficients into an amplitude encoding of the linear function. Then, we use this construction as a building block to achieve a block encoding of the amplitudes corresponding to the linear function on k0 qubits and apply the quantum singular value transformation that implements a polynomial transformation to the block encoding of the amplitudes. This unitary together with the Amplitude Amplification algorithm will enable us to prepare the quantum state that encodes the polynomial function on k0 qubits. Finally we pad n−k0 qubits to generate an approximated encoding of the polynomial on n qubits, analyzing the error depending on k0. In this regard, our methodology proposes a method to improve the state-of-the-art complexity by introducing controllable errors.},
keywords = {UPV/EHU},
pubstate = {published},
tppubtype = {article}
}
Loading functions into quantum computers represents an essential step in several quantum algorithms, such as quantum partial differential equation solvers. Therefore, the inefficiency of this process leads to a major bottleneck for the application of these algorithms. Here, we present and compare two efficient methods for the amplitude encoding of real polynomial functions on n qubits. This case holds special relevance, as any continuous function on a closed interval can be uniformly approximated with arbitrary precision by a polynomial function. The first approach relies on the matrix product state representation. We study and benchmark the approximations of the target state when the bond dimension is assumed to be small. The second algorithm combines two subroutines. Initially we encode the linear function into the quantum registers with a shallow sequence of multi-controlled gates that loads the linear function's Hadamard-Walsh series, exploring how truncating the Hadamard-Walsh series of the linear function affects the final fidelity. Applying the inverse discrete Hadamard-Walsh transform transforms the series coefficients into an amplitude encoding of the linear function. Then, we use this construction as a building block to achieve a block encoding of the amplitudes corresponding to the linear function on k0 qubits and apply the quantum singular value transformation that implements a polynomial transformation to the block encoding of the amplitudes. This unitary together with the Amplitude Amplification algorithm will enable us to prepare the quantum state that encodes the polynomial function on k0 qubits. Finally we pad n−k0 qubits to generate an approximated encoding of the polynomial on n qubits, analyzing the error depending on k0. In this regard, our methodology proposes a method to improve the state-of-the-art complexity by introducing controllable errors.
Garcia-de-Andoin, M.; Saiz, A.; Perez-Fernandez, P.; Lamata, L.; Oregi, I.; Sanz, M.
Digital-analog quantum computation with arbitrary two-body Hamiltonians Artículo de revista
En: Physical Review Research, vol. 6, iss. 1, 2024, ISBN: 2643-1564.
Resumen | Enlaces | BibTeX | Etiquetas: UPV/EHU
@article{nokey,
title = {Digital-analog quantum computation with arbitrary two-body Hamiltonians},
author = {Garcia-de-Andoin, M. and Saiz, A. and Perez-Fernandez, P. and Lamata, L. and Oregi, I. and Sanz, M. },
url = {https://journals.aps.org/prresearch/pdf/10.1103/PhysRevResearch.6.013280},
doi = {doi.org/10.1103/PhysRevResearch.6.013280},
isbn = {2643-1564},
year = {2024},
date = {2024-03-14},
urldate = {2024-03-14},
journal = {Physical Review Research},
volume = {6},
issue = {1},
abstract = {Digital-analog quantum computing is a computational paradigm which employs an analog Hamiltonian resource together with single-qubit gates to reach universality. Here, we design a new scheme which employs an arbitrary two-body source Hamiltonian, extending the experimental applicability of this computational paradigm to most quantum platforms. We show that the simulation of an arbitrary two-body target Hamiltonian of 𝑛 qubits requires 𝒪(𝑛2) analog blocks with guaranteed positive times, providing a polynomial advantage compared to the previous scheme. Additionally, we propose a classical strategy which combines a Bayesian optimization with a gradient descent method, improving the performance by ∼55% for small systems measured in the Frobenius norm.},
keywords = {UPV/EHU},
pubstate = {published},
tppubtype = {article}
}
Digital-analog quantum computing is a computational paradigm which employs an analog Hamiltonian resource together with single-qubit gates to reach universality. Here, we design a new scheme which employs an arbitrary two-body source Hamiltonian, extending the experimental applicability of this computational paradigm to most quantum platforms. We show that the simulation of an arbitrary two-body target Hamiltonian of 𝑛 qubits requires 𝒪(𝑛2) analog blocks with guaranteed positive times, providing a polynomial advantage compared to the previous scheme. Additionally, we propose a classical strategy which combines a Bayesian optimization with a gradient descent method, improving the performance by ∼55% for small systems measured in the Frobenius norm.
Labay-Mora, A.; Zambrini, R.; Giorgi, G. L.
Quantum memories for squeezed and coherent superpositions in a driven-dissipative nonlinear oscillator Artículo de revista
En: Physical Review A, vol. 109, iss. 3, 2024, ISSN: 2469-9926.
Resumen | Enlaces | BibTeX | Etiquetas: csic1
@article{nokey,
title = {Quantum memories for squeezed and coherent superpositions in a driven-dissipative nonlinear oscillator},
author = {Labay-Mora, A. and Zambrini, R. and Giorgi, G.L.
},
url = {https://journals.aps.org/pra/abstract/10.1103/PhysRevA.109.032407},
doi = {doi.org/10.1103/PhysRevA.109.032407},
issn = {2469-9926},
year = {2024},
date = {2024-03-08},
journal = {Physical Review A},
volume = {109},
issue = {3},
abstract = {Quantum oscillators with nonlinear driving and dissipative terms have gained significant attention due to their ability to stabilize cat states for universal quantum computation. Recently, superconducting circuits have been employed to realize such long-lived qubits stored in coherent states. We present a generalization of these oscillators, which are not limited to coherent states. The key ingredient lies in the presence of different nonlinearities in driving and dissipation, beyond the quadratic one. Through an extensive analysis of the asymptotic dynamical features for different nonlinearities, we identify the conditions for the storage and retrieval of quantum states, such as squeezed states, in both coherent and incoherent superpositions. We explore their applications in quantum computing, where squeezing prolongs the lifetime of memory storage for qubits encoded in the superposition of two symmetric squeezed states, and in quantum associative memory, which has so far been limited to the storage of classical patterns.},
keywords = {csic1},
pubstate = {published},
tppubtype = {article}
}
Quantum oscillators with nonlinear driving and dissipative terms have gained significant attention due to their ability to stabilize cat states for universal quantum computation. Recently, superconducting circuits have been employed to realize such long-lived qubits stored in coherent states. We present a generalization of these oscillators, which are not limited to coherent states. The key ingredient lies in the presence of different nonlinearities in driving and dissipation, beyond the quadratic one. Through an extensive analysis of the asymptotic dynamical features for different nonlinearities, we identify the conditions for the storage and retrieval of quantum states, such as squeezed states, in both coherent and incoherent superpositions. We explore their applications in quantum computing, where squeezing prolongs the lifetime of memory storage for qubits encoded in the superposition of two symmetric squeezed states, and in quantum associative memory, which has so far been limited to the storage of classical patterns.
Lugilde, G.; Combarro, E. F.; Rúa, I. F.
Functional quantum abstract detecting systems Artículo de revista
En: Quantum Information Processing, vol. 23, no 82, 2024, ISBN: 1570-0755.
Resumen | Enlaces | BibTeX | Etiquetas: UNIOVI
@article{nokey,
title = {Functional quantum abstract detecting systems},
author = {Lugilde, G. and Combarro, E.F. and Rúa, I.F.},
url = {https://link.springer.com/content/pdf/10.1007/s11128-024-04273-5.pdf},
doi = {doi.org/10.1007/s11128-024-04273-5},
isbn = {1570-0755},
year = {2024},
date = {2024-02-28},
journal = {Quantum Information Processing},
volume = {23},
number = {82},
abstract = {Quantum abstract detecting systems (QADS) provide a common framework to address detection problems in quantum computers. A particular QADS family, that of combinatorial QADS, has been proved to be useful for decision problems on eigenvalues or phase estimation methods. In this paper, we consider functional QADS, which not only have interesting theoretical properties (intrinsic detection ability, relation to the QFT), but also yield improved decision and phase estimation methods, as compared to combinatorial QADS. A first insight into the comparison with other phase estimation methods also shows promising results.},
keywords = {UNIOVI},
pubstate = {published},
tppubtype = {article}
}
Quantum abstract detecting systems (QADS) provide a common framework to address detection problems in quantum computers. A particular QADS family, that of combinatorial QADS, has been proved to be useful for decision problems on eigenvalues or phase estimation methods. In this paper, we consider functional QADS, which not only have interesting theoretical properties (intrinsic detection ability, relation to the QFT), but also yield improved decision and phase estimation methods, as compared to combinatorial QADS. A first insight into the comparison with other phase estimation methods also shows promising results.
Schenk, M.; Combarro, E. F.; Grossi, M.; Kain, V.; Shing-Brice, K.; Popa, M. M.; Vallecorsa, S.
Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines Artículo de revista
En: Quantum Science and Technology, vol. 9, 2024, ISBN: 2058-9565.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{nokey,
title = {Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines},
author = {Schenk,M. and Combarro, E.F. and Grossi, M. and Kain, V. and Shing-Brice, K. and Popa, M.M. and Vallecorsa, S.},
url = {https://iopscience.iop.org/article/10.1088/2058-9565/ad261b},
doi = {10.1088/2058-9565/ad261b},
isbn = {2058-9565},
year = {2024},
date = {2024-02-21},
urldate = {2024-02-21},
journal = {Quantum Science and Technology},
volume = {9},
abstract = {Free energy-based reinforcement learning (FERL) with clamped quantum Boltzmann machines (QBM) was shown to significantly improve the learning efficiency compared to classical Q-learning with the restriction, however, to discrete state-action space environments. In this paper, the FERL approach is extended to multi-dimensional continuous state-action space environments to open the doors for a broader range of real-world applications. First, free energy-based Q-learning is studied for discrete action spaces, but continuous state spaces and the impact of experience replay on sample efficiency is assessed. In a second step, a hybrid actor-critic (A-C) scheme for continuous state-action spaces is developed based on the deep deterministic policy gradient algorithm combining a classical actor network with a QBM-based critic. The results obtained with quantum annealing (QA), both simulated and with D-Wave QA hardware, are discussed, and the performance is compared to classical reinforcement learning methods. The environments used throughout represent existing particle accelerator beam lines at the European Organisation for Nuclear Research. Among others, the hybrid A-C agent is evaluated on the actual electron beam line of the Advanced Wakefield Experiment (AWAKE).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Free energy-based reinforcement learning (FERL) with clamped quantum Boltzmann machines (QBM) was shown to significantly improve the learning efficiency compared to classical Q-learning with the restriction, however, to discrete state-action space environments. In this paper, the FERL approach is extended to multi-dimensional continuous state-action space environments to open the doors for a broader range of real-world applications. First, free energy-based Q-learning is studied for discrete action spaces, but continuous state spaces and the impact of experience replay on sample efficiency is assessed. In a second step, a hybrid actor-critic (A-C) scheme for continuous state-action spaces is developed based on the deep deterministic policy gradient algorithm combining a classical actor network with a QBM-based critic. The results obtained with quantum annealing (QA), both simulated and with D-Wave QA hardware, are discussed, and the performance is compared to classical reinforcement learning methods. The environments used throughout represent existing particle accelerator beam lines at the European Organisation for Nuclear Research. Among others, the hybrid A-C agent is evaluated on the actual electron beam line of the Advanced Wakefield Experiment (AWAKE).
Sannia, A.; Martínez-Peña, R.; Soriano, M. C.; G.L Giorgi,; Zambrini, R.
Dissipation as a resource for Quantum Reservoir Computing Artículo de revista
En: Quantum, vol. 8, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: uib
@article{nokey,
title = {Dissipation as a resource for Quantum Reservoir Computing},
author = {Sannia, A. and Martínez-Peña, R. and Soriano, M.C. and Giorgi, G.L, and Zambrini, R. },
url = {https://quantum-journal.org/papers/q-2024-03-20-1291/},
doi = {doi.org/10.22331/q-2024-03-20-1291},
year = {2024},
date = {2024-02-20},
journal = {Quantum},
volume = {8},
abstract = {Dissipation induced by interactions with an external environment typically hinders the performance of quantum computation, but in some cases can be turned out as a useful resource. We show the potential enhancement induced by dissipation in the field of quantum reservoir computing introducing tunable local losses in spin network models. Our approach based on continuous dissipation is able not only to reproduce the dynamics of previous proposals of quantum reservoir computing, based on discontinuous erasing maps but also to enhance their performance. Control of the damping rates is shown to boost popular machine learning temporal tasks as the capability to linearly and non-linearly process the input history and to forecast chaotic series. Finally, we formally prove that, under non-restrictive conditions, our dissipative models form a universal class for reservoir computing. It means that considering our approach, it is possible to approximate any fading memory map with arbitrary precision.},
howpublished = {Preprint},
keywords = {uib},
pubstate = {published},
tppubtype = {article}
}
Dissipation induced by interactions with an external environment typically hinders the performance of quantum computation, but in some cases can be turned out as a useful resource. We show the potential enhancement induced by dissipation in the field of quantum reservoir computing introducing tunable local losses in spin network models. Our approach based on continuous dissipation is able not only to reproduce the dynamics of previous proposals of quantum reservoir computing, based on discontinuous erasing maps but also to enhance their performance. Control of the damping rates is shown to boost popular machine learning temporal tasks as the capability to linearly and non-linearly process the input history and to forecast chaotic series. Finally, we formally prove that, under non-restrictive conditions, our dissipative models form a universal class for reservoir computing. It means that considering our approach, it is possible to approximate any fading memory map with arbitrary precision.
Orts, F.; Ortega, G.; Combarro, E. F.; Rúa, I. F.; Garzón, E. M.
Quantum circuits for computing Hamming distance requiring fewer T gates Artículo de revista
En: The Journal of Supercomputing , vol. 80, pp. 12527–12542, 2024, ISBN: 0920-8542.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{nokey,
title = {Quantum circuits for computing Hamming distance requiring fewer T gates},
author = {Orts, F. and Ortega, G. and Combarro, E.F. and Rúa, I.F. and Garzón, E.M. },
url = {https://link.springer.com/article/10.1007/s11227-024-05916-1},
doi = {doi.org/10.1007/s11227-024-05916-1},
isbn = {0920-8542},
year = {2024},
date = {2024-02-16},
journal = {The Journal of Supercomputing },
volume = {80},
pages = {12527–12542},
abstract = {The so-called Hamming distance measures the difference between two binary strings A and B. In simplified form, it measures the number of changes in A to get B. This type of distance is very useful in classical computing in applications such as error correction. It is also advantageous in quantum computing, being for example widely used in quantum machine learning. Since current quantum computers have limited resources, this type of distance is particularly attractive because it can be computed using fewer qubits and operations than other distances such as Euclidean or Manhattan distances. In this paper, two circuits for calculating Hamming distances using exclusively Clifford+T gates are presented. The aim of both circuits is to reduce the quantum cost and number of T gates needed to compute the Hamming distance. The T gate is more expensive than the other gates, so this reduction will have a significant impact on the total cost of the circuits. Furthermore, the proposed circuits are implemented using only Clifford+T gates. The circuits implemented exclusively with this group of gates are compatible with proven error detection and correction codes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The so-called Hamming distance measures the difference between two binary strings A and B. In simplified form, it measures the number of changes in A to get B. This type of distance is very useful in classical computing in applications such as error correction. It is also advantageous in quantum computing, being for example widely used in quantum machine learning. Since current quantum computers have limited resources, this type of distance is particularly attractive because it can be computed using fewer qubits and operations than other distances such as Euclidean or Manhattan distances. In this paper, two circuits for calculating Hamming distances using exclusively Clifford+T gates are presented. The aim of both circuits is to reduce the quantum cost and number of T gates needed to compute the Hamming distance. The T gate is more expensive than the other gates, so this reduction will have a significant impact on the total cost of the circuits. Furthermore, the proposed circuits are implemented using only Clifford+T gates. The circuits implemented exclusively with this group of gates are compatible with proven error detection and correction codes.
Combarro, E. F.; Rúa, I. F.; Ortega, O. G.; Puertas, A. M.; Garzón, E. M.
Quantum algorithms to compute the neighbour list of N-body simulations Artículo de revista
En: Quantum Information Processing, vol. 23, no 61, 2024, ISBN: 1570-0755.
Resumen | Enlaces | BibTeX | Etiquetas: UNIOVI
@article{nokey,
title = {Quantum algorithms to compute the neighbour list of N-body simulations},
author = {Combarro, E.F. and Rúa, I.F. and Ortega, O.G. and Puertas, A.M. and Garzón, E.M. },
url = {https://link.springer.com/article/10.1007/s11128-023-04245-1},
doi = {doi.org/10.1007/s11128-023-04245-1},
isbn = {1570-0755},
year = {2024},
date = {2024-02-13},
journal = {Quantum Information Processing},
volume = {23},
number = {61},
abstract = {One of the strategies to reduce the complexity of N-body simulations is the computation of the neighbour list. However, this list needs to be updated from time to time, with a high computational cost. This paper focuses on the use of quantum computing to accelerate such a computation. Our proposal is based on a well-known oracular quantum algorithm (Grover). We introduce an efficient quantum circuit to build the oracle that marks pairs of closed bodies, and we provide three novel algorithms to calculate the neighbour list under several hypotheses which take into account a-priori information of the system. We also describe a decision methodology for the actual use of the proposed quantum algorithms. The performance of the algorithms is tested with a statistical simulation of the oracle, where a fixed number of pairs of bodies are set as neighbours. A statistical analysis of the number of oracle queries is carried out. The results obtained with our simulations indicate that when the density of bodies is low, our algorithms clearly outperform the best classical algorithm in terms of oracle queries.},
keywords = {UNIOVI},
pubstate = {published},
tppubtype = {article}
}
One of the strategies to reduce the complexity of N-body simulations is the computation of the neighbour list. However, this list needs to be updated from time to time, with a high computational cost. This paper focuses on the use of quantum computing to accelerate such a computation. Our proposal is based on a well-known oracular quantum algorithm (Grover). We introduce an efficient quantum circuit to build the oracle that marks pairs of closed bodies, and we provide three novel algorithms to calculate the neighbour list under several hypotheses which take into account a-priori information of the system. We also describe a decision methodology for the actual use of the proposed quantum algorithms. The performance of the algorithms is tested with a statistical simulation of the oracle, where a fixed number of pairs of bodies are set as neighbours. A statistical analysis of the number of oracle queries is carried out. The results obtained with our simulations indicate that when the density of bodies is low, our algorithms clearly outperform the best classical algorithm in terms of oracle queries.
García-Beni, J.; Giorgi, G. L.; Soriano, M. C.; Zambrini, R.
Squeezing as a resource for time series processing in quantum reservoir computing Artículo de revista
En: Optics Express, vol. 32, iss. 4, pp. 6733-6747, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: csic1
@article{nokey,
title = {Squeezing as a resource for time series processing in quantum reservoir computing},
author = {García-Beni, J. and Giorgi, G.L. and Soriano, M.C. and Zambrini, R.},
url = {https://opg.optica.org/oe/fulltext.cfm?uri=oe-32-4-6733&id=546462},
doi = {doi.org/10.1364/OE.507684},
year = {2024},
date = {2024-02-09},
journal = {Optics Express},
volume = {32},
issue = {4},
pages = { 6733-6747},
abstract = {Squeezing is known to be a quantum resource in many applications in metrology, cryptography, and computing, being related to entanglement in multimode settings. In this work, we address the effects of squeezing in neuromorphic machine learning for time-series processing. In particular, we consider a loop-based photonic architecture for reservoir computing and address the effect of squeezing in the reservoir, considering a Hamiltonian with both active and passive coupling terms. Interestingly, squeezing can be either detrimental or beneficial for quantum reservoir computing when moving from ideal to realistic models, accounting for experimental noise. We demonstrate that multimode squeezing enhances its accessible memory, which improves the performance in several benchmark temporal tasks. The origin of this improvement is traced back to the robustness of the reservoir to readout noise, which is increased with squeezing.},
keywords = {csic1},
pubstate = {published},
tppubtype = {article}
}
Squeezing is known to be a quantum resource in many applications in metrology, cryptography, and computing, being related to entanglement in multimode settings. In this work, we address the effects of squeezing in neuromorphic machine learning for time-series processing. In particular, we consider a loop-based photonic architecture for reservoir computing and address the effect of squeezing in the reservoir, considering a Hamiltonian with both active and passive coupling terms. Interestingly, squeezing can be either detrimental or beneficial for quantum reservoir computing when moving from ideal to realistic models, accounting for experimental noise. We demonstrate that multimode squeezing enhances its accessible memory, which improves the performance in several benchmark temporal tasks. The origin of this improvement is traced back to the robustness of the reservoir to readout noise, which is increased with squeezing.
García-Beni, J.; Giorgi, G. L.; Soriano, M. C.; Zambrini, R.
Squeezing as a resource for time series processing in quantum reservoir computing Artículo de revista
En: Optics Express, vol. 32, iss. 4, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: uib
@article{nokey,
title = {Squeezing as a resource for time series processing in quantum reservoir computing},
author = {García-Beni, J. and Giorgi, G.L. and Soriano, M.C. and Zambrini, R.},
url = {https://opg.optica.org/oe/fulltext.cfm?uri=oe-32-4-6733&id=546462},
doi = {doi.org/10.1364/OE.507684},
year = {2024},
date = {2024-02-09},
journal = {Optics Express},
volume = {32},
issue = {4},
abstract = {Squeezing is known to be a quantum resource in many applications in metrology, cryptography, and computing, being related to entanglement in multimode settings. In this work, we address the effects of squeezing in neuromorphic machine learning for time-series processing. In particular, we consider a loop-based photonic architecture for reservoir computing and address the effect of squeezing in the reservoir, considering a Hamiltonian with both active and passive coupling terms. Interestingly, squeezing can be either detrimental or beneficial for quantum reservoir computing when moving from ideal to realistic models, accounting for experimental noise. We demonstrate that multimode squeezing enhances its accessible memory, which improves the performance in several benchmark temporal tasks. The origin of this improvement is traced back to the robustness of the reservoir to readout noise, which is increased with squeezing.},
keywords = {uib},
pubstate = {published},
tppubtype = {article}
}
Squeezing is known to be a quantum resource in many applications in metrology, cryptography, and computing, being related to entanglement in multimode settings. In this work, we address the effects of squeezing in neuromorphic machine learning for time-series processing. In particular, we consider a loop-based photonic architecture for reservoir computing and address the effect of squeezing in the reservoir, considering a Hamiltonian with both active and passive coupling terms. Interestingly, squeezing can be either detrimental or beneficial for quantum reservoir computing when moving from ideal to realistic models, accounting for experimental noise. We demonstrate that multimode squeezing enhances its accessible memory, which improves the performance in several benchmark temporal tasks. The origin of this improvement is traced back to the robustness of the reservoir to readout noise, which is increased with squeezing.
García-Beni, J.; Giorgi, G. L.; Soriano, M. C.; Zambrini, R.
Squeezing as a resource for time series processing in quantum reservoir computing Artículo de revista
En: Optics Express, vol. 32, iss. 4, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: uib
@article{nokey,
title = {Squeezing as a resource for time series processing in quantum reservoir computing},
author = {García-Beni, J. and Giorgi, G.L. and Soriano, M.C. and Zambrini, R. },
url = {https://opg.optica.org/oe/fulltext.cfm?uri=oe-32-4-6733&id=546462},
doi = {doi.org/10.1364/OE.507684},
year = {2024},
date = {2024-02-09},
urldate = {2024-02-09},
journal = {Optics Express},
volume = {32},
issue = {4},
abstract = {Squeezing is known to be a quantum resource in many applications in metrology, cryptography, and computing, being related to entanglement in multimode settings. In this work, we address the effects of squeezing in neuromorphic machine learning for time-series processing. In particular, we consider a loop-based photonic architecture for reservoir computing and address the effect of squeezing in the reservoir, considering a Hamiltonian with both active and passive coupling terms. Interestingly, squeezing can be either detrimental or beneficial for quantum reservoir computing when moving from ideal to realistic models, accounting for experimental noise. We demonstrate that multimode squeezing enhances its accessible memory, which improves the performance in several benchmark temporal tasks. The origin of this improvement is traced back to the robustness of the reservoir to readout noise, which is increased with squeezing.},
keywords = {uib},
pubstate = {published},
tppubtype = {article}
}
Squeezing is known to be a quantum resource in many applications in metrology, cryptography, and computing, being related to entanglement in multimode settings. In this work, we address the effects of squeezing in neuromorphic machine learning for time-series processing. In particular, we consider a loop-based photonic architecture for reservoir computing and address the effect of squeezing in the reservoir, considering a Hamiltonian with both active and passive coupling terms. Interestingly, squeezing can be either detrimental or beneficial for quantum reservoir computing when moving from ideal to realistic models, accounting for experimental noise. We demonstrate that multimode squeezing enhances its accessible memory, which improves the performance in several benchmark temporal tasks. The origin of this improvement is traced back to the robustness of the reservoir to readout noise, which is increased with squeezing.
Xu, R.; Tang, J.; Chandarana, P.; Paul, K.; Xu, X.; Yung, M.; Chen, X.
Benchmarking hybrid digitized-counterdiabatic quantum optimization Artículo de revista
En: Physical Review Research, vol. 6, iss. 1, 2024, ISBN: 2643-1564.
Resumen | Enlaces | BibTeX | Etiquetas: UPV/EHU
@article{nokey,
title = {Benchmarking hybrid digitized-counterdiabatic quantum optimization},
author = {Xu, R. and Tang, J. and Chandarana, P. and Paul, K. and Xu, X. and Yung, M. and Chen, X. },
url = {https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.013147},
doi = {doi.org/10.1103/PhysRevResearch.6.013147},
isbn = {2643-1564},
year = {2024},
date = {2024-02-07},
journal = {Physical Review Research},
volume = {6},
issue = {1},
abstract = {Hybrid digitized-counterdiabatic quantum computing (DCQC) is a promising approach for leveraging the capabilities of nearterm quantum computers, utilizing parameterized quantum circuits designed with counterdiabatic protocols. However, the classical aspect of this approach has received limited attention. In this study, we systematically analyze the convergence behavior and solution quality of various classical optimizers when used in conjunction with the digitized-counterdiabatic approach. We demonstrate the effectiveness of this hybrid algorithm by comparing its performance to the traditional QAOA on systems containing up to 28 qubits. Furthermore, we employ principal component analysis to investigate the cost landscape and explore the crucial influence of parametrization on the performance of the counterdiabatic ansatz. Our findings indicate that fewer iterations are required when local cost landscape minima are present, and the SPSA-based BFGS optimizer emerges as a standout choice for the hybrid DCQC paradigm.},
keywords = {UPV/EHU},
pubstate = {published},
tppubtype = {article}
}
Hybrid digitized-counterdiabatic quantum computing (DCQC) is a promising approach for leveraging the capabilities of nearterm quantum computers, utilizing parameterized quantum circuits designed with counterdiabatic protocols. However, the classical aspect of this approach has received limited attention. In this study, we systematically analyze the convergence behavior and solution quality of various classical optimizers when used in conjunction with the digitized-counterdiabatic approach. We demonstrate the effectiveness of this hybrid algorithm by comparing its performance to the traditional QAOA on systems containing up to 28 qubits. Furthermore, we employ principal component analysis to investigate the cost landscape and explore the crucial influence of parametrization on the performance of the counterdiabatic ansatz. Our findings indicate that fewer iterations are required when local cost landscape minima are present, and the SPSA-based BFGS optimizer emerges as a standout choice for the hybrid DCQC paradigm.
Anglés-Castillo, A.; Pérez, A.; Roldán, E.
Bright and dark solitons in a photonic nonlinear quantum walk: lessons from the continuum Artículo de revista
En: New Journal of Physics, vol. 26, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: UV
@article{nokey,
title = {Bright and dark solitons in a photonic nonlinear quantum walk: lessons from the continuum},
author = {Anglés-Castillo, A. and Pérez, A. and Roldán, E. },
url = {https://iopscience.iop.org/article/10.1088/1367-2630/ad1e24},
doi = {10.1088/1367-2630/ad1e24},
year = {2024},
date = {2024-02-05},
journal = {New Journal of Physics},
volume = {26},
abstract = {We propose a nonlinear quantum walk model inspired in a photonic implementation in which the polarization state of the light field plays the role of the coin-qubit. In particular, we take profit of the nonlinear polarization rotation occurring in optical media with Kerr nonlinearity, which allows to implement a nonlinear coin operator, one that depends on the state of the coin-qubit. We consider the space-time continuum limit of the evolution equation, which takes the form of a nonlinear Dirac equation. The analysis of this continuum limit allows us to gain some insight into the existence of different solitonic structures, such as bright and dark solitons. We illustrate several properties of these solitons with numerical calculations, including the effect on them of an additional phase simulating an external electric field.},
keywords = {UV},
pubstate = {published},
tppubtype = {article}
}
We propose a nonlinear quantum walk model inspired in a photonic implementation in which the polarization state of the light field plays the role of the coin-qubit. In particular, we take profit of the nonlinear polarization rotation occurring in optical media with Kerr nonlinearity, which allows to implement a nonlinear coin operator, one that depends on the state of the coin-qubit. We consider the space-time continuum limit of the evolution equation, which takes the form of a nonlinear Dirac equation. The analysis of this continuum limit allows us to gain some insight into the existence of different solitonic structures, such as bright and dark solitons. We illustrate several properties of these solitons with numerical calculations, including the effect on them of an additional phase simulating an external electric field.
Olivera-Atencio, M. L.; Lamata, L.; Casado-Pascual, J.
Benefits of Open Quantum Systems for Quantum Machine Learning Artículo de revista
En: Adv Quantum Technologies, 2023, ISBN: 2511-9044.
Resumen | Enlaces | BibTeX | Etiquetas: artificial intelligence, Quantum algorithms, quantum machine learning, US
@article{nokey,
title = {Benefits of Open Quantum Systems for Quantum Machine Learning},
author = {Olivera-Atencio, M.L. and Lamata, L. and Casado-Pascual, J.
},
url = {https://quantumspain-project.es/wp-content/uploads/2023/12/Adv-Quantum-Tech-2023-Olivera‐Atencio-Benefits-of-Open-Quantum-Systems-for-Quantum-Machine-Learning.pdf},
doi = {10.1002/qute.202300247},
isbn = {2511-9044},
year = {2023},
date = {2023-12-10},
urldate = {2023-12-10},
journal = {Adv Quantum Technologies},
abstract = {Quantum machine learning (QML) is a discipline that holds the promise ofrevolutionizing data processing and problem-solving. However, dissipationand noise arising from the coupling with the environment are commonlyperceived as major obstacles to its practical exploitation, as they impact thecoherence and performance of the utilized quantum devices. Significantefforts have been dedicated to mitigating and controlling their negative effectson these devices. This perspective takes a different approach, aiming toharness the potential of noise and dissipation instead of combating them.Surprisingly, it is shown that these seemingly detrimental factors can providesubstantial advantages in the operation of QML algorithms under certaincircumstances. Exploring and understanding the implications of adaptingQML algorithms to open quantum systems opens up pathways for devisingstrategies that effectively leverage noise and dissipation. The recent worksanalyzed in this perspective represent only initial steps toward uncoveringother potential hidden benefits that dissipation and noise may offer. Asexploration in this field continues, significant discoveries are anticipated thatcould reshape the future of quantum computing.},
keywords = {artificial intelligence, Quantum algorithms, quantum machine learning, US},
pubstate = {published},
tppubtype = {article}
}
Quantum machine learning (QML) is a discipline that holds the promise ofrevolutionizing data processing and problem-solving. However, dissipationand noise arising from the coupling with the environment are commonlyperceived as major obstacles to its practical exploitation, as they impact thecoherence and performance of the utilized quantum devices. Significantefforts have been dedicated to mitigating and controlling their negative effectson these devices. This perspective takes a different approach, aiming toharness the potential of noise and dissipation instead of combating them.Surprisingly, it is shown that these seemingly detrimental factors can providesubstantial advantages in the operation of QML algorithms under certaincircumstances. Exploring and understanding the implications of adaptingQML algorithms to open quantum systems opens up pathways for devisingstrategies that effectively leverage noise and dissipation. The recent worksanalyzed in this perspective represent only initial steps toward uncoveringother potential hidden benefits that dissipation and noise may offer. Asexploration in this field continues, significant discoveries are anticipated thatcould reshape the future of quantum computing.
Ding, Y.; Martín-Guerrero, J. D.; Vives-Gilabert, Y.; Chen, X.
Active Learning in Physics: From 101, to Progress, and Perspective Bachelor Thesis
2023.
Resumen | Enlaces | BibTeX | Etiquetas: UV
@bachelorthesis{nokey,
title = {Active Learning in Physics: From 101, to Progress, and Perspective},
author = {Ding, Y. and Martín-Guerrero, J.D. and Vives-Gilabert, Y. and Chen, X. },
url = {https://onlinelibrary.wiley.com/doi/10.1002/qute.202300208},
doi = {doi.org/10.1002/qute.202300208},
year = {2023},
date = {2023-11-30},
urldate = {2023-11-30},
journal = {Advanced Quantum Technologies},
abstract = {Active learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence. Unlike traditional approaches that require labeled samples for training, AL iteratively selects unlabeled samples to be annotated by an expert. This protocol aims to prioritize the most informative samples, leading to improved model performance compared to training with all labeled samples. In recent years, AL has gained increasing attention, particularly in the field of physics. This paper presents a comprehensive and accessible introduction to the theory of AL reviewing the latest advancements across various domains. Additionally, the potential integration of AL is explored with quantum ML, envisioning a synergistic fusion of these two fields rather than viewing AL as a mere extension of classical ML into the quantum realm.},
keywords = {UV},
pubstate = {published},
tppubtype = {bachelorthesis}
}
Active learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence. Unlike traditional approaches that require labeled samples for training, AL iteratively selects unlabeled samples to be annotated by an expert. This protocol aims to prioritize the most informative samples, leading to improved model performance compared to training with all labeled samples. In recent years, AL has gained increasing attention, particularly in the field of physics. This paper presents a comprehensive and accessible introduction to the theory of AL reviewing the latest advancements across various domains. Additionally, the potential integration of AL is explored with quantum ML, envisioning a synergistic fusion of these two fields rather than viewing AL as a mere extension of classical ML into the quantum realm.
Clemente, G.; Crippa, A.; Jansen, K.; Ramírez-Uribe, S.; Rentería-Olivo, A. E.; Rodrigo, G.; Sborlini Germán Rodrigo, G.; Silva, L. V.
Variational quantum eigensolver for causal loop Feynman diagrams and directed acyclic graphs Artículo de revista
En: Physical Review D, vol. 108, iss. 9, 2023.
Resumen | Enlaces | BibTeX | Etiquetas: UV
@article{nokey,
title = {Variational quantum eigensolver for causal loop Feynman diagrams and directed acyclic graphs},
author = {Clemente, G. and Crippa, A. and Jansen, K. and Ramírez-Uribe, S. and Rentería-Olivo, A.E. and Rodrigo, G. and Germán Rodrigo, Sborlini, G. and Silva, L.V. },
url = {https://journals.aps.org/prd/abstract/10.1103/PhysRevD.108.096035},
doi = {doi.org/10.1103/PhysRevD.108.096035},
year = {2023},
date = {2023-11-29},
urldate = {2023-11-29},
journal = {Physical Review D},
volume = {108},
issue = {9},
abstract = {We present a variational quantum eigensolver (VQE) algorithm for the efficient bootstrapping of the causal representation of multiloop Feynman diagrams in the loop-tree duality or, equivalently, the selection of acyclic configurations in directed graphs. A loop Hamiltonian based on the adjacency matrix describing a multiloop topology, and whose different energy levels correspond to the number of cycles, is minimized by VQE to identify the causal or acyclic configurations. The algorithm has been adapted to select multiple degenerated minima and thus achieves higher detection rates. A performance comparison with a Grover’s based algorithm is discussed in detail. The VQE approach requires, in general, fewer qubits and shorter circuits for its implementation, albeit with lesser success rates.},
keywords = {UV},
pubstate = {published},
tppubtype = {article}
}
We present a variational quantum eigensolver (VQE) algorithm for the efficient bootstrapping of the causal representation of multiloop Feynman diagrams in the loop-tree duality or, equivalently, the selection of acyclic configurations in directed graphs. A loop Hamiltonian based on the adjacency matrix describing a multiloop topology, and whose different energy levels correspond to the number of cycles, is minimized by VQE to identify the causal or acyclic configurations. The algorithm has been adapted to select multiple degenerated minima and thus achieves higher detection rates. A performance comparison with a Grover’s based algorithm is discussed in detail. The VQE approach requires, in general, fewer qubits and shorter circuits for its implementation, albeit with lesser success rates.
Martín-Guerrero, J. D.; Lamata, L.; Villmann, T.
Quantum Artificial Intelligence: A tutorial Conferencia
2023, ISBN: 978-2-87587-088-9.
Resumen | Enlaces | BibTeX | Etiquetas: artificial intelligence, machine learning, US
@conference{nokey,
title = {Quantum Artificial Intelligence: A tutorial},
author = {Martín-Guerrero, J. D. and Lamata, L. and Villmann, T.},
url = {https://quantumspain-project.es/wp-content/uploads/2023/09/ES2023-2.pdf},
doi = {10.14428/esann/2023.ES2023-2},
isbn = {978-2-87587-088-9},
year = {2023},
date = {2023-10-06},
urldate = {2023-10-06},
abstract = {This special session includes five high-quality papers on relevant topics, like quantum reinforcement learning, parallelization of quantum calculations, quantum feature selection and quantum vector quantization, thus capturing the richness and variability of approaches within QAI.},
keywords = {artificial intelligence, machine learning, US},
pubstate = {published},
tppubtype = {conference}
}
This special session includes five high-quality papers on relevant topics, like quantum reinforcement learning, parallelization of quantum calculations, quantum feature selection and quantum vector quantization, thus capturing the richness and variability of approaches within QAI.
Etxezarreta Martinez, J.; deMarti iOlius, A.; Crespo, P. M.
Superadditivity effects of quantum capacity decrease with the dimension for qudit depolarizing channels Artículo de revista
En: Physical Review A, vol. 108, iss. 3, 2023.
Resumen | Enlaces | BibTeX | Etiquetas: tecnun
@article{nokey,
title = {Superadditivity effects of quantum capacity decrease with the dimension for qudit depolarizing channels},
author = {Etxezarreta Martinez, J. and deMarti iOlius, A. and Crespo, P. M. },
url = {https://quantumspain-project.es/wp-content/uploads/2023/10/2301.10132-2.pdf},
doi = {doi.org/10.48550/arXiv.2301.10132},
year = {2023},
date = {2023-09-12},
urldate = {2023-09-12},
journal = {Physical Review A},
volume = {108},
issue = {3},
abstract = {Quantum channel capacity is a fundamental quantity in order to understand how well quantum information can be transmitted or corrected when subjected to noise. However, it is generally not known how to compute such quantities since the quantum channel coherent information is not additive for all channels, implying that it must be maximized over an unbounded number of channel uses. This leads to the phenomenon known as superadditivity, which refers to the fact that the regularized coherent information of n channel uses exceeds one-shot coherent information. In this article, we study how the gain in quantum capacity of qudit depolarizing channels relates to the dimension of the considered systems. We make use of an argument based on the no-cloning bound in order to prove that the possible superadditive effects decrease as a function of the dimension for such family of channels. In addition, we prove that the capacity of the qudit depolarizing channel coincides with the coherent information when d→∞. We also discuss the private classical capacity and obtain similar results. We conclude that when high-dimensional qudits experiencing depolarizing noise are considered, the coherent information of the channel is not only an achievable rate, but essentially the maximum possible rate for any quantum block code.},
keywords = {tecnun},
pubstate = {published},
tppubtype = {article}
}
Quantum channel capacity is a fundamental quantity in order to understand how well quantum information can be transmitted or corrected when subjected to noise. However, it is generally not known how to compute such quantities since the quantum channel coherent information is not additive for all channels, implying that it must be maximized over an unbounded number of channel uses. This leads to the phenomenon known as superadditivity, which refers to the fact that the regularized coherent information of n channel uses exceeds one-shot coherent information. In this article, we study how the gain in quantum capacity of qudit depolarizing channels relates to the dimension of the considered systems. We make use of an argument based on the no-cloning bound in order to prove that the possible superadditive effects decrease as a function of the dimension for such family of channels. In addition, we prove that the capacity of the qudit depolarizing channel coincides with the coherent information when d→∞. We also discuss the private classical capacity and obtain similar results. We conclude that when high-dimensional qudits experiencing depolarizing noise are considered, the coherent information of the channel is not only an achievable rate, but essentially the maximum possible rate for any quantum block code.
Hernani-Morales, C.; Alvarado, G.; Albarrán-Arriagada, F.; Vives-Gilabert, Y.; Solano, E.; Martín-Guerrero, J. D.
Machine Learning for maximizing the memristivity of single and coupled quantum memristors pre-print
2023.
Resumen | Enlaces | BibTeX | Etiquetas: machine learning, UV
@pre-print{nokey,
title = {Machine Learning for maximizing the memristivity of single and coupled quantum memristors},
author = {Hernani-Morales, C. and Alvarado, G. and Albarrán-Arriagada, F. and Vives-Gilabert, Y. and Solano, E. and Martín-Guerrero, J.D. },
url = {https://quantumspain-project.es/wp-content/uploads/2023/09/2309.05062.pdf},
doi = { https://doi.org/10.48550/arXiv.2309.05062},
year = {2023},
date = {2023-09-10},
urldate = {2023-09-10},
abstract = {We propose machine learning (ML) methods to characterize the memristive properties of single and coupled quantum memristors. We show that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unveiling the close relationship between quantum correlations and memory. Our results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing.
},
keywords = {machine learning, UV},
pubstate = {published},
tppubtype = {pre-print}
}
We propose machine learning (ML) methods to characterize the memristive properties of single and coupled quantum memristors. We show that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unveiling the close relationship between quantum correlations and memory. Our results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing.
Ferrer-Sánchez, A.; Flores-Garrigós, C.; Hernani-Morales, C.; Orquín-Marqués, J. J.; Hegade, N. N.; A.G. Cadavid, Montalban; Solano, E.; Vives-Gilabert, Y.; Martín-Guerrero, J. D.
Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation pre-print
2023.
Resumen | Enlaces | BibTeX | Etiquetas: UV
@pre-print{nokey,
title = {Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation},
author = {Ferrer-Sánchez, A. and Flores-Garrigós, C. and Hernani-Morales, C. and Orquín-Marqués, J.J. and Hegade, N.N. and Cadavid, A.G., Montalban, I. and Solano, E. and Vives-Gilabert, Y. and Martín-Guerrero, J.D. },
url = {https://quantumspain-project.es/wp-content/uploads/2023/09/2309.04434.pdf},
doi = { https://doi.org/10.48550/arXiv.2309.04434},
year = {2023},
date = {2023-09-08},
abstract = {We introduce a novel methodology that leverages the strength of Physics-Informed Neural Networks (PINNs) to address the counterdiabatic (CD) protocol in the optimization of quantum circuits comprised of systems with NQ qubits. The primary objective is to utilize physics-inspired deep learning techniques to accurately solve the time evolution of the different physical observables within the quantum system. To accomplish this objective, we embed the necessary physical information into an underlying neural network to effectively tackle the problem. In particular, we impose the hermiticity condition on all physical observables and make use of the principle of least action, guaranteeing the acquisition of the most appropriate counterdiabatic terms based on the underlying physics. The proposed approach offers a dependable alternative to address the CD driving problem, free from the constraints typically encountered in previous methodologies relying on classical numerical approximations. Our method provides a general framework to obtain optimal results from the physical observables relevant to the problem, including the external parameterization in time known as scheduling function, the gauge potential or operator involving the non-adiabatic terms, as well as the temporal evolution of the energy levels of the system, among others. The main applications of this methodology have been the H2 and LiH molecules, represented by a 2-qubit and 4-qubit systems employing the STO-3G basis. The presented results demonstrate the successful derivation of a desirable decomposition for the non-adiabatic terms, achieved through a linear combination utilizing Pauli operators. This attribute confers significant advantages to its practical implementation within quantum computing algorithms.},
keywords = {UV},
pubstate = {published},
tppubtype = {pre-print}
}
We introduce a novel methodology that leverages the strength of Physics-Informed Neural Networks (PINNs) to address the counterdiabatic (CD) protocol in the optimization of quantum circuits comprised of systems with NQ qubits. The primary objective is to utilize physics-inspired deep learning techniques to accurately solve the time evolution of the different physical observables within the quantum system. To accomplish this objective, we embed the necessary physical information into an underlying neural network to effectively tackle the problem. In particular, we impose the hermiticity condition on all physical observables and make use of the principle of least action, guaranteeing the acquisition of the most appropriate counterdiabatic terms based on the underlying physics. The proposed approach offers a dependable alternative to address the CD driving problem, free from the constraints typically encountered in previous methodologies relying on classical numerical approximations. Our method provides a general framework to obtain optimal results from the physical observables relevant to the problem, including the external parameterization in time known as scheduling function, the gauge potential or operator involving the non-adiabatic terms, as well as the temporal evolution of the energy levels of the system, among others. The main applications of this methodology have been the H2 and LiH molecules, represented by a 2-qubit and 4-qubit systems employing the STO-3G basis. The presented results demonstrate the successful derivation of a desirable decomposition for the non-adiabatic terms, achieved through a linear combination utilizing Pauli operators. This attribute confers significant advantages to its practical implementation within quantum computing algorithms.
De Marti i Olius, A.; Etxezarreta Martinez, J.; Fuentes, P.; Crespo, P. M.
Performance enhancement of surface codes via recursive minimum-weight perfect-match decoding Artículo de revista
En: Physical Review A, vol. 108, iss. 2, 2023, ISBN: 2469-9934.
Resumen | Enlaces | BibTeX | Etiquetas: tecnun
@article{,
title = {Performance enhancement of surface codes via recursive minimum-weight perfect-match decoding},
author = {De Marti i Olius, A. and Etxezarreta Martinez, J. and Fuentes, P. and Crespo, P. M.},
url = {https://quantumspain-project.es/wp-content/uploads/2023/10/2212.11632.pdf},
doi = {doi.org/10.1103/PhysRevA.108.022401},
isbn = {2469-9934},
year = {2023},
date = {2023-08-03},
urldate = {2023-08-03},
journal = {Physical Review A},
volume = {108},
issue = {2},
abstract = {The minimum weight perfect matching (MWPM) decoder is the standard decoding strategy for quantum surface codes. However, it suffers a harsh decrease in performance when subjected to biased or nonidentical quantum noise. In this work, we modify the conventional MWPM decoder so that it considers the biases, the nonuniformities, and the relationship between X, Y, and Z errors of the constituent qubits of a given surface code. Our modified approach, which we refer to as the recursive MWPM decoder, obtains an 18% improvement in the probability threshold pth under depolarizing noise. We also obtain significant performance improvements when considering biased noise and independent nonidentically distributed (i.ni.d.) error models derived from measurements performed on state-of-the-art quantum processors. In fact, when subjected to i.ni.d. noise, the recursive MWPM decoder yields a performance improvement of 105.5% over the conventional MWPM strategy, and in some cases, it even surpasses the performance obtained over the well-known depolarizing channel.},
keywords = {tecnun},
pubstate = {published},
tppubtype = {article}
}
The minimum weight perfect matching (MWPM) decoder is the standard decoding strategy for quantum surface codes. However, it suffers a harsh decrease in performance when subjected to biased or nonidentical quantum noise. In this work, we modify the conventional MWPM decoder so that it considers the biases, the nonuniformities, and the relationship between X, Y, and Z errors of the constituent qubits of a given surface code. Our modified approach, which we refer to as the recursive MWPM decoder, obtains an 18% improvement in the probability threshold pth under depolarizing noise. We also obtain significant performance improvements when considering biased noise and independent nonidentically distributed (i.ni.d.) error models derived from measurements performed on state-of-the-art quantum processors. In fact, when subjected to i.ni.d. noise, the recursive MWPM decoder yields a performance improvement of 105.5% over the conventional MWPM strategy, and in some cases, it even surpasses the performance obtained over the well-known depolarizing channel.
Pérez-Obiol, A.; Romero, A. M.; Menéndez, J.; Rios, A.; García-Sáez, A.; Juliá-Díaz, B.
Nuclear shell-model simulation in digital quantum computers Artículo de revista
En: Scientific Reports, vol. 13, 2023.
Resumen | Enlaces | BibTeX | Etiquetas: algorithms, quantic, quantum computing, simulations
@article{nokey,
title = {Nuclear shell-model simulation in digital quantum computers},
author = {Pérez-Obiol, A. and Romero, A. M. and Menéndez, J. and Rios, A. and García-Sáez, A. and Juliá-Díaz, B. },
url = {https://www.nature.com/articles/s41598-023-39263-7},
doi = {doi.org/10.1038/s41598-023-39263-7},
year = {2023},
date = {2023-07-29},
urldate = {2023-02-07},
journal = {Scientific Reports},
volume = {13},
abstract = {The nuclear shell model is one of the prime many-body methods to study the structure of atomic nuclei, but it is hampered by an exponential scaling on the basis size as the number of particles increases. We present a shell-model quantum circuit design strategy to find nuclear ground states that circumvents this limitation by exploiting an adaptive variational quantum eigensolver algorithm. Our circuit implementation is in excellent agreement with classical shell-model simulations for a dozen of light and medium-mass nuclei, including neon and calcium isotopes. We quantify the circuit depth, width and number of gates to encode realistic shell-model wavefunctions. Our strategy also addresses explicitly energy measurements and the required number of circuits to perform them. Our simulated circuits approach the benchmark results exponentially with a polynomial scaling in quantum resources for each nucleus and configuration space. Our work paves the way for quantum computing shell-model studies across the nuclear chart.},
keywords = {algorithms, quantic, quantum computing, simulations},
pubstate = {published},
tppubtype = {article}
}
The nuclear shell model is one of the prime many-body methods to study the structure of atomic nuclei, but it is hampered by an exponential scaling on the basis size as the number of particles increases. We present a shell-model quantum circuit design strategy to find nuclear ground states that circumvents this limitation by exploiting an adaptive variational quantum eigensolver algorithm. Our circuit implementation is in excellent agreement with classical shell-model simulations for a dozen of light and medium-mass nuclei, including neon and calcium isotopes. We quantify the circuit depth, width and number of gates to encode realistic shell-model wavefunctions. Our strategy also addresses explicitly energy measurements and the required number of circuits to perform them. Our simulated circuits approach the benchmark results exponentially with a polynomial scaling in quantum resources for each nucleus and configuration space. Our work paves the way for quantum computing shell-model studies across the nuclear chart.
Etxezarreta Martinez, J.; Fuentes, P.; deMarti iOlius, A.; Garcia-Frias, J.; Rodríguez Fonollosa, J.; Crespo, P. M.
Multiqubit time-varying quantum channels for NISQ-era superconducting quantum processors Artículo de revista
En: Physical Review Research, vol. 5, iss. 3, 2023, ISBN: 2643-1564.
Resumen | Enlaces | BibTeX | Etiquetas: tecnun
@article{nokey,
title = {Multiqubit time-varying quantum channels for NISQ-era superconducting quantum processors},
author = {Etxezarreta Martinez, J. and Fuentes, P. and deMarti iOlius, A. and Garcia-Frias, J. and Rodríguez Fonollosa, J. and Crespo, P.M.},
url = {https://journals.aps.org/prresearch/pdf/10.1103/PhysRevResearch.5.033055},
doi = {doi.org/10.1103/PhysRevResearch.5.033055},
isbn = {2643-1564},
year = {2023},
date = {2023-07-26},
journal = {Physical Review Research},
volume = {5},
issue = {3},
abstract = {Time-varying quantum channels (TVQCs) have been proposed as a model to include fluctuations of the relaxation (𝑇1) and dephasing times (𝑇2). In previous works, realizations of multiqubit TVQCs have been assumed to be equal for all the qubits of an error correction block, implying that the random variables that describe the fluctuations of 𝑇1 and 𝑇2 are block-to-block uncorrelated but qubit-wise perfectly correlated for the same block. In this article, we perform a correlation analysis of the fluctuations of the relaxation times of five multiqubit quantum processors. Our results show that it is reasonable to assume that the fluctuations of the relaxation and dephasing times of superconducting qubits are local to each of the qubits of the system. Based on these results, we discuss the multiqubit TVQCs when the fluctuations of the decoherence parameters for an error correction block are qubit-wise uncorrelated (as well as from block-to-block), a scenario we have named the fast time-varying quantum channel (FTVQC). Furthermore, we lower-bound the quantum capacity of general FTVQCs based on a quantity we refer to as the ergodic quantum capacity. Finally, we use numerical simulations to study the performance of quantum error correction codes when they operate over FTVQCs.},
keywords = {tecnun},
pubstate = {published},
tppubtype = {article}
}
Time-varying quantum channels (TVQCs) have been proposed as a model to include fluctuations of the relaxation (𝑇1) and dephasing times (𝑇2). In previous works, realizations of multiqubit TVQCs have been assumed to be equal for all the qubits of an error correction block, implying that the random variables that describe the fluctuations of 𝑇1 and 𝑇2 are block-to-block uncorrelated but qubit-wise perfectly correlated for the same block. In this article, we perform a correlation analysis of the fluctuations of the relaxation times of five multiqubit quantum processors. Our results show that it is reasonable to assume that the fluctuations of the relaxation and dephasing times of superconducting qubits are local to each of the qubits of the system. Based on these results, we discuss the multiqubit TVQCs when the fluctuations of the decoherence parameters for an error correction block are qubit-wise uncorrelated (as well as from block-to-block), a scenario we have named the fast time-varying quantum channel (FTVQC). Furthermore, we lower-bound the quantum capacity of general FTVQCs based on a quantity we refer to as the ergodic quantum capacity. Finally, we use numerical simulations to study the performance of quantum error correction codes when they operate over FTVQCs.
Combarro, E. F.; Pérez-Fernández, R.; Ranilla, J.; De Baets, B.
Solving the Kemeny ranking aggregation problem with quantum optimization algorithms Artículo de revista
En: Mathematical Methods in the Applied Sciences, vol. 46, iss. 16, 2023, ISBN: 0170-4214.
Resumen | Enlaces | BibTeX | Etiquetas: UNIOVI
@article{nokey,
title = {Solving the Kemeny ranking aggregation problem with quantum optimization algorithms},
author = {Combarro, E.F. and Pérez-Fernández, R. and Ranilla, J. and De Baets, B. },
url = {https://onlinelibrary.wiley.com/doi/full/10.1002/mma.9489},
doi = {doi.org/10.1002/mma.9489},
isbn = {0170-4214},
year = {2023},
date = {2023-07-13},
journal = {Mathematical Methods in the Applied Sciences},
volume = {46},
issue = {16},
abstract = {The aim of a ranking aggregation problem is to combine several rankings into a single one that best represents them. A common method for solving this problem is due to Kemeny and selects as the aggregated ranking the one that minimizes the sum of the Kendall distances to the rankings to be aggregated. Unfortunately, the identification of the said ranking—called the Kemeny ranking—is known to be a computationally expensive task. In this paper, we study different ways of computing the Kemeny ranking with quantum optimization algorithms, and in particular, we provide some alternative formulations for the search for the Kemeny ranking as an optimization problem. To the best of our knowledge, this is the first time that this problem is addressed with quantum techniques. We propose four different ways of formulating the problem, one novel to this work. Two different quantum optimization algorithms—Quantum Approximate Optimization Algorithm and Quantum Adiabatic Computing—are used to evaluate each of the different formulations. The experimental results show that the choice of the formulation plays a big role on the performance of the quantum optimization algorithms.},
keywords = {UNIOVI},
pubstate = {published},
tppubtype = {article}
}
The aim of a ranking aggregation problem is to combine several rankings into a single one that best represents them. A common method for solving this problem is due to Kemeny and selects as the aggregated ranking the one that minimizes the sum of the Kendall distances to the rankings to be aggregated. Unfortunately, the identification of the said ranking—called the Kemeny ranking—is known to be a computationally expensive task. In this paper, we study different ways of computing the Kemeny ranking with quantum optimization algorithms, and in particular, we provide some alternative formulations for the search for the Kemeny ranking as an optimization problem. To the best of our knowledge, this is the first time that this problem is addressed with quantum techniques. We propose four different ways of formulating the problem, one novel to this work. Two different quantum optimization algorithms—Quantum Approximate Optimization Algorithm and Quantum Adiabatic Computing—are used to evaluate each of the different formulations. The experimental results show that the choice of the formulation plays a big role on the performance of the quantum optimization algorithms.
Pérez-Obiol, A.; Masot-Llima, S.; Romero, A. M.; Menéndez, J.; Rios, A.; García-Sáez, A.; Juliá-Díaz, B.
Quantum entanglement patterns in the structure of atomic nuclei within the nuclear shell model Artículo de revista
En: The European Physical Journal A, vol. 59, no 240, 2023, ISBN: 1434-601X.
Resumen | Enlaces | BibTeX | Etiquetas: quantic
@article{nokey,
title = {Quantum entanglement patterns in the structure of atomic nuclei within the nuclear shell model},
author = {Pérez-Obiol, A. and Masot-Llima, S. and Romero, A. M. and Menéndez, J. and Rios, A. and García-Sáez, A. and Juliá-Díaz, B. },
url = {https://link.springer.com/article/10.1140/epja/s10050-023-01151-z},
doi = {doi.org/10.1140/epja/s10050-023-01151-z},
isbn = {1434-601X},
year = {2023},
date = {2023-07-11},
urldate = {2023-07-11},
journal = {The European Physical Journal A},
volume = {59},
number = {240},
abstract = {Quantum entanglement offers a unique perspective into the underlying structure of strongly-correlated systems such as atomic nuclei. In this paper, we use quantum information tools to analyze the structure of light and medium-mass berillyum, oxygen, neon and calcium isotopes within the nuclear shell model. We use different entanglement metrics, including single-orbital entanglement, mutual information, and von Neumann entropies for different equipartitions of the shell-model valence space and identify mode-entanglement patterns related to the energy, angular momentum and isospin of the nuclear single-particle orbitals. We observe that the single-orbital entanglement is directly related to the number of valence nucleons and the energy structure of the shell, while the mutual information highlights signatures of proton–proton and neutron–neutron pairing, as well as nuclear deformation. Proton and neutron orbitals are weakly entangled by all measures, and in fact have the lowest von Neumann entropies among all possible equipartitions of the valence space. In contrast, orbitals with opposite angular momentum projection have relatively large entropies, especially in spherical nuclei. This analysis provides a guide for designing more efficient quantum algorithms for the noisy intermediate-scale quantum era.},
keywords = {quantic},
pubstate = {published},
tppubtype = {article}
}
Quantum entanglement offers a unique perspective into the underlying structure of strongly-correlated systems such as atomic nuclei. In this paper, we use quantum information tools to analyze the structure of light and medium-mass berillyum, oxygen, neon and calcium isotopes within the nuclear shell model. We use different entanglement metrics, including single-orbital entanglement, mutual information, and von Neumann entropies for different equipartitions of the shell-model valence space and identify mode-entanglement patterns related to the energy, angular momentum and isospin of the nuclear single-particle orbitals. We observe that the single-orbital entanglement is directly related to the number of valence nucleons and the energy structure of the shell, while the mutual information highlights signatures of proton–proton and neutron–neutron pairing, as well as nuclear deformation. Proton and neutron orbitals are weakly entangled by all measures, and in fact have the lowest von Neumann entropies among all possible equipartitions of the valence space. In contrast, orbitals with opposite angular momentum projection have relatively large entropies, especially in spherical nuclei. This analysis provides a guide for designing more efficient quantum algorithms for the noisy intermediate-scale quantum era.
Nzongani, U.; Zylberman, J.; Doncecchi, C. E.; Pérez, A.; Debbasch, F.; Arnault, P.
Quantum circuits for discrete-time quantum walks with position-dependent coin operator Artículo de revista
En: 2023.
Resumen | Enlaces | BibTeX | Etiquetas: UV
@article{nokey,
title = {Quantum circuits for discrete-time quantum walks with position-dependent coin operator},
author = {Nzongani, U. and Zylberman, J. and Doncecchi, C.E. and Pérez, A. and Debbasch, F. and Arnault, P. },
url = {https://link.springer.com/article/10.1007/s11128-023-03957-8},
doi = {doi.org/10.1007/s11128-023-03957-8},
year = {2023},
date = {2023-07-01},
abstract = {The aim of this paper is to build quantum circuits that implement discrete-time quantum walks having an arbitrary position-dependent coin operator. The position of the walker is encoded in base 2: with n wires, each corresponding to one qubit, we encode position states. The data necessary to define an arbitrary position-dependent coin operator is therefore exponential in n. Hence, the exponentiality will necessarily appear somewhere in our circuits. We first propose a circuit implementing the position-dependent coin operator, that is naive, in the sense that it has exponential depth and implements sequentially all appropriate position-dependent coin operators. We then propose a circuit that “transfers” all the depth into ancillae, yielding a final depth that is linear in n at the cost of an exponential number of ancillae. The main idea of this linear-depth circuit is to implement in parallel all coin operators at the different positions. Reducing the depth exponentially at the cost of having an exponential number of ancillae is a goal which has already been achieved for the problem of loading classical data on a quantum circuit (Araujo in Sci Rep 11:6329, 2021) (notice that such a circuit can be used to load the initial state of the walker). Here, we achieve this goal for the problem of applying a position-dependent coin operator in a discrete-time quantum walk. Finally, we extend the result of Welch (New J Phys 16:033040, 2014) from position-dependent unitaries which are diagonal in the position basis to position-dependent -block-diagonal unitaries: indeed, we show that for a position dependence of the coin operator (the block-diagonal unitary) which is smooth enough, one can find an efficient quantum-circuit implementation approximating the coin operator up to an error (in terms of the spectral norm), the depth and size of which scale as. A typical application of the efficient implementation would be the quantum simulation of a relativistic spin-1/2 particle on a lattice, coupled to a smooth external gauge field; notice that recently, quantum spatial-search schemes have been developed which use gauge fields as the oracle, to mark the vertex to be found (Zylberman in Entropy 23:1441, 2021), (Fredon arXiv:2210.13920). A typical application of the linear-depth circuit would be when there is spatial noise on the coin operator (and hence a non-smooth dependence in the position).},
keywords = {UV},
pubstate = {published},
tppubtype = {article}
}
The aim of this paper is to build quantum circuits that implement discrete-time quantum walks having an arbitrary position-dependent coin operator. The position of the walker is encoded in base 2: with n wires, each corresponding to one qubit, we encode position states. The data necessary to define an arbitrary position-dependent coin operator is therefore exponential in n. Hence, the exponentiality will necessarily appear somewhere in our circuits. We first propose a circuit implementing the position-dependent coin operator, that is naive, in the sense that it has exponential depth and implements sequentially all appropriate position-dependent coin operators. We then propose a circuit that “transfers” all the depth into ancillae, yielding a final depth that is linear in n at the cost of an exponential number of ancillae. The main idea of this linear-depth circuit is to implement in parallel all coin operators at the different positions. Reducing the depth exponentially at the cost of having an exponential number of ancillae is a goal which has already been achieved for the problem of loading classical data on a quantum circuit (Araujo in Sci Rep 11:6329, 2021) (notice that such a circuit can be used to load the initial state of the walker). Here, we achieve this goal for the problem of applying a position-dependent coin operator in a discrete-time quantum walk. Finally, we extend the result of Welch (New J Phys 16:033040, 2014) from position-dependent unitaries which are diagonal in the position basis to position-dependent -block-diagonal unitaries: indeed, we show that for a position dependence of the coin operator (the block-diagonal unitary) which is smooth enough, one can find an efficient quantum-circuit implementation approximating the coin operator up to an error (in terms of the spectral norm), the depth and size of which scale as. A typical application of the efficient implementation would be the quantum simulation of a relativistic spin-1/2 particle on a lattice, coupled to a smooth external gauge field; notice that recently, quantum spatial-search schemes have been developed which use gauge fields as the oracle, to mark the vertex to be found (Zylberman in Entropy 23:1441, 2021), (Fredon arXiv:2210.13920). A typical application of the linear-depth circuit would be when there is spatial noise on the coin operator (and hence a non-smooth dependence in the position).
Casas, B.; Cervera-Lierta, A.
Multi-dimensional Fourier series with quantum circuits Artículo de revista
En: Physical Review A, vol. 107, iss. 5, pp. 15, 2023.
Resumen | Enlaces | BibTeX | Etiquetas: algorithms, quantic, quantumcircuits, quantumsimulation
@article{,
title = {Multi-dimensional Fourier series with quantum circuits},
author = {Casas, B. and Cervera-Lierta, A.},
url = {https://journals.aps.org/pra/abstract/10.1103/PhysRevA.107.062612
Preprint version: https://arxiv.org/abs/2302.03389
},
doi = {10.1103/PhysRevA.107.062612},
year = {2023},
date = {2023-06-29},
urldate = {2023-06-29},
journal = {Physical Review A},
volume = {107},
issue = {5},
pages = {15},
abstract = {Quantum machine learning is the field that aims to integrate machine learning with quantum computation. In recent years, the field has emerged as an active research area with the potential to bring new insights to classical machine learning problems. One of the challenges in the field is to explore the expressibility of parametrized quantum circuits and their ability to be universal function approximators, as classical neural networks are. Recent works have shown that, with a quantum supervised learning model, we can fit any one-dimensional Fourier series, proving their universality. However, models for multidimensional functions have not been explored in the same level of detail. In this work, we study the expressibility of various types of circuit Ansätze that generate multidimensional Fourier series. We found that, for some Ansätze, the degrees of freedom required for fitting such functions grow faster than the available degrees in the Hilbert space generated by the circuits. For example, single-qudit models have limited power to represent arbitrary multidimensional Fourier series. Despite this, we show that we can enlarge the Hilbert space of the circuit by using more qudits or higher local dimensions to meet the degrees of freedom requirements, thus ensuring the universality of the models.},
keywords = {algorithms, quantic, quantumcircuits, quantumsimulation},
pubstate = {published},
tppubtype = {article}
}
Quantum machine learning is the field that aims to integrate machine learning with quantum computation. In recent years, the field has emerged as an active research area with the potential to bring new insights to classical machine learning problems. One of the challenges in the field is to explore the expressibility of parametrized quantum circuits and their ability to be universal function approximators, as classical neural networks are. Recent works have shown that, with a quantum supervised learning model, we can fit any one-dimensional Fourier series, proving their universality. However, models for multidimensional functions have not been explored in the same level of detail. In this work, we study the expressibility of various types of circuit Ansätze that generate multidimensional Fourier series. We found that, for some Ansätze, the degrees of freedom required for fitting such functions grow faster than the available degrees in the Hilbert space generated by the circuits. For example, single-qudit models have limited power to represent arbitrary multidimensional Fourier series. Despite this, we show that we can enlarge the Hilbert space of the circuit by using more qudits or higher local dimensions to meet the degrees of freedom requirements, thus ensuring the universality of the models.
Ding, Y.; Chen, X.; Magdalena-Benedito, R.; Martín-Guerrero, J. D.
Closed-loop control of a noisy qubit with reinforcement learning Bachelor Thesis
2023, ISBN: 2632-2153.
Resumen | Enlaces | BibTeX | Etiquetas: UPV/EHU
@bachelorthesis{nokey,
title = {Closed-loop control of a noisy qubit with reinforcement learning},
author = {Ding, Y. and Chen, X. and Magdalena-Benedito, R. and Martín-Guerrero, J.D.},
url = {https://iopscience.iop.org/article/10.1088/2632-2153/acd048},
doi = {10.1088/2632-2153/acd048},
isbn = {2632-2153},
year = {2023},
date = {2023-05-05},
journal = {Machine Learning: Science and Technology},
volume = {4},
number = {2},
abstract = {The exotic nature of quantum mechanics differentiates machine learning applications in the quantum realm from classical ones. Stream learning is a powerful approach that can be applied to extract knowledge continuously from quantum systems in a wide range of tasks. In this paper, we propose a deep reinforcement learning method that uses streaming data from a continuously measured qubit in the presence of detuning, dephasing, and relaxation. The model receives streaming quantum information for learning and decision-making, providing instant feedback on the quantum system. We also explore the agent's adaptability to other quantum noise patterns through transfer learning. Our protocol offers insights into closed-loop quantum control, potentially advancing the development of quantum technologies.},
keywords = {UPV/EHU},
pubstate = {published},
tppubtype = {bachelorthesis}
}
The exotic nature of quantum mechanics differentiates machine learning applications in the quantum realm from classical ones. Stream learning is a powerful approach that can be applied to extract knowledge continuously from quantum systems in a wide range of tasks. In this paper, we propose a deep reinforcement learning method that uses streaming data from a continuously measured qubit in the presence of detuning, dephasing, and relaxation. The model receives streaming quantum information for learning and decision-making, providing instant feedback on the quantum system. We also explore the agent's adaptability to other quantum noise patterns through transfer learning. Our protocol offers insights into closed-loop quantum control, potentially advancing the development of quantum technologies.
Ding, Y.; Gonzalez-Conde, J.; Lamata, L.; Martín-Guerrero, J. D.; Lizaso, E.; Mugel, S.; Chen, X.; Orús, R.; Solano, E.; Sanz, M.
Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer Artículo de revista
En: Entropy, vol. 25, no 2, pp. 323, 2023, ISBN: 1099-4300.
Resumen | Enlaces | BibTeX | Etiquetas: UPV/EHU
@article{nokey,
title = {Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer},
author = {Ding, Y. and Gonzalez-Conde, J. and Lamata, L. and Martín-Guerrero, J.D. and Lizaso, E. and Mugel, S. and Chen, X. and Orús, R. and Solano, E. and Sanz, M. },
url = {https://www.mdpi.com/1099-4300/25/2/323},
doi = {doi.org/10.3390/e25020323},
isbn = {1099-4300},
year = {2023},
date = {2023-02-10},
journal = {Entropy},
volume = {25},
number = {2},
pages = {323},
abstract = {The prediction of financial crashes in a complex financial network is known to be an NP-hard problem, which means that no known algorithm can efficiently find optimal solutions. We experimentally explore a novel approach to this problem by using a D-Wave quantum annealer, benchmarking its performance for attaining a financial equilibrium. To be specific, the equilibrium condition of a nonlinear financial model is embedded into a higher-order unconstrained binary optimization (HUBO) problem, which is then transformed into a spin-1/2 Hamiltonian with at most, two-qubit interactions. The problem is thus equivalent to finding the ground state of an interacting spin Hamiltonian, which can be approximated with a quantum annealer. The size of the simulation is mainly constrained by the necessity of a large number of physical qubits representing a logical qubit with the correct connectivity. Our experiment paves the way for the codification of this quantitative macroeconomics problem in quantum annealers.},
keywords = {UPV/EHU},
pubstate = {published},
tppubtype = {article}
}
The prediction of financial crashes in a complex financial network is known to be an NP-hard problem, which means that no known algorithm can efficiently find optimal solutions. We experimentally explore a novel approach to this problem by using a D-Wave quantum annealer, benchmarking its performance for attaining a financial equilibrium. To be specific, the equilibrium condition of a nonlinear financial model is embedded into a higher-order unconstrained binary optimization (HUBO) problem, which is then transformed into a spin-1/2 Hamiltonian with at most, two-qubit interactions. The problem is thus equivalent to finding the ground state of an interacting spin Hamiltonian, which can be approximated with a quantum annealer. The size of the simulation is mainly constrained by the necessity of a large number of physical qubits representing a logical qubit with the correct connectivity. Our experiment paves the way for the codification of this quantitative macroeconomics problem in quantum annealers.
S.; Sancho-Lorente Roca-Jerat, T. ; Román-Roche
Circuit Complexity through phase transitions: consequences in quantum state preparation pre-print
2023.
Resumen | Enlaces | BibTeX | Etiquetas: adiabatic algorithms, algorithms, quantia, quantum, quantum computing
@pre-print{nokey,
title = {Circuit Complexity through phase transitions: consequences in quantum state preparation},
author = {Roca-Jerat, S.; Sancho-Lorente, T.; Román-Roche, J.; & Zueco, D. (2023). },
url = {https://quantumspain-project.es/wp-content/uploads/2023/01/Circuit-Complexity-through-phase-transitions_UNIZAR-1.pdf},
doi = { https://doi.org/10.48550/arXiv.2301.04671},
year = {2023},
date = {2023-01-11},
urldate = {2023-01-11},
abstract = {In this paper, we analyze the circuit complexity for preparing ground states of quantum manybody
systems. In particular, how this complexity grows as the ground state approaches a quantum
phase transition. We discuss di erent de nitions of complexity, namely the one following the Fubini-
Study metric or the Nielsen complexity. We also explore di erent models: Ising, ZZXZ or Dicke.
In addition, di erent forms of state preparation are investigated: analytic or exact diagonalization
techniques, adiabatic algorithms (with and without shortcuts), and Quantum Variational Eigensolvers.
We nd that the divergence (or lack thereof) of the complexity near a phase transition depends on
the non-local character of the operations used to reach the ground state. For Fubini-Study based
complexity, we extract the universal properties and their critical exponents.
In practical algorithms, we nd that the complexity depends crucially on whether or not the system
passes close to a quantum critical point when preparing the state. While in the adiabatic case it is
dicult not to cross a critical point when the reference and target states are in di erent phases, for
VQE the algorithm can nd a way to avoid criticality.},
keywords = {adiabatic algorithms, algorithms, quantia, quantum, quantum computing},
pubstate = {published},
tppubtype = {pre-print}
}
In this paper, we analyze the circuit complexity for preparing ground states of quantum manybody
systems. In particular, how this complexity grows as the ground state approaches a quantum
phase transition. We discuss di erent de nitions of complexity, namely the one following the Fubini-
Study metric or the Nielsen complexity. We also explore di erent models: Ising, ZZXZ or Dicke.
In addition, di erent forms of state preparation are investigated: analytic or exact diagonalization
techniques, adiabatic algorithms (with and without shortcuts), and Quantum Variational Eigensolvers.
We nd that the divergence (or lack thereof) of the complexity near a phase transition depends on
the non-local character of the operations used to reach the ground state. For Fubini-Study based
complexity, we extract the universal properties and their critical exponents.
In practical algorithms, we nd that the complexity depends crucially on whether or not the system
passes close to a quantum critical point when preparing the state. While in the adiabatic case it is
dicult not to cross a critical point when the reference and target states are in di erent phases, for
VQE the algorithm can nd a way to avoid criticality.
systems. In particular, how this complexity grows as the ground state approaches a quantum
phase transition. We discuss di erent de nitions of complexity, namely the one following the Fubini-
Study metric or the Nielsen complexity. We also explore di erent models: Ising, ZZXZ or Dicke.
In addition, di erent forms of state preparation are investigated: analytic or exact diagonalization
techniques, adiabatic algorithms (with and without shortcuts), and Quantum Variational Eigensolvers.
We nd that the divergence (or lack thereof) of the complexity near a phase transition depends on
the non-local character of the operations used to reach the ground state. For Fubini-Study based
complexity, we extract the universal properties and their critical exponents.
In practical algorithms, we nd that the complexity depends crucially on whether or not the system
passes close to a quantum critical point when preparing the state. While in the adiabatic case it is
dicult not to cross a critical point when the reference and target states are in di erent phases, for
VQE the algorithm can nd a way to avoid criticality.
Miranda, E. R.; Martín-Guerrero, J. D.; Venkatesh, S.; Hernani-Morales, C.; Lamata, L.; Solano, E.
Quantum Brain Networks: A Perspective Artículo de revista
En: Electronics , vol. 11, no 10, pp. 1528, 2022.
Resumen | Enlaces | BibTeX | Etiquetas: artificial intelligence, quantum computing, UV
@article{nokey,
title = {Quantum Brain Networks: A Perspective},
author = {Miranda, E. R. and Martín-Guerrero, J. D. and Venkatesh, S. and Hernani-Morales, C. and Lamata, L. and Solano, E. },
editor = {Durdu Guney},
url = {https://www.mdpi.com/2079-9292/11/10/1528/htm},
doi = {10.3390/electronics11101528},
year = {2022},
date = {2022-05-11},
urldate = {2022-05-11},
journal = {Electronics },
volume = {11},
number = {10},
pages = {1528},
abstract = {We propose Quantum Brain Networks (QBraiNs) as a new interdisciplinary field integrating knowledge and methods from neurotechnology, artificial intelligence, and quantum computing. The objective is to develop an enhanced connectivity between the human brain and quantum computers for a variety of disruptive applications. We foresee the emergence of hybrid classical-quantum networks of wetware and hardware nodes, mediated by machine learning techniques and brain–machine interfaces. QBraiNs will harness and transform in unprecedented ways arts, science, technologies, and entrepreneurship, in particular activities related to medicine, Internet of Humans, intelligent devices, sensorial experience, gaming, Internet of Things, crypto trading, and business. },
keywords = {artificial intelligence, quantum computing, UV},
pubstate = {published},
tppubtype = {article}
}
We propose Quantum Brain Networks (QBraiNs) as a new interdisciplinary field integrating knowledge and methods from neurotechnology, artificial intelligence, and quantum computing. The objective is to develop an enhanced connectivity between the human brain and quantum computers for a variety of disruptive applications. We foresee the emergence of hybrid classical-quantum networks of wetware and hardware nodes, mediated by machine learning techniques and brain–machine interfaces. QBraiNs will harness and transform in unprecedented ways arts, science, technologies, and entrepreneurship, in particular activities related to medicine, Internet of Humans, intelligent devices, sensorial experience, gaming, Internet of Things, crypto trading, and business.
Dawid, Anna; Arnold, Julian; Requena, Borja; Gresch, Alexander; Płodzień, Marcin; Donatella, Kaelan; Nicoli, Kim; Stornati, Paolo; Koch, Rouven; Büttner, Miriam; Okuła, Robert; Muñoz-Gil, Gorka; Vargas-Hernández, Rodrigo A.; Cervera-Lierta, Alba; Carrasquilla, Juan; Dunjko, Vedran; Gabrié, Marylou; Huembeli, Patrick; van Nieuwenburg, Evert; Vicentini, Filippo; Wang, Lei; Wetzel, Sebastian J.; Carleo, Giuseppe; Greplová, Eliška; Krems, Roman; Marquardt, Florian; Tomza, Michał; Lewenstein, Maciej; Dauphin, Alexandre
Modern applications of machine learning in quantum sciences pre-print
2022.
Resumen | Enlaces | BibTeX | Etiquetas: machine learning, quantic, quantum science, quantumsimulation
@pre-print{nokey,
title = {Modern applications of machine learning in quantum sciences},
author = {Anna Dawid and Julian Arnold and Borja Requena and Alexander Gresch and Marcin Płodzień and Kaelan Donatella and Kim Nicoli and Paolo Stornati and Rouven Koch and Miriam Büttner and Robert Okuła and Gorka Muñoz-Gil and Rodrigo A. Vargas-Hernández and Alba Cervera-Lierta and Juan Carrasquilla and Vedran Dunjko and Marylou Gabrié and Patrick Huembeli and Evert van Nieuwenburg and Filippo Vicentini and Lei Wang and Sebastian J. Wetzel and Giuseppe Carleo and Eliška Greplová and Roman Krems and Florian Marquardt and Michał Tomza and Maciej Lewenstein and Alexandre Dauphin},
url = {https://arxiv.org/abs/2204.04198},
doi = {10.48550/arXiv.2204.04198},
year = {2022},
date = {2022-04-08},
urldate = {2022-04-08},
journal = {Arxiv},
pages = {268},
abstract = {In these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.},
keywords = {machine learning, quantic, quantum science, quantumsimulation},
pubstate = {published},
tppubtype = {pre-print}
}
In these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.
Ding, Y.; Gonzalez-Conde, J.; L. Lamata, Martín-Guerrero; Lizaso, E.; Mugel, S.; Chen, X.; Orús, R.; Solano, E.; Sanz, M.
Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer Artículo de revista
En: Entropy, vol. 25, iss. 2, pp. 323, 0000, ISBN: 1099-4300.
Resumen | Enlaces | BibTeX | Etiquetas: quantum, quantum annealer, UV
@article{nokey,
title = {Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer},
author = {Ding, Y. and Gonzalez-Conde, J. and Lamata, L., Martín-Guerrero, J. D. and Lizaso, E. and Mugel, S. and Chen, X. and Orús, R. and Solano, E. and Sanz, M.
},
url = {https://quantumspain-project.es/wp-content/uploads/2023/05/entropy-25-00323-v2-1.pdf},
doi = {doi.org/10.3390/e25020323},
isbn = {1099-4300},
journal = {Entropy},
volume = {25},
issue = {2},
pages = {323},
abstract = {The prediction of financial crashes in a complex financial network is known to be an NP-hard problem, which means that no known algorithm can efficiently find optimal solutions. We experimentally explore a novel approach to this problem by using a D-Wave quantum annealer, benchmarking its performance for attaining a financial equilibrium. To be specific, the equilibrium condition of a nonlinear financial model is embedded into a higher-order unconstrained binary optimization (HUBO) problem, which is then transformed into a spin-1/2
Hamiltonian with at most, two-qubit interactions. The problem is thus equivalent to finding the ground state of an interacting spin Hamiltonian, which can be approximated with a quantum annealer. The size of the simulation is mainly constrained by the necessity of a large number of physical qubits representing a logical qubit with the correct connectivity. Our experiment paves the way for the codification of this quantitative macroeconomics problem in quantum annealers.},
keywords = {quantum, quantum annealer, UV},
pubstate = {published},
tppubtype = {article}
}
The prediction of financial crashes in a complex financial network is known to be an NP-hard problem, which means that no known algorithm can efficiently find optimal solutions. We experimentally explore a novel approach to this problem by using a D-Wave quantum annealer, benchmarking its performance for attaining a financial equilibrium. To be specific, the equilibrium condition of a nonlinear financial model is embedded into a higher-order unconstrained binary optimization (HUBO) problem, which is then transformed into a spin-1/2
Hamiltonian with at most, two-qubit interactions. The problem is thus equivalent to finding the ground state of an interacting spin Hamiltonian, which can be approximated with a quantum annealer. The size of the simulation is mainly constrained by the necessity of a large number of physical qubits representing a logical qubit with the correct connectivity. Our experiment paves the way for the codification of this quantitative macroeconomics problem in quantum annealers.
Hamiltonian with at most, two-qubit interactions. The problem is thus equivalent to finding the ground state of an interacting spin Hamiltonian, which can be approximated with a quantum annealer. The size of the simulation is mainly constrained by the necessity of a large number of physical qubits representing a logical qubit with the correct connectivity. Our experiment paves the way for the codification of this quantitative macroeconomics problem in quantum annealers.
deMarti iOlius, A.; Etxezarreta Martinez, J.
The closed-branch decoder for quantum LDPC codes Sin publicar
Preprint, 0000.
Resumen | Enlaces | BibTeX | Etiquetas: tecnun
@unpublished{nokey,
title = {The closed-branch decoder for quantum LDPC codes},
author = {deMarti iOlius, A. and Etxezarreta Martinez, J.},
url = {https://arxiv.org/pdf/2402.01532},
doi = {doi.org/10.48550/arXiv.2402.01532},
abstract = {Quantum error correction is the building block for constructing fault-tolerant quantum processors that can operate reliably even if its constituting elements are corrupted by decoherence. In this context, real-time decoding is a necessity for implementing arbitrary quantum computations on the logical level. In this work, we present a new decoder for Quantum Low Density Parity Check (QLDPC) codes, named the closed-branch decoder, with a worst-case complexity loosely upper bounded by O(nmaxgrmaxbr), where maxgr and maxbr are tunable parameters that pose the accuracy versus speed trade-off of decoding algorithms. For the best precision, the maxgrmaxbr product increases exponentially as ∝djd, where d indicates the distance of the code and j indicates the average row weight of its parity check matrix. Nevertheless, we numerically show that considering small values that are polynomials of the code distance are enough for good error correction performance. The decoder is described to great extent and compared with the Belief Propagation Ordered Statistics Decoder (BPOSD) operating over data qubit, phenomenological and circuit-level noise models for the class of Bivariate Bicycle (BB) codes. The results showcase a promising performance of the decoder, obtaining similar results with much lower complexity than BPOSD when considering the smallest distance codes, but experiencing some logical error probability degradation for the larger ones. Ultimately, the performance and complexity of the decoder depends on the product maxgrmaxbr, which can be considered taking into account benefiting one of the two aspects at the expense of the other.},
howpublished = {Preprint},
keywords = {tecnun},
pubstate = {published},
tppubtype = {unpublished}
}
Quantum error correction is the building block for constructing fault-tolerant quantum processors that can operate reliably even if its constituting elements are corrupted by decoherence. In this context, real-time decoding is a necessity for implementing arbitrary quantum computations on the logical level. In this work, we present a new decoder for Quantum Low Density Parity Check (QLDPC) codes, named the closed-branch decoder, with a worst-case complexity loosely upper bounded by O(nmaxgrmaxbr), where maxgr and maxbr are tunable parameters that pose the accuracy versus speed trade-off of decoding algorithms. For the best precision, the maxgrmaxbr product increases exponentially as ∝djd, where d indicates the distance of the code and j indicates the average row weight of its parity check matrix. Nevertheless, we numerically show that considering small values that are polynomials of the code distance are enough for good error correction performance. The decoder is described to great extent and compared with the Belief Propagation Ordered Statistics Decoder (BPOSD) operating over data qubit, phenomenological and circuit-level noise models for the class of Bivariate Bicycle (BB) codes. The results showcase a promising performance of the decoder, obtaining similar results with much lower complexity than BPOSD when considering the smallest distance codes, but experiencing some logical error probability degradation for the larger ones. Ultimately, the performance and complexity of the decoder depends on the product maxgrmaxbr, which can be considered taking into account benefiting one of the two aspects at the expense of the other.
Mehrabankar, S.; García-March, M. A.; Almudéver, C. G.; Pérez, A.
Reducing the number of qubits in quantum simulations of one dimensional many-body Hamiltonians Sin publicar
Preprint, 0000.
Resumen | Enlaces | BibTeX | Etiquetas: UV
@unpublished{nokey,
title = {Reducing the number of qubits in quantum simulations of one dimensional many-body Hamiltonians},
author = {Mehrabankar, S. and García-March, M.A. and Almudéver, C.G. and Pérez, A. },
url = {https://arxiv.org/abs/2308.01545},
doi = {doi.org/10.48550/arXiv.2308.01545},
abstract = {We investigate the Ising and Heisenberg models using the Block Renormalization Group Method (BRGM), focusing on its behavior across different system sizes. The BRGM reduces the number of spins by a factor of 1/2 (1/3) for the Ising (Heisenberg) model, effectively preserving essential physical features of the model while using only a fraction of the spins. Through a comparative analysis, we demonstrate that as the system size increases, there is an exponential convergence between results obtained from the original and renormalized Ising Hamiltonians, provided the coupling constants are redefined accordingly. Remarkably, for a spin chain with 24 spins, all physical features, including magnetization, correlation function, and entanglement entropy, exhibit an exact correspondence with the results from the original Hamiltonian. The study of the Heisenberg model also shows this tendency, although complete convergence may appear for a size much larger than 24 spins, and is therefore beyond our computational capabilities. The success of BRGM in accurately characterizing the Ising model, even with a relatively small number of spins, underscores its robustness and utility in studying complex physical systems, and facilitates its simulation on current NISQ computers, where the available number of qubits is largely constrained.},
howpublished = {Preprint},
keywords = {UV},
pubstate = {published},
tppubtype = {unpublished}
}
We investigate the Ising and Heisenberg models using the Block Renormalization Group Method (BRGM), focusing on its behavior across different system sizes. The BRGM reduces the number of spins by a factor of 1/2 (1/3) for the Ising (Heisenberg) model, effectively preserving essential physical features of the model while using only a fraction of the spins. Through a comparative analysis, we demonstrate that as the system size increases, there is an exponential convergence between results obtained from the original and renormalized Ising Hamiltonians, provided the coupling constants are redefined accordingly. Remarkably, for a spin chain with 24 spins, all physical features, including magnetization, correlation function, and entanglement entropy, exhibit an exact correspondence with the results from the original Hamiltonian. The study of the Heisenberg model also shows this tendency, although complete convergence may appear for a size much larger than 24 spins, and is therefore beyond our computational capabilities. The success of BRGM in accurately characterizing the Ising model, even with a relatively small number of spins, underscores its robustness and utility in studying complex physical systems, and facilitates its simulation on current NISQ computers, where the available number of qubits is largely constrained.
Palacios, A.; Martínez-Pena, R.; Soriano, M. C.; Giorgi, G. L.; Zambrini, R.
Role of coherence in many-body Quantum Reservoir Computing Sin publicar
Preprint, 0000.
Resumen | Enlaces | BibTeX | Etiquetas: uib
@unpublished{nokey,
title = {Role of coherence in many-body Quantum Reservoir Computing},
author = {Palacios, A. and Martínez-Pena, R. and Soriano, M.C. and Giorgi, G.L. and Zambrini, R. },
url = {https://arxiv.org/pdf/2409.17734},
doi = {doi.org/10.48550/arXiv.2409.17734},
abstract = {Quantum Reservoir Computing (QRC) offers potential advantages over classical reservoir computing, including inherent processing of quantum inputs and a vast Hilbert space for state exploration. Yet, the relation between the performance of reservoirs based on complex and many-body quantum systems and non-classical state features is not established. Through an extensive analysis of QRC based on a transverse-field Ising model we show how different quantum effects, such as quantum
coherence and correlations, contribute to improving the performance in temporal tasks, as measured by the Information Processing Capacity. Additionally, we critically assess the impact of finite measurement resources and noise on the reservoir’s dynamics in different regimes, quantifying the limited ability to exploit quantum effects for increasing damping and noise strengths. Our results reveal a monotonic relationship between reservoir performance and coherence, along with the importance of quantum effects in the ergodic regime.},
howpublished = {Preprint},
keywords = {uib},
pubstate = {published},
tppubtype = {unpublished}
}
Quantum Reservoir Computing (QRC) offers potential advantages over classical reservoir computing, including inherent processing of quantum inputs and a vast Hilbert space for state exploration. Yet, the relation between the performance of reservoirs based on complex and many-body quantum systems and non-classical state features is not established. Through an extensive analysis of QRC based on a transverse-field Ising model we show how different quantum effects, such as quantum
coherence and correlations, contribute to improving the performance in temporal tasks, as measured by the Information Processing Capacity. Additionally, we critically assess the impact of finite measurement resources and noise on the reservoir’s dynamics in different regimes, quantifying the limited ability to exploit quantum effects for increasing damping and noise strengths. Our results reveal a monotonic relationship between reservoir performance and coherence, along with the importance of quantum effects in the ergodic regime.
coherence and correlations, contribute to improving the performance in temporal tasks, as measured by the Information Processing Capacity. Additionally, we critically assess the impact of finite measurement resources and noise on the reservoir’s dynamics in different regimes, quantifying the limited ability to exploit quantum effects for increasing damping and noise strengths. Our results reveal a monotonic relationship between reservoir performance and coherence, along with the importance of quantum effects in the ergodic regime.
