Aplicación
IA cuántica para la caracterización del ruido de los ordenadores cuánticos
Grupo de ordenadores cuánticos y simulaciones cuánticas de la Universitat de València
Descripción del grupo:
El grupo está conformado por Armando Pérez (catedrático de Universidad), Germán Rodrigo (Investigador Científico CSIC), Manuel Gessner (Investigador Ramón y Cajal), Leandro Cieri y Bryan Zaldívar (Investigadores Distinguidos CIDEGENT), Somayeh Mehrabankar y Selomit Ramírez (Investigadoras Postdoctorales), German Sborlini y Luiz Vale Silva (Investigadores MSCA), Andreu Anglés, Rafael Gómez, Jorge Martínez de Lejarza, Andrés Rentería y David Rentería (estudiantes de doctorado). Dentro de sus líneas de investigación se encuentran: ordenadores cuánticos, sistemas cuánticos abiertos, algoritmos cuánticos y simulaciones de sistemas cuánticos, entrelazamiento cuántico, metrología cuántica y aprendizaje cuántico de máquina.
Descripción de la actividad:
El grupo de Información cuántica y computación cuántica de la Universitat de València estudia y caracteriza el ruido en ordenadores cuánticos usando técnicas de IA. En concreto, se estudian técnicas de mitigación del ruido en este tipo de ordenadores, ya sea en entornos Markovianos o no Markovianos. Se investiga en la simulación de sistemas físicos cuánticos de muchos cuerpos (modelo de Ising, modelo XX y otros) mediante ordenadores cuánticos. Otras actividades son la investigación del entrelazamiento cuántico, metrología cuántica y aprendizaje cuántico de máquina. Además, está especializado en el desarrollo y aplicación de algoritmos cuánticos en física de partículas elementales y teoría cuántica de campos, como por ejemplo métodos de clusterización cuánticos, integradores Monte Carlo cuánticos y caracterización causal de diagramas de Feynman multiloop.
Grupo IDAL
Descripción del grupo:
El grupo está conformado por José D. Martín Guerrero. Catedràtic d’Universitat. Intelligent Data Analysis Laboratory (IDAL), ETSE-UV; J. Rafael Magdalena Benedicto. Professor Titular d’Universitat. Intelligent Data Analysis Laboratory (IDAL), ETSE-UV; Carlos Hernani Morales. Investigador no doctor. Intelligent Data Analysis Laboratory (IDAL), ETSE-UV y Antonio Ferrer Sánchez. Investigador no doctor. Intelligent Data Analysis Laboratory (IDAL), ETSE-UV.
Descripción de la actividad:
Investigación en:
– Quantum Machine Learning
– Machine Learning clàssic per a la descripció i intepretació de fenomenologia quàntica
Resultados
Simen, A.; Flores-Garrigos, C.; Hegade, N. N.; Montalban, I.; Vives-Gilabert, Y.; Michon, E.; Zhang, Q.; Solano, E.; J.D. 2 Martín-Guerrero, 3
Digital-analog quantum convolutional neural networks for image classification Artículo de revista
En: Physical Review Research, vol. 6, iss. 4, pp. 2060, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: UV
@article{nokey,
title = {Digital-analog quantum convolutional neural networks for image classification},
author = {Simen, A. and Flores-Garrigos, C. and Hegade, N.N. and Montalban, I. and Vives-Gilabert, Y. and Michon, E. and Zhang, Q. and Solano, E. and Martín-Guerrero, J.D.
2,3,§},
url = {https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.L042060},
doi = {doi.org/10.1103/PhysRevResearch.6.L042060},
year = {2024},
date = {2024-12-11},
journal = {Physical Review Research},
volume = {6},
issue = {4},
pages = {2060},
abstract = {We propose digital-analog quantum kernels for enhancing the detection of complex features in the classification of images. We consider multipartite-entangled analog blocks, stemming from native Ising interactions in neutral-atom quantum processors, and individual operations as digital steps to implement the protocol. To further improve the detection of complex features, we apply multiple quantum kernels by varying the qubit connectivity according to the hardware constraints. An architecture that combines nontrainable quantum kernels and standard convolutional neural networks is used to classify realistic medical images, from breast cancer and pneumonia diseases, with a significantly reduced number of parameters. Despite this fact, the model exhibits better performance than its classical counterparts and achieves comparable metrics according to public benchmarks. These findings highlight the potential of digital-analog quantum convolutions in extracting complex and meaningful features from images, positioning them as a candidate model for addressing challenging classification problems.},
keywords = {UV},
pubstate = {published},
tppubtype = {article}
}
We propose digital-analog quantum kernels for enhancing the detection of complex features in the classification of images. We consider multipartite-entangled analog blocks, stemming from native Ising interactions in neutral-atom quantum processors, and individual operations as digital steps to implement the protocol. To further improve the detection of complex features, we apply multiple quantum kernels by varying the qubit connectivity according to the hardware constraints. An architecture that combines nontrainable quantum kernels and standard convolutional neural networks is used to classify realistic medical images, from breast cancer and pneumonia diseases, with a significantly reduced number of parameters. Despite this fact, the model exhibits better performance than its classical counterparts and achieves comparable metrics according to public benchmarks. These findings highlight the potential of digital-analog quantum convolutions in extracting complex and meaningful features from images, positioning them as a candidate model for addressing challenging classification problems.
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.
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.
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 Working paper
2024.
Resumen | Enlaces | BibTeX | Etiquetas: UV
@workingpaper{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.2505.03642},
year = {2024},
date = {2024-07-10},
urldate = {2024-07-10},
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.},
keywords = {UV},
pubstate = {published},
tppubtype = {workingpaper}
}
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.
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 Artículo de revista
En: Machine Learning: Science and Technology, vol. 5, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: UV
@article{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://iopscience.iop.org/article/10.1088/2632-2153/ad450f},
doi = {10.1088/2632-2153/ad450f},
year = {2024},
date = {2024-05-09},
urldate = {2023-09-08},
journal = {Machine Learning: Science and Technology},
volume = {5},
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 = {article}
}
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.
Ferrer-Sánchez, A.; J.D. Ruiz de Austri-Bazan Martín-Guerrero, R.; Torres-Forné, A.; Font, J. A.
Gradient-annihilated PINNs for solving Riemann problems: Application to relativistic hydrodynamics Artículo de revista
En: Computer Methods in Applied Mechanics and Engineering, vol. 424, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: UV
@article{nokey,
title = {Gradient-annihilated PINNs for solving Riemann problems: Application to relativistic hydrodynamics},
author = {Ferrer-Sánchez, A. and Martín-Guerrero, J.D. Ruiz de Austri-Bazan, R. and Torres-Forné, A. and Font, J.A. },
url = {https://www.sciencedirect.com/science/article/pii/S0045782524001622?via%3Dihub},
doi = {doi.org/10.1016/j.cma.2024.116906},
year = {2024},
date = {2024-05-01},
journal = {Computer Methods in Applied Mechanics and Engineering},
volume = {424},
abstract = {We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for solving systems of partial differential equations admitting discontinuous solutions. Our method, called Gradient-Annihilated PINNs (GA-PINNs), introduces a modified loss function that forces the model to partially ignore high-gradients in the physical variables, achieved by introducing a suitable weighting function. The method relies on a set of hyperparameters that control how gradients are treated in the physical loss. The performance of our methodology is demonstrated by solving Riemann problems in special relativistic hydrodynamics, extending earlier studies with PINNs in the context of the classical Euler equations. The solutions obtained with the GA-PINN model correctly describe the propagation speeds of discontinuities and sharply capture the associated jumps. We use the relative 𝓁² error to compare our results with the exact solution of special relativistic Riemann problems, used as the reference “ground truth”, and with the corresponding error obtained with a second-order, central, shock-capturing scheme. In all problems investigated, the accuracy reached by the GA-PINN model is comparable to that obtained with a shock-capturing scheme, achieving a performance superior to that of the baseline PINN algorithm in general. An additional benefit worth stressing is that our PINN-based approach sidesteps the costly recovery of the primitive variables from the state vector of conserved variables, a well-known drawback of grid-based solutions of the relativistic hydrodynamics equations. Due to its inherent generality and its ability to handle steep gradients, the GA-PINN methodology discussed in this paper could be a valuable tool to model relativistic flows in astrophysics and particle physics, characterized by the prevalence of discontinuous solutions.},
keywords = {UV},
pubstate = {published},
tppubtype = {article}
}
We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for solving systems of partial differential equations admitting discontinuous solutions. Our method, called Gradient-Annihilated PINNs (GA-PINNs), introduces a modified loss function that forces the model to partially ignore high-gradients in the physical variables, achieved by introducing a suitable weighting function. The method relies on a set of hyperparameters that control how gradients are treated in the physical loss. The performance of our methodology is demonstrated by solving Riemann problems in special relativistic hydrodynamics, extending earlier studies with PINNs in the context of the classical Euler equations. The solutions obtained with the GA-PINN model correctly describe the propagation speeds of discontinuities and sharply capture the associated jumps. We use the relative 𝓁² error to compare our results with the exact solution of special relativistic Riemann problems, used as the reference “ground truth”, and with the corresponding error obtained with a second-order, central, shock-capturing scheme. In all problems investigated, the accuracy reached by the GA-PINN model is comparable to that obtained with a shock-capturing scheme, achieving a performance superior to that of the baseline PINN algorithm in general. An additional benefit worth stressing is that our PINN-based approach sidesteps the costly recovery of the primitive variables from the state vector of conserved variables, a well-known drawback of grid-based solutions of the relativistic hydrodynamics equations. Due to its inherent generality and its ability to handle steep gradients, the GA-PINN methodology discussed in this paper could be a valuable tool to model relativistic flows in astrophysics and particle physics, characterized by the prevalence of discontinuous solutions.
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.
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.
Ding, Y.; Martín-Guerrero, J. D.; Vives-Gilabert, Y.; Chen, X.
Active Learning in Physics: From 101, to Progress, and Perspective Artículo de revista
En: Advanced Quantum Technologies, vol. 6, 2023.
Resumen | Enlaces | BibTeX | Etiquetas: UV
@article{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://advanced.onlinelibrary.wiley.com/doi/full/10.1002/qute.202300208},
doi = {doi.org/10.1002/qute.202300208},
year = {2023},
date = {2023-10-24},
urldate = {2023-11-30},
journal = {Advanced Quantum Technologies},
volume = {6},
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 = {article}
}
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.
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).
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 Artículo de revista
En: Advanced Quantum Technologies, 2023.
Resumen | Enlaces | BibTeX | Etiquetas: UV
@article{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://advanced.onlinelibrary.wiley.com/doi/10.1002/qute.202300294},
doi = {doi.org/10.1002/qute.202300294},
year = {2023},
date = {2023-04-14},
urldate = {2023-09-10},
journal = {Advanced Quantum Technologies},
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 = {UV},
pubstate = {published},
tppubtype = {article}
}
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.
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: 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 = {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.
