Descubriendo el potencial de la computación cuántica
Conocimiento al servicio de la ciencia y la innovación
Del desarrollo a la aplicación
Encuentra las últimas publicaciones disponibles para cada una de nuestras actividades
Arquitecturas de simulación cuántica en sistemas HPC
IA cuántica para aplicaciones científicas e industriales
IA cuántica para la caracterización del ruido de los ordenadores cuánticos
Algoritmos de aprendizaje automático de inspiración cuántica y sus aplicaciones en la privacidad
Algoritmos de cadenas de Markov cuánticas
Computación cuántica analógico-digital
Algoritmos genéticos cuánticos, variacionales y adiabáticos y sus aplicaciones
Aplicaciones de la IA cuántica a la química cuántica, ciencia de materiales y finanzas
“Quantum reservoir computing” para aprendizaje automático
Algoritmos de corrección cuántica de errores e IA
Modelos de clasificación y generativos cuánticos aplicados a física de partículas y medicina
Análisis de complejidad computacional de algoritmos cuánticos e IA para analizar procesos cuánticos
Optimización con algoritmos de templado cuántico (“quantum annealing”)
Técnicas de IA y quantum machine learning para la identificación de procesos cuánticos
Desarrollo de software de control de simuladores cuánticos para quantum machine learning
Desarrollo y certificación de algoritmos cuánticos en hardware NISQ
Redes neuronales cuánticas y sus aplicaciones al aprendizaje de procesos cuánticos
Algoritmos cuánticos y de inspiración cuántica para problemas matemáticos complejos
Desarrollo de algoritmos y tecnología de control para simuladores cuánticos
Estudio pormenorizado de las arquitecturas cuánticas y sus desafíos de software y hardware
El trayecto de Quantum Spain
Lee los logros más recientes del proyecto
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
10.09.2023.
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
08.09.2023.
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.
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.
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 Pre-print
11.07.2023.
Resumen | Enlaces | BibTeX | Etiquetas: quantic
@pre-print{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://quantumspain-project.es/wp-content/uploads/2023/08/Quantum-entanglement-patterns-in-the-structure-of-atomic-nuclei-within-the-nuclear-shell-model.pdf},
doi = {doi.org/10.48550/arXiv.2307.05197},
year = {2023},
date = {2023-07-11},
urldate = {2023-07-11},
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. 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. This analysis provides a guide for designing more efficient quantum algorithms for the noisy intermediate-scale quantum era.},
keywords = {quantic},
pubstate = {published},
tppubtype = {pre-print}
}
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. 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. This analysis provides a guide for designing more efficient quantum algorithms for the noisy intermediate-scale quantum era.
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.
S.; Sancho-Lorente Roca-Jerat, T. ; Román-Roche
Circuit Complexity through phase transitions: consequences in quantum state preparation Pre-print
11.01.2023.
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
08.04.2022.
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.
