Application
Quantum AI for scientific and industrial applications
QUANTIA
Group Description:
The group is made up of the following staff researchers: David Íñiguez Dieste, Alfonso Tarancón Lafita, Manuel Asorey Carballeira, Fernando Falceto Blecua, Jose Garcia Esteve, David Zueco Lainez and other scientific collaborators: Yisely Martinez Perez, Fernando Ezquerro Sastre.
QUANTIA explores real applications of quantum technologies. Our mission is to bring quantum energy closer to Spanish industry. To do this, we research and develop algorithms to solve various problems such as material and chemical compound simulators, optimization, recommendation or classification.
Activity description:
The QUANTIA group of Universidad de Zaragoza is exploring applications of quantum optimization in contexts, both of commercial and scientific interests. For such goals it carries out comparative studies of quantum algorithms of optimization (variational algorithms NISQ, quantum annealers, and qudit type molecular processors) for posterior implementation in model problems like portfolios optimisation. We also analyse non-supervised quantum machine learning algorithms, both of clustering and recommendation, acting on classic data, and quantum algorithms for the classification of quantum states of matter.
Results
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.
Abstract | Links | BibTeX | Tags: 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}
}
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.
Abstract | Links | BibTeX | Tags: 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}
}
Miranda, E. R.; Martín-Guerrero, J. D.; Venkatesh, S.; Hernani-Morales, C.; Lamata, L.; Solano, E.
Quantum Brain Networks: A Perspective Journal Article
In: Electronics , vol. 11, no. 10, pp. 1528, 2022.
Abstract | Links | BibTeX | Tags: 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}
}
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 Journal Article
In: Entropy, vol. 25, iss. 2, pp. 323, 0000, ISBN: 1099-4300.
Abstract | Links | BibTeX | Tags: 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}
}
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.