Aplicación

@article{nokey,

title = {Neural quantum kernels: Training quantum kernels with quantum neural networks},

author = {Rodriguez-Grasa, P. and Ban, Y. and Sanz, M.},

url = {https://journals.aps.org/prresearch/abstract/10.1103/xphb-x2g4},

doi = {doi.org/10.1103/xphb-x2g4},

year  = {2025},

date = {2025-06-16},

urldate = {2025-06-16},

journal = {Physical Review Research},

volume = {7},

number = {23269 },

issue = {2},

abstract = {Quantum and classical machine learning have been naturally connected through kernel methods, which have also served as proof-of-concept for quantum advantage. Quantum embeddings encode classical data into quantum feature states, enabling the construction of embedding quantum kernels (EQKs) by measuring vector similarities and projected quantum kernels (PQKs) through projections of these states. However, in both approaches, the model is influenced by the choice of the embedding. In this work, we propose using the training of a quantum neural network (QNN) to construct neural quantum kernels, specifically neural EQKs and neural PQKs—problem-inspired kernel functions. Unlike previous approaches, our method requires the kernel matrix to be constructed only once, significantly reducing computational overhead. To achieve this, we introduce a scalable training method for an 𝑛-qubit data reuploading QNN. Furthermore, we demonstrate neural quantum kernels can alleviate exponential concentration and enhance generalization capabilities compared to problem-agnostic kernels, positioning them as a scalable and robust solution for quantum machine learning applications.},

keywords = {UPV/EHU},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Quantum Carleman linearization efficiency in nonlinear fluid dynamics},

author = {Gonzalez-Conde, J. and Lewis, D. and Bharadwaj, S.S. and Sanz, M. },

url = {https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.7.023254},

doi = {doi.org/10.1103/PhysRevResearch.7.023254},

year  = {2025},

date = {2025-06-12},

urldate = {2025-06-12},

journal = {Physical Review Research},

volume = {7},

number = {23254},

issue = {2},

abstract = {Computational fluid dynamics (CFD) is a specialized branch of fluid mechanics that utilizes numerical methods and algorithms to solve and analyze fluid-flow problems. One promising avenue to enhance CFD is the use of quantum computing, which has the potential to resolve nonlinear differential equations more efficiently than classical computers. Here, we try to answer the question of which regimes of nonlinear partial differential equations for fluid dynamics can have an efficient quantum algorithm. We propose a connection between the numerical parameter 𝑅, which guarantees efficiency in the truncation of the Carleman linearization, and the physical parameters that describe the fluid flow. This link can be made thanks to the Kolmogorov scale, which determines the minimum size of the grid needed to properly resolve the energy cascade induced by the nonlinear term. Additionally, we introduce the formalism for vector field simulation in different spatial dimensions, providing the discretization of the operators and the boundary conditions.},

keywords = {UPV/EHU},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Scrambling in the charging of quantum batteries},

author = {Romero, S. Ding, Y. and Chen, Xi. and Ban, Y.},

url = {https://link.springer.com/content/pdf/10.1007/JHEP05(2025)021.pdf},

doi = {doi.org/10.1007/JHEP05(2025)021},

year  = {2025},

date = {2025-05-05},

journal = {High Energy Physics},

volume = {2025},

number = {21},

abstract = {Exponentially fast scrambling of an initial state characterizes quantum chaotic systems. Given the importance of quickly populating higher energy levels from low-energy states in quantum battery charging protocols, this work investigates the role of quantum scrambling in quantum batteries and its effect on optimal power and charging times by means of the Sachdev-Ye-Kitaev model, a maximally-chaotic black hole physics model that has been recently proposed as a quantum battery. We adopt a bare representation with normalized bandwidths to suppress system energy dependence. To our knowledge, this is the first in-depth exploration of quantum scrambling in the context of quantum batteries. By analyzing the dynamics of out-of-time-order correlators, our findings indicate that quantum scrambling does not necessarily lead to faster charging, despite its potential for accelerating the process.},

keywords = {UPV/EHU},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Existence of unbiased estimators in discrete quantum systems},

author = {Navarro, J. and Ravell Rodríguez, R. and Sanz, M. },

url = {https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.7.023060},

doi = {doi.org/10.1103/PhysRevResearch.7.023060},

year  = {2025},

date = {2025-04-16},

journal = {Physical Review Research},

volume = {7},

abstract = {The Cramér-Rao bound serves as a crucial lower limit for the mean square error of an estimator in frequentist parameter estimation. Paradoxically, it requires highly accurate prior knowledge of the estimated parameter for constructing the optimal unbiased estimator. In contrast, Bhattacharyya bounds offer a more robust estimation framework with respect to prior accuracy by introducing additional constraints on the estimator. In this work, we examine divergences that arise in the computation of these bounds and establish the conditions under which they remain valid. Notably, we show that when the number of constraints exceeds the number of measurement outcomes, an estimator with finite variance typically does not exist. Furthermore, we systematically investigate the properties of these bounds using paradigmatic examples, comparing them to the Cramér-Rao and Bayesian approaches.},

keywords = {UPV/EHU},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Entanglement transfer during quantum frequency conversion in gas-filled hollow-core fibers},

author = {Gonzalez-Raya, T. and Mena, A. and Lazo, M. and Leggio L. and Novoa, D. and Sanz, M. },

url = {https://pubs.aip.org/aip/app/article/10/4/041302/3341958/Entanglement-transfer-during-quantum-frequency},

doi = {doi.org/10.1063/5.0246782},

year  = {2025},

date = {2025-04-02},

journal = {APL Photonics},

volume = {10},

abstract = {Quantum transduction is essential for the future hybrid quantum networks, connecting devices across different spectral ranges. In this regard, molecular modulation in hollow-core fibers has proven to be exceptional for efficient and tunable frequency conversion of arbitrary light fields down to the single-photon limit. However, insights into this conversion method for quantum light have remained elusive beyond standard semi-classical models. In this Letter, we employ a quantum Hamiltonian framework to characterize the behavior of entanglement during molecular modulation while describing the quantum dynamics of both molecules and photons in agreement with recent experiments. In particular, apart from obtaining analytical expressions for the final opto-molecular states, our model predicts a close correlation between the evolution of the average photon numbers and the transfer of entanglement between the interacting parties. These results will contribute to the development of new fiber-based strategies to tackle the challenges associated with the upcoming generation of lightwave quantum technologies.},

keywords = {UPV/EHU},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Ring-exchange physics in a chain of three-level ions},

author = {Biswas, S. and Rico, E. and Grass, T. },

url = {https://quantum-journal.org/papers/q-2025-04-01-1683/},

doi = {doi.org/10.22331/q-2025-04-01-1683},

year  = {2025},

date = {2025-04-01},

journal = {Quantum },

abstract = {In the presence of ring exchange interactions, bosons in a ladder-like lattice may form the bosonic analogon of a correlated metal, known as the d-wave Bose liquid (DBL). In this paper, we show that a chain of trapped ions with three internal levels can mimic a ladder-like system constrained to a maximum occupation of one boson per rung. The setup enables tunable ring exchange interactions, transitioning between a polarized regime with all bosons confined to one leg and the DBL regime. The latter state is characterized by a splitting of the peak in the momentum distribution and an oscillating pair correlation function.},

keywords = {UPV/EHU},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Quantum approximated cloning-assisted density matrix exponentiation},

author = {Rodriguez-Grasa, P. and Ibarrondo, R. and Gonzalez-Conde, J. and Ban, Y. and Rebentrost, P. and Sanz, M. },

url = {https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.7.013264},

doi = {doi.org/10.1103/PhysRevResearch.7.013264},

year  = {2025},

date = {2025-03-12},

journal = {Physical Review Research},

volume = {7},

abstract = {Classical information loading is an essential task for many processing quantum algorithms, constituting a cornerstone in the field of quantum machine learning. In particular, the embedding techniques based on Hamiltonian simulation techniques enable the loading of matrices into quantum computers. A representative example of these methods is the Lloyd-Mohseni-Rebentrost (LMR) protocol, which efficiently implements matrix exponentiation when multiple copies of a quantum state are available. However, this is a quite ideal setup, and in a realistic scenario, the copies are limited and the noncloning theorem prevents one from producing more exact copies in order to increase the accuracy of the protocol. Here, we propose a method to circumvent this limitation by introducing imperfect quantum copies, which significantly improve the performance of the LMR when the eigenvectors are known.},

keywords = {UPV/EHU},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Satellite image classification with neural quantum kernels},

author = {Rodriguez-Grasa, P. and Farzan-Rodríguez, R. and Novelli, G. and Ban, Y. and Sanz, M. },

url = {https://iopscience.iop.org/article/10.1088/2632-2153/ada86c/pdf},

doi = {10.1088/2632-2153/ada86c},

year  = {2025},

date = {2025-02-18},

journal = {Machine Learning: Science and Technology},

volume = {6},

number = {1},

abstract = {Achieving practical applications of quantum machine learning (QML) for real-world scenarios

remains challenging despite significant theoretical progress. This paper proposes a novel approach

for classifying satellite images, a task of particular relevance to the earth observation industry, using

QML techniques. Specifically, we focus on classifying images that contain solar panels, addressing a

complex real-world classification problem. Our approach begins with classical pre-processing to

reduce the dimensionality of the satellite image dataset. We then apply neural quantum

kernels-quantum kernels derived from trained quantum neural networks-for classification. We

evaluate several strategies within this framework, demonstrating results that are competitive with

the best classical methods. Key findings include the robustness of or results and their scalability,

with successful performance achieved up to 8 qubits.

},

keywords = {UPV/EHU},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Exploring ground states of Fermi-Hubbard model on honeycomb lattices with counterdiabaticity},

author = {Tang, J. and Xu, R. and Ding, Y. and Xu, X. and Ban, Y. and Yung, M.H. and Pérez-Obiol, A. and Platero, G. Chen, Xi.},

url = {https://www.nature.com/articles/s41535-024-00697-5},

doi = {doi.org/10.1038/s41535-024-00697-5},

year  = {2024},

date = {2024-11-07},

journal = {Quantum Materials },

volume = {9},

number = {87},

abstract = {Exploring the ground state properties of many-body quantum systems conventionally involves adiabatic processes, alongside exact diagonalization, in the context of quantum annealing or adiabatic quantum computation. Shortcuts to adiabaticity by counter-diabatic driving serve to accelerate these processes by suppressing energy excitations. Motivated by this, we develop variational quantum algorithms incorporating the auxiliary counter-diabatic interactions, comparing them with digitized adiabatic algorithms. These algorithms are then implemented on gate-based quantum circuits to explore the ground states of the Fermi-Hubbard model on honeycomb lattices, utilizing systems with up to 26 qubits. The comparison reveals that the counter-diabatic inspired ansatz is superior to traditional Hamiltonian variational ansatz. Furthermore, the number and duration of Trotter steps are analyzed to understand and mitigate errors. Given the model’s relevance to materials in condensed matter, our study paves the way for using variational quantum algorithms with counterdiabaticity to explore quantum materials in the noisy intermediate-scale quantum era.},

keywords = {UPV/EHU},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@article{nokey,

title = {Closed-loop control of a noisy qubit with reinforcement learning},

author = {Ding, Y. and Chen, Xi. 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},

urldate = {2023-05-05},

journal = {Machine Learning: Science and Technology},

volume = {4},

issue = {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 = {article}

}

@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}

}