Aplicación
Computación cuántica analógico-digital
Tecnologías Cuánticas y Aprendizaje Cuántico de Máquinas
Descripción del grupo:
El interés de la Universidad de Sevilla (US) en la computación cuántica radica en contribuir al conocimiento fundamental en un área puntera como esta y en particular en el aprendizaje cuántico de máquinas, que podría tener implicaciones no sólo científicas, sino también para el conjunto de la sociedad. El investigador principal Lucas Lamata es uno de los mayores expertos en la computación cuántica digital-analógica y ha empleado los últimos años de investigación en desarrollarla y aplicarla a diferentes problemas. En el marco de Quantum Spain, la US estudiará el uso de este paradigma computacional para el machine learning cuántico y lo aplicará a la resolución de varios problemas.
Descripción de la actividad:
La computación cuántica analógico-digital consiste en combinar operaciones elementales (puertas cuánticas) como las empleadas en los ordenadores cuánticos actuales, con períodos en los que el sistema de qubits evoluciona según sus interacciones naturales, tal vez con la mediación de un ligero control externo “entrenado”. En esta actividad, se abordarán las siguientes tareas en este contexto:
1. Analizar cómo se pueden mejorar los algoritmos de aprendizaje de máquinas cuánticos, en particular de aprendizaje de refuerzo cuántico con el nuevo paradigma de algoritmos cuánticos analógico-digitales.
2. Estudiar la implementación de estos algoritmos en plataformas de circuitos superconductores, iones atrapados y fotónica cuántica.
Resultados
Olivera-Atencio, M. L.; Lamata, L.; Casado-Pascual, J.
Impact of amplitude and phase damping noise on quantum reinforcement learning: challenges and opportunities Artículo de revista
En: The European Physical Journal Special Topics (EPJ ST), 2025.
Resumen | Enlaces | BibTeX | Etiquetas: US
@article{nokey,
title = {Impact of amplitude and phase damping noise on quantum reinforcement learning: challenges and opportunities},
author = {Olivera-Atencio, M.L. and Lamata, L. and Casado-Pascual, J.},
editor = {Olivera-Atencio, M.L. and Lamata, L. and Casado-Pascual, J. },
url = {https://link.springer.com/article/10.1140/epjs/s11734-025-01760-3},
doi = {doi.org/10.1140/epjs/s11734-025-01760-3},
year = {2025},
date = {2025-07-04},
urldate = {2025-07-04},
journal = {The European Physical Journal Special Topics (EPJ ST)},
abstract = {Quantum machine learning (QML) is an emerging field with significant potential, yet it remains highly susceptible to noise, which poses a major challenge to its practical implementation. While various noise mitigation strategies have been proposed to enhance algorithmic performance, the impact of noise is not fully understood. In this work, we investigate the effects of amplitude and phase damping noise on a quantum reinforcement learning algorithm. Through analytical and numerical analysis, we assess how these noise sources influence the learning process and overall performance. Our findings contribute to a deeper understanding of the role of noise in quantum learning algorithms and suggest that, rather than being purely detrimental, unavoidable noise may present opportunities to enhance QML processes.},
keywords = {US},
pubstate = {published},
tppubtype = {article}
}
Quantum machine learning (QML) is an emerging field with significant potential, yet it remains highly susceptible to noise, which poses a major challenge to its practical implementation. While various noise mitigation strategies have been proposed to enhance algorithmic performance, the impact of noise is not fully understood. In this work, we investigate the effects of amplitude and phase damping noise on a quantum reinforcement learning algorithm. Through analytical and numerical analysis, we assess how these noise sources influence the learning process and overall performance. Our findings contribute to a deeper understanding of the role of noise in quantum learning algorithms and suggest that, rather than being purely detrimental, unavoidable noise may present opportunities to enhance QML processes.
Lamata, L.
Digital-Analog Quantum Machine Learning Artículo de revista
En: Advanced Intelligent Discovery, vol. 1, iss. 1, 2025.
Resumen | Enlaces | BibTeX | Etiquetas: US
@article{nokey,
title = {Digital-Analog Quantum Machine Learning},
author = {Lamata, L.},
url = {https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202400023},
doi = {doi.org/10.1002/aidi.202400023},
year = {2025},
date = {2025-02-13},
journal = {Advanced Intelligent Discovery},
volume = {1},
issue = {1},
abstract = {Machine learning algorithms are extensively used in an increasing number of systems, applications, technologies, and products, both in industry and in society as a whole. They enable computing devices to learn from previous experience and therefore improve their performance in a certain context or environment. In this way, many useful possibilities have been made accessible. However, dealing with an increasing amount of data poses difficulties for classical devices. Quantum systems may offer a way forward, possibly enabling to scale up machine learning calculations in certain contexts. On the contrary, quantum systems themselves are also hard to scale up, due to decoherence and the fragility of quantum superpositions. In the short and mid term, it has been evidenced that a quantum paradigm that combines evolution under large analog blocks with discrete quantum gates, may be fruitful to achieve new knowledge of classical and quantum systems with no need of having a fault-tolerant quantum computer. In this perspective, we review some recent works that employ this digital-analog quantum paradigm to carry out efficient machine learning calculations with current quantum devices.},
keywords = {US},
pubstate = {published},
tppubtype = {article}
}
Machine learning algorithms are extensively used in an increasing number of systems, applications, technologies, and products, both in industry and in society as a whole. They enable computing devices to learn from previous experience and therefore improve their performance in a certain context or environment. In this way, many useful possibilities have been made accessible. However, dealing with an increasing amount of data poses difficulties for classical devices. Quantum systems may offer a way forward, possibly enabling to scale up machine learning calculations in certain contexts. On the contrary, quantum systems themselves are also hard to scale up, due to decoherence and the fragility of quantum superpositions. In the short and mid term, it has been evidenced that a quantum paradigm that combines evolution under large analog blocks with discrete quantum gates, may be fruitful to achieve new knowledge of classical and quantum systems with no need of having a fault-tolerant quantum computer. In this perspective, we review some recent works that employ this digital-analog quantum paradigm to carry out efficient machine learning calculations with current quantum devices.
Lamata, L.; Llenas, A.
Digital-analog quantum genetic algorithm using Rydberg-atom arrays Artículo de revista
En: Physical Review A, vol. 110, iss. 4, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: US
@article{nokey,
title = {Digital-analog quantum genetic algorithm using Rydberg-atom arrays},
author = {Lamata, L. and Llenas, A. },
url = {https://arxiv.org/pdf/2407.09308},
doi = {doi.org/10.1103/PhysRevA.110.042603},
year = {2024},
date = {2024-10-03},
urldate = {2024-10-03},
journal = {Physical Review A},
volume = {110},
issue = {4},
abstract = {Digital-analog quantum computing (DAQC) combines digital gates with analog operations, offering an alternative paradigm for universal quantum computation. This approach leverages the higher fidelities of analog operations and the flexibility of local single-qubit gates. In this paper, we propose a quantum genetic algorithm within the DAQC framework using a Rydberg-atom emulator. The algorithm employs single-qubit operations in the digital domain and a global driving interaction based on the Rydberg Hamiltonian in the analog domain. We evaluate the algorithm performance by estimating the ground-state energy of Hamiltonians, with a focus on molecules such as H2, LiH, and BeH2. Our results show energy estimations with less than 1% error and state overlaps nearing 1, with computation times ranging from a few minutes for H2 (2-qubit circuits) to one to two days for LiH and BeH2 (6-qubit circuits). The gate fidelities of global analog operations further underscore DAQC as a promising quantum computing strategy in the noisy intermediate-scale quantum era.},
keywords = {US},
pubstate = {published},
tppubtype = {article}
}
Digital-analog quantum computing (DAQC) combines digital gates with analog operations, offering an alternative paradigm for universal quantum computation. This approach leverages the higher fidelities of analog operations and the flexibility of local single-qubit gates. In this paper, we propose a quantum genetic algorithm within the DAQC framework using a Rydberg-atom emulator. The algorithm employs single-qubit operations in the digital domain and a global driving interaction based on the Rydberg Hamiltonian in the analog domain. We evaluate the algorithm performance by estimating the ground-state energy of Hamiltonians, with a focus on molecules such as H2, LiH, and BeH2. Our results show energy estimations with less than 1% error and state overlaps nearing 1, with computation times ranging from a few minutes for H2 (2-qubit circuits) to one to two days for LiH and BeH2 (6-qubit circuits). The gate fidelities of global analog operations further underscore DAQC as a promising quantum computing strategy in the noisy intermediate-scale quantum era.
Sáiz, Á.; Khalouf‑Rivera, J.; Arias, J. M.; Pérez‑Fernández, P.; Casado‑Pascual, J.
Quantum Phase Transitions in periodically quenched systems Artículo de revista
En: Quantum, vol. 8, pp. 1365, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: US
@article{nokey,
title = {Quantum Phase Transitions in periodically quenched systems},
author = {Sáiz, Á. and Khalouf‑Rivera, J. and Arias, J. M. and Pérez‑Fernández, P. and Casado‑Pascual, J.},
url = {https://quantum-journal.org/papers/q-2024-06-11-1365/},
doi = {doi.org/10.22331/q-2024-06-11-1365},
year = {2024},
date = {2024-06-11},
urldate = {2024-06-11},
journal = {Quantum},
volume = {8},
pages = {1365},
abstract = {Quantum phase transitions encompass a variety of phenomena that occur in quantum systems exhibiting several possible symmetries. Traditionally, these transitions are explored by continuously varying a control parameter that connects two different symmetry configurations. Here we propose an alternative approach where the control parameter undergoes abrupt and time-periodic jumps between only two values. This approach yields results surprisingly similar to those obtained by the traditional one and may prove experimentally useful in situations where accessing the control parameter is challenging.},
keywords = {US},
pubstate = {published},
tppubtype = {article}
}
Quantum phase transitions encompass a variety of phenomena that occur in quantum systems exhibiting several possible symmetries. Traditionally, these transitions are explored by continuously varying a control parameter that connects two different symmetry configurations. Here we propose an alternative approach where the control parameter undergoes abrupt and time-periodic jumps between only two values. This approach yields results surprisingly similar to those obtained by the traditional one and may prove experimentally useful in situations where accessing the control parameter is challenging.
García-Ramos, J. E.; Sáiz, A.; Arias, J. M.; Lamata, L.; Pérez Fernández, P.
Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning Artículo de revista
En: Advanced Quantum Technologies, vol. 6, iss. 1, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: US
@article{nokey,
title = {Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning},
author = {García-Ramos, J.E. and Sáiz, A. and Arias, J.M. and Lamata, L. and Pérez Fernández, P. },
url = {https://advanced.onlinelibrary.wiley.com/doi/10.1002/qute.202300219},
doi = {doi.org/10.1002/qute.202300219},
year = {2024},
date = {2024-05-03},
journal = {Advanced Quantum Technologies},
volume = {6},
issue = {1},
abstract = {In this paper, the application of quantum simulations and quantum machine learning is explored to solve problems in low-energy nuclear physics. The use of quantum computing to address nuclear physics problems is still in its infancy, and particularly, the application of quantum machine learning (QML) in the realm of low-energy nuclear physics is almost nonexistent. Three specific examples are presented where the utilization of quantum computing and QML provides, or can potentially provide in the future, a computational advantage: i) determining the phase/shape in schematic nuclear models, ii) calculating the ground state energy of a nuclear shell model-type Hamiltonian, and iii) identifying particles or determining trajectories in nuclear physics experiments.},
keywords = {US},
pubstate = {published},
tppubtype = {article}
}
In this paper, the application of quantum simulations and quantum machine learning is explored to solve problems in low-energy nuclear physics. The use of quantum computing to address nuclear physics problems is still in its infancy, and particularly, the application of quantum machine learning (QML) in the realm of low-energy nuclear physics is almost nonexistent. Three specific examples are presented where the utilization of quantum computing and QML provides, or can potentially provide in the future, a computational advantage: i) determining the phase/shape in schematic nuclear models, ii) calculating the ground state energy of a nuclear shell model-type Hamiltonian, and iii) identifying particles or determining trajectories in nuclear physics experiments.
Olivera-Atencio, M. L.; Lamata, L.; Casado-Pascual, J.
Benefits of Open Quantum Systems for Quantum Machine Learning Artículo de revista
En: Adv Quantum Technologies, 2023, ISBN: 2511-9044.
Resumen | Enlaces | BibTeX | Etiquetas: US
@article{nokey,
title = {Benefits of Open Quantum Systems for Quantum Machine Learning},
author = {Olivera-Atencio, M.L. and Lamata, L. and Casado-Pascual, J.
},
url = {https://quantumspain-project.es/wp-content/uploads/2023/12/Adv-Quantum-Tech-2023-Olivera‐Atencio-Benefits-of-Open-Quantum-Systems-for-Quantum-Machine-Learning.pdf},
doi = {10.1002/qute.202300247},
isbn = {2511-9044},
year = {2023},
date = {2023-12-10},
urldate = {2023-12-10},
journal = {Adv Quantum Technologies},
abstract = {Quantum machine learning (QML) is a discipline that holds the promise ofrevolutionizing data processing and problem-solving. However, dissipationand noise arising from the coupling with the environment are commonlyperceived as major obstacles to its practical exploitation, as they impact thecoherence and performance of the utilized quantum devices. Significantefforts have been dedicated to mitigating and controlling their negative effectson these devices. This perspective takes a different approach, aiming toharness the potential of noise and dissipation instead of combating them.Surprisingly, it is shown that these seemingly detrimental factors can providesubstantial advantages in the operation of QML algorithms under certaincircumstances. Exploring and understanding the implications of adaptingQML algorithms to open quantum systems opens up pathways for devisingstrategies that effectively leverage noise and dissipation. The recent worksanalyzed in this perspective represent only initial steps toward uncoveringother potential hidden benefits that dissipation and noise may offer. Asexploration in this field continues, significant discoveries are anticipated thatcould reshape the future of quantum computing.},
keywords = {US},
pubstate = {published},
tppubtype = {article}
}
Quantum machine learning (QML) is a discipline that holds the promise ofrevolutionizing data processing and problem-solving. However, dissipationand noise arising from the coupling with the environment are commonlyperceived as major obstacles to its practical exploitation, as they impact thecoherence and performance of the utilized quantum devices. Significantefforts have been dedicated to mitigating and controlling their negative effectson these devices. This perspective takes a different approach, aiming toharness the potential of noise and dissipation instead of combating them.Surprisingly, it is shown that these seemingly detrimental factors can providesubstantial advantages in the operation of QML algorithms under certaincircumstances. Exploring and understanding the implications of adaptingQML algorithms to open quantum systems opens up pathways for devisingstrategies that effectively leverage noise and dissipation. The recent worksanalyzed in this perspective represent only initial steps toward uncoveringother potential hidden benefits that dissipation and noise may offer. Asexploration in this field continues, significant discoveries are anticipated thatcould reshape the future of quantum computing.
Martín-Guerrero, J. D.; Lamata, L.; Villmann, T.
Quantum Artificial Intelligence: A tutorial Conferencia
2023, ISBN: 978-2-87587-088-9.
Resumen | Enlaces | BibTeX | Etiquetas: US
@conference{nokey,
title = {Quantum Artificial Intelligence: A tutorial},
author = {Martín-Guerrero, J. D. and Lamata, L. and Villmann, T.},
url = {https://quantumspain-project.es/wp-content/uploads/2023/09/ES2023-2.pdf},
doi = {10.14428/esann/2023.ES2023-2},
isbn = {978-2-87587-088-9},
year = {2023},
date = {2023-10-06},
urldate = {2023-10-06},
abstract = {This special session includes five high-quality papers on relevant topics, like quantum reinforcement learning, parallelization of quantum calculations, quantum feature selection and quantum vector quantization, thus capturing the richness and variability of approaches within QAI.},
keywords = {US},
pubstate = {published},
tppubtype = {conference}
}
This special session includes five high-quality papers on relevant topics, like quantum reinforcement learning, parallelization of quantum calculations, quantum feature selection and quantum vector quantization, thus capturing the richness and variability of approaches within QAI.
