Artículo
Benefits of Open Quantum Systems for Quantum Machine Learning
Autor/es | Olivera Atencio, María Laura
![]() ![]() ![]() ![]() Lamata Manuel, Lucas ![]() ![]() ![]() ![]() ![]() ![]() Casado Pascual, Jesús ![]() ![]() ![]() ![]() ![]() ![]() |
Departamento | Universidad de Sevilla. Departamento de Física Atómica, Molecular y Nuclear |
Fecha de publicación | 2023 |
Fecha de depósito | 2023-12-11 |
Publicado en |
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Resumen | 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 ... 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. |
Agencias financiadoras | Junta de Andalucía Ministerio de Ciencia, Innovación y Universidades (MICINN). España Ministerio de Ciencia e Innovación (MICIN). España |
Identificador del proyecto | P20-00617
![]() US-1380840 ![]() PID2019-104002GB-C21 ![]() PID2019-104002GB- C22 ![]() PID2022-136228NB-C2 ![]() 10.13039/501100011033 ![]() |
Cita | Olivera Atencio, M.L., Lamata Manuel, L. y Casado Pascual, J. (2023). Benefits of Open Quantum Systems for Quantum Machine Learning. Advanced Quantum Technologies, 2300247. https://doi.org/10.1002/qute.202300247. |
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