Article
Reinforcement learning for semi-autonomous approximate quantum eigensolver
Author/s | Albarrán Arriagada, Francisco
Retamal, Juan Carlos Solano, Enrique Lamata Manuel, Lucas |
Department | Universidad de Sevilla. Departamento de Física Atómica, Molecular y Nuclear |
Publication Date | 2020 |
Deposit Date | 2021-04-29 |
Published in |
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Abstract | The characterization of an operator by its eigenvectors and eigenvalues
allows us to know its action over any quantum state. Here, we propose a protocol
to obtain an approximation of the eigenvectors of an arbitrary ... The characterization of an operator by its eigenvectors and eigenvalues allows us to know its action over any quantum state. Here, we propose a protocol to obtain an approximation of the eigenvectors of an arbitrary Hermitian quantum operator. This protocol is based on measurement and feedback processes, which characterize a reinforcement learning protocol. Our proposal is composed of two systems, a black box named environment and a quantum state named agent. The role of the environment is to change any quantum state by a unitary matrix UˆE = e −iτOˆE where OˆE is a Hermitian operator, and τ is a real parameter. The agent is a quantum state which adapts to some eigenvector of OˆE by repeated interactions with the environment, feedback process, and semi-random rotations. With this proposal, we can obtain an approximation of the eigenvectors of a random qubit operator with average fidelity over 90% in less than 10 iterations, and surpass 98% in less than 300 iterations. Moreover, for the two-qubit cases, the four eigenvectors are obtained with fidelities above 89% in 8000 iterations for a random operator, and fidelities of 99% for an operator with the Bell states as eigenvectors. This protocol can be useful to implement semi-autonomous quantum devices which should be capable of extracting information and deciding with minimal resources and without human intervention. |
Funding agencies | Comisión Nacional de Investigación Científica y Tecnológica (CONICYT). Chile European Union (UE) Gobierno Vasco Ministerio de Ciencia e Innovación (MICIN). España Agencia Estatal de Investigación. España European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) |
Project ID. | CONICYT-FB0807
QMiCS (820505) OpenSuperQ (820363) IT986-16 PGC2018-095113-B-I00 |
Citation | Albarrán Arriagada, F., Retamal, J.C., Solano, E. y Lamata Manuel, L. (2020). Reinforcement learning for semi-autonomous approximate quantum eigensolver. Machine Learning: Science and Technology, 1 (1), 015002. |
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