Efficient quantum-control protocols are required to utilize the full power of quantum computers. A new reinforcement learning approach can realize efficient, robust control of quantum many-body states, promising a practical advance in harnessing present-day quantum technologies.
References
Daley, A. J. et al. Nature 607, 667–676 (2022).
Metz, F. & Bukov, M. Nat. Mach. Intell. 5, 780–791 (2023).
Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction 2nd ed. (MIT Press, 2018).
Krenn, M., Landgraf, J., Foesel, T. & Marquardt, F. Phys. Rev. A 107, 010101 (2023).
Ran, S.-J. et al. Tensor Network Contractions: Methods and Applications to Quantum Many-Body Systems (Springer International, 2020).
Orús, R. Nat. Rev. Phys. 1, 538–550 (2019).
Eisert, J., Cramer, M. & Plenio, M. B. Rev. Mod. Phys. 82, 277–306 (2010).
Perez-Garcia, D., Verstraete, F., Wolf, M. M. & Cirac, J. I. Quantum Inf. Comput. 7, 401–430 (2007).
Watkins, C. J. C. H. Learning from Delayed Rewards. PhD thesis, Univ. Cambridge (1989).
Doria, P., Calarco, T. & Montangero, S. Phys. Rev. Lett. 106, 190501 (2011).
Preskill, J. Quantum 2, 79 (2018).
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Lu, Y., Ran, SJ. Many-body control with reinforcement learning and tensor networks. Nat Mach Intell 5, 1058–1059 (2023). https://doi.org/10.1038/s42256-023-00732-3
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DOI: https://doi.org/10.1038/s42256-023-00732-3
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