Deep Reinforcement Learning for Quantum Hamiltonian Engineering

Pai Peng (彭湃), Xiaoyang Huang, Chao Yin, Linta Joseph, Chandrasekhar Ramanathan, and Paola Cappellaro
Phys. Rev. Applied 18, 024033 – Published 11 August 2022
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Abstract

Engineering desired Hamiltonians in quantum many-body systems is essential for applications such as quantum simulation, computation, and sensing. Many widely used quantum Hamiltonian engineering sequences are designed using human intuition based on perturbation theory, which may not describe the optimal solution and is unable to accommodate complex experimental imperfections. Here we numerically search for Hamiltonian engineering sequences using deep reinforcement learning (DRL) techniques and experimentally demonstrate that they outperform celebrated decoupling sequences on a solid-state nuclear magnetic resonance quantum simulator. As an example, we aim at decoupling strongly interacting spin-1/2 systems. We train DRL agents in the presence of different experimental imperfections and verify robustness of the output sequences both in simulations and experiments. Surprisingly, many of the learned sequences exhibit a common pattern that had not been discovered before, to our knowledge, but has a meaningful analytical description. We can thus restrict the searching space based on this control pattern, allowing us to search for longer sequences, ultimately leading to sequences that are robust against dominant imperfections in our experiments. Our results not only demonstrate a general method for quantum Hamiltonian engineering, but also highlight the importance of combining black-box artificial intelligence with an understanding of the physical system in order to realize experimentally feasible applications.

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  • Received 26 February 2021
  • Revised 12 July 2022
  • Accepted 18 July 2022

DOI:https://doi.org/10.1103/PhysRevApplied.18.024033

© 2022 American Physical Society

Physics Subject Headings (PhySH)

Atomic, Molecular & OpticalInterdisciplinary PhysicsQuantum Information, Science & TechnologyGeneral Physics

Authors & Affiliations

Pai Peng (彭湃)1,*, Xiaoyang Huang2,‡, Chao Yin2, Linta Joseph3, Chandrasekhar Ramanathan3, and Paola Cappellaro4,2,†

  • 1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
  • 2Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
  • 3Department of Physics and Astronomy, Dartmouth College, Hanover, New Hampshire 03755, USA
  • 4Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

  • *paipeng@mit.edu
  • pcappell@mit.edu
  • P.P. and X.H. contributed equally to this work.

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Vol. 18, Iss. 2 — August 2022

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