Deep reinforcement learning for quantum Hamiltonian engineering

• URL: http://arxiv.org/abs/2102.13161v1
• Date: Thu, 25 Feb 2021 20:44:31 GMT
• Title: Deep reinforcement learning for quantum Hamiltonian engineering
• Authors: Pai Peng, Xiaoyang Huang, Chao Yin, Linta Joseph, Chandrasekhar Ramanathan, Paola Cappellaro
• Abstract summary: We numerically search for Hamiltonian engineering sequences using deep reinforcement learning techniques. We experimentally demonstrate that they outperform celebrated sequences on a solid-state nuclear magnetic resonance quantum simulator.
• Score: 6.632380923836344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Engineering desired Hamiltonian in quantum many-body systems is essential for applications such as quantum simulation, computation and sensing. Conventional 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 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 an meaningful analytical description. We can thus restrict the searching space based on this control pattern, allowing 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 understanding of physical system in order to realize experimentally feasible applications.

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