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arXiv 2102.13161quant-phphysics.atm-clusphysics.comp-ph
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用于量子哈密顿工程的深度强化学习

Deep reinforcement learning for quantum Hamiltonian engineering

Pai Peng, Xiaoyang Huang, Chao Yin, Linta Joseph, Chandrasekhar Ramanathan, Paola Cappellaro

摘要:

在量子多体系统中设计所需的哈密顿量对于量子模拟、计算和传感等应用是必不可少的。传统的量子哈密顿工程序列是基于微扰理论使用人类直觉设计的,这可能无法描述最优解,也无法适应复杂的实验缺陷。在这里,我们使用深度强化学习(DRL)技术在数值上搜索哈密顿工程序列,并通过实验证明它们在固态核磁共振量子模拟器上的表现优于著名的序列。作为一个例子,我们的目标是解耦强相互作用的自旋1/2系统。我们训练DRL代理在不同的实验缺陷的存在下,并在模拟和实验中验证输出序列的鲁棒性。令人惊讶的是,许多学习序列显示出一种共同的模式,据我们所知,以前没有发现过,但有一个有意义的分析描述。因此,我们可以限制搜索空间的基础上,这种控制模式,允许搜索更长的序列,最终导致的序列是强大的,对占主导地位的缺陷,在我们的实验。我们的结果不仅展示了量子哈密顿工程的一般方法,而且强调了黑箱人工智能与物理系统的理解相结合,以实现实验可行的应用的重要性。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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