强化学习辅助量子优化
Reinforcement Learning assisted Quantum Optimization
摘要：
提出了一种基于量子近似优化算法（QAOA）的反馈量子控制强化学习（RL）方案。QAOA需要一个变分最小化的状态构造通过应用一系列的酉算子，取决于参数生活在高维空间。我们重新制定这样一个最小的搜索作为一个学习任务，其中RL代理选择的控制参数的幺正，系统的部分信息。我们发现，我们的RL计划找到一个政策收敛到最佳绝热解QAOA发现Mbeng等人arXiv：1906.08948的几何不变的量子伊辛链。在存在无序的情况下，我们证明了我们的RL方案允许在小样本上执行训练部分，并在较大的系统上成功转移。
英文摘要：
We propose a reinforcement learning (RL) scheme for feedback quantum control within the quan-tum approximate optimization algorithm (QAOA). QAOA requires a variational minimization for states constructed by applying a sequence of unitary operators, depending on parameters living ina highly dimensional space. We reformulate such a minimum search as a learning task, where a RL agent chooses the control parameters for the unitaries, given partial information on the system. We show that our RL scheme finds a policy converging to the optimal adiabatic solution for QAOA found by Mbeng et al. arXiv:1906.08948 for the translationally invariant quantum Ising chain. In presence of disorder, we show that our RL scheme allows the training part to be performed on small samples, and transferred successfully on larger systems.
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