论文标题
增强学习协助量子优化
Reinforcement Learning assisted Quantum Optimization
论文作者
论文摘要
我们提出了一种增强学习（RL）方案，以在Quan-Tum近似优化算法（QAOA）内进行反馈量子控制。 QAOA需要通过应用一系列单一操作员来构建的状态的各种最小化，具体取决于居住在高度维空间的参数。我们将这种最低限度的搜索重新制定为一项学习任务，其中RL代理选择了单位人员的控制参数，并且给定部分信息。我们表明，我们的RL方案找到了Mbeng等人发现的QAOA的最佳绝热解决方案的策略。 Arxiv：1906.08948用于翻译不变的量子链。在存在障碍的情况下，我们表明我们的RL方案允许在小样本上执行训练零件，并在较大的系统上成功传输。
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.
