Reinforcement Learning with Neural Networks for Quantum Feedback
Reinforcement Learning with Neural Networks for Quantum Feedback
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DOI：
10.1103/physrevx.8.031084
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发表时间：
2018-09-27
影响因子：
12.5
通讯作者：
Marquardt, Florian
中科院分区：
文献类型：
--
作者：
Foesel, Thomas;Tighineanu, Petru;Marquardt, Florian
人工神经网络的机器学习正在给科学带来革命性的变化。最高级的挑战需要自主发现答案。在强化学习领域，控制策略根据奖励函数进行改进。基于神经网络的强化学习的力量已经被最近的惊人成功所突出，比如下围棋，但它对物理学的好处还有待证明。在这里，我们展示了一个基于网络的“代理”如何发现完整的量子纠错策略，保护一组量子比特免受噪声的影响。这些策略需要与测量结果相适应的反馈。在没有人工指导和针对不同硬件资源进行定制的情况下，从零开始查找它们是一项艰巨的挑战，因为组合搜索空间非常大。为了解决这一挑战，我们提出了两个想法：教师和学生网络的两阶段学习，以及量化恢复存储在多量子位系统中的量子信息的能力的奖励。除了对量子计算的直接影响之外，我们的工作更普遍地证明了基于神经网络的强化学习在物理学中的前景。
Machine learning with artificial neural networks is revolutionizing science. The most advanced challenges require discovering answers autonomously. In the domain of reinforcement learning, control strategies are improved according to a reward function. The power of neural-network-based reinforcement learning has been highlighted by spectacular recent successes such as playing Go, but its benefits for physics are yet to be demonstrated. Here, we show how a network-based "agent" can discover complete quantum-error-correction strategies, protecting a collection of qubits against noise. These strategies require feedback adapted to measurement outcomes. Finding them from scratch without human guidance and tailored to different hardware resources is a formidable challenge due to the combinatorially large search space. To solve this challenge, we develop two ideas: two-stage learning with teacher and student networks and a reward quantifying the capability to recover the quantum information stored in a multiqubit system. Beyond its immediate impact on quantum computation, our work more generally demonstrates the promise of neural-network-based reinforcement learning in physics.