论文标题
用蒙特卡洛树搜索通过神经网络来优化量子退火计划
Optimizing Quantum Annealing Schedules with Monte Carlo Tree Search enhanced with neural networks
论文作者
论文摘要
量子退火是一种实用方法，可以在现实世界中近似实现绝热量子计算模型。绝热算法的目的是在退火路径结束时准备问题编码的哈密顿量的基态。这通常是通过缓慢地驱动量子系统的动态演化来实现的，以实现绝热。正确优化的退火计划通常会显着加速计算过程。受深钢筋学习的成功启发，例如DeepMind的Alphazero，我们提出了一种蒙特卡洛树搜索（MCTS）算法及其增强版本以神经网络增强，我们将其命名为Quantumzero（QZERO），以自动化杂种量子量子群中退火时间表的设计。即使我们在本研究中考虑的3个SAT示例的缩短，MCT和QZERO算法在发现有效退火时间表方面的表现都非常出色。此外，神经网络的灵活性使我们能够应用转移学习技术来提高Qzero的性能。我们在基准研究中证明了MCT和QZERO在设计退火计划时的性能比其他强化学习算法更有效。
Quantum annealing is a practical approach to approximately implement the adiabatic quantum computational model under a real-world setting. The goal of an adiabatic algorithm is to prepare the ground state of a problem-encoded Hamiltonian at the end of an annealing path. This is typically achieved by driving the dynamical evolution of a quantum system slowly to enforce adiabaticity. Properly optimized annealing schedules often significantly accelerate the computational process. Inspired by the recent success of deep reinforcement learning such as DeepMind's AlphaZero, we propose a Monte Carlo Tree Search (MCTS) algorithm and its enhanced version boosted with neural networks, which we name QuantumZero (QZero), to automate the design of annealing schedules in a hybrid quantum-classical framework. Both the MCTS and QZero algorithms perform remarkably well in discovering effective annealing schedules even when the annealing time is short for the 3-SAT examples we consider in this study. Furthermore, the flexibility of neural networks allows us to apply transfer-learning techniques to boost QZero's performance. We demonstrate in benchmark studies, that MCTS and QZero perform more efficiently than other reinforcement learning algorithms in designing annealing schedules.
