用强化学习自动设计量子电路，提升可扩展性。
Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations
- 将量子电路设计建模为马尔可夫决策过程，用Q-learning与DQN求解。
- 相比人工启发式方法，实现更自动、可扩展的电路合成。
- 适合研究量子算法自动化与硬件适配的工程师与学者。
量子计算有望超越经典计算。当前量子硬件仍处于发展初期，被称为嘈杂中等规模量子（Noisy Intermediate-Scale Quantum, NISQ）时代。主要挑战之一是自动化量子电路设计，即将目标量子电路映射到通用门集。本文提出一种通用的马尔可夫决策过程（MDP）建模，并采用Q-learning与深度Q网络（DQN）算法进行量子电路设计。通过利用深度强化学习的强大能力，旨在提供一种比传统手工启发式方法更具自动性与可扩展性的解决方案。
原文摘要 · Abstract (English)
Quantum computing promises advantages over classical computing. The manufacturing of quantum hardware is in the infancy stage, called the Noisy Intermediate-Scale Quantum (NISQ) era. A major challenge is automated quantum circuit design that map a quantum circuit to gates in a universal gate set. In this paper, we present a generic MDP modeling and employ Q-learning and DQN algorithms for quantum circuit design. By leveraging the power of deep reinforcement learning, we aim to provide an automatic and scalable approach over traditional hand-crafted heuristic methods.
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