Quantum compiling aims to construct a quantum circuit V by quantum gates drawn from a native gate alphabet, which is functionally equivalent to the target unitary U. It is a crucial stage for the running of quantum algorithms on noisy intermediate-scale quantum (NISQ) devices. However, the space for structure exploration of quantum circuit is enormous, resulting in the requirement of human expertise, hundreds of experimentations or modifications from existing quantum circuits. In this paper, we propose a variational quantum compiling (VQC) algorithm based on reinforcement learning (RL), in order to automatically design the structure of quantum circuit for VQC with no human intervention. An agent is trained to sequentially select quantum gates from the native gate alphabet and the qubits they act on by double Q-learning with \epsilon-greedy exploration strategy and experience replay. At first, the agent randomly explores a number of quantum circuits with different structures, and then iteratively discovers structures with higher performance on the learning task. Simulation results show that the proposed method can make exact compilations with less quantum gates compared to previous VQC algorithms. It can reduce the errors of quantum algorithms due to decoherence process and gate noise in NISQ devices, and enable quantum algorithms especially for complex algorithms to be executed within coherence time.
翻译:量子电路的汇编用量子门字母绘制量子电流V, 其功能相当于目标单一的U。 这是一个运行音量算法的关键阶段, 用于运行音量中音量设备。 然而, 量子电路的结构探索空间巨大, 导致需要人的专门知识、 数百次实验或对现有量子电路的修改。 在本文中, 我们提议根据强化学习( RL) 自动设计无人类干预的 VQC 量子电流结构。 代理经过培训, 从本地门字母和量子中按顺序选择量子门算法。 然而, 量子电路的结构探索空间巨大, 导致需要人材、 数百次实验或对现有量子电路进行修改。 在本文中, 我们建议根据强化学习学习( RL ) 学习( RL ), 进行量子电量计算( VQC) 算法, 以比先前的 VQC 级算法更精确的量门进行编译, 特别是将QQ 级算算算法的精确的量算算算算,, 将使得QIS 的量算算算算系统系统的序列错误降低到QQRISQQQQQ 。