用强化学习自动设计量子电路，精准生成目标量子态
Approximate Quantum State Preparation Through Proximal Policy Optimization
- 用近端策略优化算法搜索最优量子门序列
- 2到5比特系统误差低至10^{-14}，接近完美逼近
- 适合量子算法设计与硬件优化研究者
本文提出一种用于近似量子态制备（QSP）的量子架构搜索框架。由于量子比特数量增加导致搜索空间呈指数级增长，寻找最优量子线路极具挑战。为此，采用基于近端策略优化（PPO）的深度强化学习智能体，目标是在逼近目标态的同时最小化门数。每一步中，智能体添加一个新门并重新计算近似态与目标态之间的保真度。实验覆盖2至5个量子比特，涵盖贝尔态、GHZ态、W态、迪克态等预定义态以及完全随机态。该框架实现的近似误差低至10^{-14}。
原文摘要 · Abstract (English)
In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed. QSP is a challenging task, since the search space grows exponentially with the number of qubits, making the identification of the optimal circuit non-trivial. To address this problem, deep reinforcement learning is employed through an agent based on proximal policy optimization. The objective of the agent is to identify the best possible approximation of the target state while simultaneously minimizing the number of gates used. At each step, the agent appends a new gate to the circuit and recomputes the fidelity between the approximated state and the target states. Various experiments have been performed from 2 to 5 qubits. Both predefined states, such as Bell, GHZ, W, and Dicke states, and completely random states are considered. The proposed framework is able to achieve approximation errors of $10^{-14}$.
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