arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2404.14865quant-ph

基于强化学习的Clifford+T电路酉综合

Unitary Synthesis of Clifford+T Circuits with Reinforcement Learning

Sebastian Rietsch, Abhishek Y. Dubey, Christian Ufrecht, Maniraman Periyasamy, Axel Plinge, Christopher Mutschler, Daniel D. Scherer

AI总结:

该研究提出基于Gumbel AlphaZero树搜索的深度强化学习方法,解决无参数Clifford+T门集合的酉综合问题,在多量子比特场景下综合效率优于多款现有工具,为后续酉综合算法提供强基线。

摘要:

本文提出一种用于将酉矩阵综合为量子电路的深度强化学习方法。酉综合的目标是找到一个表示给定酉矩阵的量子电路,同时最小化电路深度、总门数、特定门数或这些因素的组合。以往研究主要聚焦于连续门集合,而从无参数的Clifford+T门集合进行酉综合仍是一个挑战。尽管对于一般酉矩阵,该任务的时间复杂度不可避免地会随量子比特数呈指数增长,但减少简单问题实例的运行时间仍然是一项重大挑战。本研究中,我们应用树搜索方法Gumbel AlphaZero(冈贝尔阿尔法零)来解决一类可精确综合的Clifford+T酉矩阵子集的问题。我们的方法能有效综合最多5个量子比特、由最多60个门的随机电路生成的酉矩阵对应的电路,在更大量子比特数的综合时间上优于QuantumCircuitOpt和MIN-T-SYNTH等现有工具。此外,它在成功综合随机的、可精确综合的酉矩阵方面超过了Synthetiq。这些结果为未来的酉综合算法建立了一个强基线。

英文摘要:

This paper presents a deep reinforcement learning approach for synthesizing unitaries into quantum circuits. Unitary synthesis aims to identify a quantum circuit that represents a given unitary while minimizing circuit depth, total gate count, a specific gate count, or a combination of these factors. While past research has focused predominantly on continuous gate sets, synthesizing unitaries from the parameter-free Clifford+T gate set remains a challenge. Although the time complexity of this task will inevitably remain exponential in the number of qubits for general unitaries, reducing the runtime for simple problem instances still poses a significant challenge. In this study, we apply the tree-search method Gumbel AlphaZero to solve the problem for a subset of exactly synthesizable Clifford+T unitaries. Our method effectively synthesizes circuits for up to five qubits generated from randomized circuits with up to 60 gates, outperforming existing tools like QuantumCircuitOpt and MIN-T-SYNTH in terms of synthesis time for larger qubit counts. Furthermore, it surpasses Synthetiq in successfully synthesizing random, exactly synthesizable unitaries. These results establish a strong baseline for future unitary synthesis algorithms.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑