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arXiv 2212.01002quant-ph

面向对角酉矩阵的自动深度优化量子电路综合,具有渐近最优门数

Automatic Depth-Optimized Quantum Circuit Synthesis for Diagonal Unitary Matrices with Asymptotically Optimal Gate Count

Shihao Zhang, Kai Huang, Lvzhou Li

AI总结:

本文针对对角酉矩阵综合提出一种深度优化算法,在保持渐近最优门数的同时将电路深度较先前方法降低近一半,并在随机矩阵和QAOA电路上验证了显著效果。

摘要:

当前含噪声的中等规模量子(NISQ)设备只能执行深度较浅的小型电路,因为它们仍然受到噪声存在的制约:量子门具有错误率,量子态因退相干而脆弱。因此,在为特定任务设计量子电路时,优化深度/门数具有重要意义。对角酉矩阵被公认为许多量子算法或量子计算过程的关键构建模块。先前的工作已经讨论了在原始门集 $\{\ ext{CNOT}, R_Z\}$ 上综合对角酉矩阵的问题。然而,该问题尚未被完全理解,因为现有的综合方法没有优化电路深度。在本文中,我们提出了一种深度优化的综合算法,能够为任意给定的对角酉矩阵自动生成量子电路。特别地,它不仅保证了渐近最优的门数,而且与先前方法相比,将总电路深度几乎减半。在技术层面,我们发现了一种非常适合降低电路深度的统一电路重写规则。我们的综合算法的性能既进行了理论分析,又通过对两个示例的评估进行了实验验证。首先,在综合最多16个量子比特的随机对角酉矩阵时,我们相比Welch方法实现了近50%的深度降低。其次,在重新综合最多14个量子比特的特定量子近似优化算法(QAOA)电路的对角部分时,我们实现了平均22.05%的深度降低。

英文摘要:

Current noisy intermediate-scale quantum (NISQ) devices can only execute small circuits with shallow depth, as they are still constrained by the presence of noise: quantum gates have error rates and quantum states are fragile due to decoherence. Hence, it is of great importance to optimize the depth/gate-count when designing quantum circuits for specific tasks. Diagonal unitary matrices are well-known to be key building blocks of many quantum algorithms or quantum computing procedures. Prior work has discussed the synthesis of diagonal unitary matrices over the primitive gate set $\{\text{CNOT}, R_Z\}$. However, the problem has not yet been fully understood, since the existing synthesis methods have not optimized the circuit depth. In this paper, we propose a depth-optimized synthesis algorithm that automatically produces a quantum circuit for any given diagonal unitary matrix. Specially, it not only ensures the asymptotically optimal gate-count, but also nearly halves the total circuit depth compared with the previous method. Technically, we discover a uniform circuit rewriting rule well-suited for reducing the circuit depth. The performance of our synthesis algorithm is both theoretically analyzed and experimentally validated by evaluations on two examples. First, we achieve a nearly 50\% depth reduction over Welch's method for synthesizing random diagonal unitary matrices with up to 16 qubits. Second, we achieve an average of 22.05\% depth reduction for resynthesizing the diagonal part of specific quantum approximate optimization algorithm (QAOA) circuits with up to 14 qubits.

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。

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