Resiliency analysis and improvement of variational quantum factoring in superconducting qubit

Ling Qiu, M. Alam, Abdullah Ash-Saki, Swaroop Ghosh
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引用次数: 3

Abstract

Variational algorithm using Quantum Approximate Optimization Algorithm (QAOA) can solve the prime factorization problem in near-term noisy quantum computers. Conventional Variational Quantum Factoring (VQF) requires a large number of 2-qubit gates (especially for factoring a large number) resulting in deep circuits. The output quality of the deep quantum circuit is degraded due to errors limiting the computational power of quantum computing. In this paper, we explore various transformations to optimize the QAOA circuit for integer factorization. We propose two criteria to select the optimal quantum circuit that can improve the noise resiliency of VQF.
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超导量子比特变分量子因子的弹性分析与改进
采用量子近似优化算法(QAOA)的变分算法可以解决近期噪声量子计算机中的质因数分解问题。传统的变分量子因式分解(VQF)需要大量的2量子位门(特别是对于大数的因式分解),从而导致深度电路。由于误差限制了量子计算的计算能力,导致深度量子电路的输出质量下降。在本文中,我们探索了各种变换来优化QAOA电路的整数分解。我们提出了两个标准来选择最优量子电路,以提高VQF的抗噪声能力。
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