南京航空航天大学Tianlai Yang团队近日实现了通过分解随机基优化改进绝热量子分解。这一研究成果发表在2025年5月19日出版的《物理评论A》杂志上。

整数因式分解仍然是经典计算机面临的一个重大挑战，也是RSA加密安全的基础。绝热量子算法是一种有前景的解决方案，但它们的实际实现受到当前噪声中等规模量子器件和量子模拟器的短相干时间的限制。在这项工作中，研究组应用了分解随机基（CRAB）优化技术来增强绝热量子因式分解算法。 

通过将CRAB应用于21到2479的整数因子来证明其有效性，当演化时间超过量子速度限制时，可以显著提高目标状态的保真度。值得注意的是，这种性能改进显示了在存在去相位噪声的情况下的弹性，突显了CRAB在噪声量子系统中的实际应用。该研究结果表明，CRAB优化可以作为推进绝热量子算法的有力工具，对量子信息处理任务具有更广泛的影响。

附：英文原文

Title: Improving adiabatic quantum factorization via chopped random-basis optimization

Author: Tianlai Yang, Mo Xiong, Ming Xue, Xinwei Li, Jinbin Li

Issue&Volume: 2025/05/19

Abstract: Integer factorization remains a significant challenge for classical computers and is fundamental to the security of RSA encryption. Adiabatic quantum algorithms present a promising solution, yet their practical implementation is limited by the short coherence times of current noisy intermediate-scale quantum devices and quantum simulators. In this work, we apply the chopped random-basis (CRAB) optimization technique to enhance adiabatic quantum factorization algorithms. We demonstrate the effectiveness of CRAB by applying it to factor the integers ranging from 21 to 2479, achieving significantly improved fidelity of the target state when the evolution time exceeds the quantum speed limit. Notably, this performance improvement shows resilience in the presence of dephasing noise, highlighting CRAB's practical utility in noisy quantum systems. Our findings suggest that CRAB optimization can serve as a powerful tool for advancing adiabatic quantum algorithms, with broader implications for quantum information processing tasks.

DOI: 10.1103/PhysRevA.111.052617

Source: https://journals.aps.org/pra/abstract/10.1103/PhysRevA.111.052617