Linghu Kehuan, Qian Yang, Wang Ruixia, Hu Meng-Jun, Li Zhiyuan, Li Xuegang, Xu Huikai, Zhang Jingning, Ma Teng, Zhao Peng, Liu Dong E, Hsieh Min-Hsiu, Wu Xingyao, Du Yuxuan, Tao Dacheng, Jin Yirong, Yu Haifeng
Beijing Academy of Quantum Information Sciences, Beijing 100193, China.
School of Computer Science, Faculty of Engineering, University of Sydney, Camperdown, NSW 2006, Australia.
Entropy (Basel). 2024 Nov 26;26(12):1025. doi: 10.3390/e26121025.
Variational quantum algorithms (VQAs) have shown strong evidence to gain provable computational advantages in diverse fields such as finance, machine learning, and chemistry. However, the heuristic ansatz exploited in modern VQAs is incapable of balancing the trade-off between expressivity and trainability, which may lead to degraded performance when executed on noisy intermediate-scale quantum (NISQ) machines. To address this issue, here, we demonstrate the first proof-of-principle experiment of applying an efficient automatic ansatz design technique, i.e., quantum architecture search (QAS), to enhance VQAs on an 8-qubit superconducting quantum processor. In particular, we apply QAS to tailor the hardware-efficient ansatz toward classification tasks. Compared with heuristic ansätze, the ansatz designed by QAS improves the test accuracy from 31% to 98%. We further explain this superior performance by visualizing the loss landscape and analyzing effective parameters of all ansätze. Our work provides concrete guidance for developing variable ansätze to tackle various large-scale quantum learning problems with advantages.
变分量子算法（VQAs）已显示出有力证据，表明其在金融、机器学习和化学等不同领域能够获得可证明的计算优势。然而，现代VQAs中所采用的启发式近似方法无法平衡表现力和可训练性之间的权衡，这可能导致在有噪声的中等规模量子（NISQ）机器上执行时性能下降。为解决这一问题，在此我们展示了首个原理验证实验，即应用一种高效的自动近似设计技术——量子架构搜索（QAS），以在一个8量子比特超导量子处理器上增强VQAs。具体而言，我们应用QAS来针对分类任务定制硬件高效近似方法。与启发式近似方法相比，由QAS设计的近似方法将测试准确率从31%提高到了98%。我们通过可视化损失曲面并分析所有近似方法的有效参数，进一步解释了这种卓越性能。我们的工作为开发可变近似方法以有效解决各种大规模量子学习问题提供了具体指导。
