Combinatorial optimization enhanced by shallow quantum circuits with 104 superconducting qubits

作者：Xuhao Zhu, Zuoheng Zou, Feitong Jin, P. A. Mosharev, Maolin Luo, Yaozu Wu, Jiachen Chen, Chuanyu Zhang, Yu Gao, Ning Wang, Yiren Zou, Aosai Zhang, Fanhao Shen, Zehang Bao, Zitian Zhu, Jiarun Zhong, Zhengyi Cui, Yihang Han, Yiyang He, Han Wang, Jianan Yang, Yanzhe Wang, Yanzhe Wang, Jiayuan Shen, Gongyu Liu, Zixuan Song, Jinfeng Deng, Hang Dong, Pengfei Zhang, Chao Song, Zhen Wang, Hekang Li, Qiujiang Guo, Man-Hong Yung, Haohua Wang, Haohua Wang · 发表于：National Science Review · 年份：2026 · DOI：10.1093/nsr/nwag124 · 被引用次数：1 · 研究领域：Quantum Computing Algorithms and Architecture、Quantum-Dot Cellular Automata

A pivotal task for quantum computing is to speed up the solution of problems that are both classically intractable and practically valuable. Among these, combinatorial optimization problems have attracted tremendous attention due to their broad applicability and natural fitness to Ising Hamiltonians. Here we propose a quantum-sampling strategy, on the basis of which we design an algorithm to accelerate the solution of the ground states of the Ising model, a class of Nondeterministic Polynomial time (NP)-hard problems in combinatorial optimization. The algorithm employs a shallow-circuit quantum-sampling subroutine to navigate the energy landscape. Using up to 104 superconducting qubits, we experimentally demonstrate that this algorithm outputs favorable solutions compared with even a highly optimized classical simulated-annealing algorithm, and we illustrate the path toward quantum speedup based on the time-to-solution metric relative to simulated annealing under serial execution. Our results indicate a promising alternative to classical heuristics for combinatorial optimization, for which quantum advantage may become possible on near-term superconducting quantum processors.