Combinatorial optimization enhanced by shallow quantum circuits with 104 superconducting qubits
Abstract
A pivotal task for quantum computing is to speed up solving 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, based on which we design an algorithm for accelerating solving the ground states of Ising model, a class of NP-hard problems in combinatorial optimization. The algorithm employs a hybrid quantum-classical workflow, with a shallow-circuit quantum sampling subroutine dedicated to navigating the energy landscape. Using up to 104 superconducting qubits, we demonstrate that this algorithm outputs favorable solutions against even a highly-optimized classical simulated annealing (SA) algorithm. Furthermore, we illustrate the path toward quantum speedup based on the time-to-solution metric against SA running on a single-core CPU with just 100 qubits. Our results indicate a promising alternative to classical heuristics for combinatorial optimization, a paradigm where quantum advantage might become possible on near-term superconducting quantum processors with thousands of qubits and without the assistance of error correction.
Cite
@article{arxiv.2509.11535,
title = {Combinatorial optimization enhanced by shallow quantum circuits with 104 superconducting qubits},
author = {Xuhao Zhu and Zuoheng Zou and Feitong Jin and Pavel Mosharev and Maolin Luo and Yaozu Wu and Jiachen Chen and Chuanyu Zhang and Yu Gao and Ning Wang and Yiren Zou and Aosai Zhang and Fanhao Shen and Zehang Bao and Zitian Zhu and Jiarun Zhong and Zhengyi Cui and Yihang Han and Yiyang He and Han Wang and Jia-Nan Yang and Yanzhe Wang and Jiayuan Shen and Gongyu Liu and Zixuan Song and Jinfeng Deng and Hang Dong and Pengfei Zhang and Chao Song and Zhen Wang and Hekang Li and Qiujiang Guo and Man-Hong Yung and H. Wang},
journal= {arXiv preprint arXiv:2509.11535},
year = {2026}
}