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  1. Home
  3. Combinatorial Optimization Enhanced By Shallow Quantum Circuits With 104 Superconducting Qubits.
  1. Home
  3. Combinatorial Optimization Enhanced By Shallow Quantum Circuits With 104 Superconducting Qubits.

Related Experiment Video

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Combinatorial optimization enhanced by shallow quantum circuits with 104 superconducting qubits.

Xuhao Zhu1, Zuoheng Zou2, Feitong Jin1

  • 1School of Physics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, and Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control, Zhejiang University, Hangzhou 310027, China.

National Science Review
|June 1, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a quantum-sampling algorithm for solving complex combinatorial optimization problems, like the Ising model. Experiments show it outperforms classical methods, paving the way for quantum advantage in optimization.

Keywords:
Ising modelcombinatorial optimizationhybrid quantum-classical algorithmquantum samplingquantum speedupsuperconducting qubit

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Area of Science:

  • Quantum Computing
  • Combinatorial Optimization
  • Computational Physics

Background:

  • Quantum computing aims to solve classically intractable problems.
  • Combinatorial optimization problems are widely applicable and map to Ising Hamiltonians.
  • Nondeterministic Polynomial time (NP)-hard problems, such as finding ground states of the Ising model, are key targets.

Purpose of the Study:

  • To propose a quantum-sampling strategy for accelerating solutions to Ising model ground states.
  • To design and experimentally demonstrate a quantum algorithm for NP-hard combinatorial optimization problems.
  • To assess the potential for quantum speedup compared to classical algorithms.

Main Methods:

  • Developed a quantum-sampling algorithm utilizing a shallow-circuit subroutine.
  • Navigated the energy landscape of the Ising model.
  • Experimentally tested the algorithm on up to 104 superconducting qubits.

    • Main Results:

    • The quantum algorithm produced favorable solutions compared to a highly optimized classical simulated-annealing algorithm.
    • Demonstrated a path toward quantum speedup using the time-to-solution metric.
    • Showcased competitive performance against classical heuristics on near-term superconducting processors.

    Conclusions:

    • The proposed quantum-sampling strategy offers a promising alternative for combinatorial optimization.
    • Quantum advantage for these problems may be achievable on current superconducting quantum processors.
    • The study highlights the potential of quantum computing for tackling valuable, complex optimization tasks.