We propose the unitary variational quantum-neural hybrid eigensolver (U-VQNHE), which improves upon the original VQNHE by enforcing unitary neural transformations. The non-unitary nature of VQNHE causes normalization issues and divergence of the loss function during training, leading to exponential scaling of measurement overhead with qubit number. U-VQNHE resolves these issues, significantly reduces required measurements, and retains improved accuracy and stability over standard variational quantum eigensolvers.
@article{arxiv.2507.11002,
title = {A scalable quantum-neural hybrid variational algorithm for ground state estimation},
author = {Minwoo Kim and Kyoung Keun Park and Uihwan Jeong and Sangyeon Lee and Taehyun Kim},
journal= {arXiv preprint arXiv:2507.11002},
year = {2026}
}
Comments
Superseded by arXiv:2602.17295. Readers should refer to that manuscript instead of the present article. Version v3 contains no scientific changes and updates only the comments