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arXiv · 2507.11002

A scalable quantum-neural hybrid variational algorithm for ground state estimation

Minwoo Kim·Kyoung Keun Park·Uihwan Jeong·Sangyeon Lee·Taehyun Kim
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Abstract

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.

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Minwoo Kim, Kyoung Keun Park, Uihwan Jeong, Sangyeon Lee, Taehyun Kim. 2025-07-15. A scalable quantum-neural hybrid variational algorithm for ground state estimation. https://arxiv.org/abs/2507.11002

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