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A scalable quantum-neural hybrid variational algorithm for ground state estimation
Minwoo Kim, Kyoung Keun Park, Uihwan Jeong, Sangyeon Lee, Taehyun Kim·July 15, 2025
Quantum Physics
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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.