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

TL;DR

This paper introduces U-VQNHE, a quantum-neural hybrid algorithm that enforces unitarity to improve stability, reduce measurement overhead, and enhance accuracy in ground state estimation for quantum systems.

Contribution

The paper presents a novel unitary variational quantum-neural hybrid eigensolver that addresses normalization and divergence issues in previous methods, enabling scalable and stable quantum ground state estimation.

Findings

U-VQNHE reduces measurement requirements significantly.

The method improves accuracy over standard variational quantum eigensolvers.

U-VQNHE demonstrates enhanced stability during training.

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.