Warm Start of Variational Quantum Algorithms for Quadratic Unconstrained Binary Optimization Problems

TL;DR

This paper introduces a warm start method for Variational Quantum Eigensolver (VQE) inspired by imaginary time evolution, which improves convergence, success rate, and robustness against errors in quantum optimization tasks.

Contribution

The paper proposes a novel warm start technique for VQE based on imaginary time evolution, enhancing its efficiency and effectiveness in solving quadratic unconstrained binary optimization problems.

Findings

Significantly improves VQE success rate and reduces convergence iterations.

Mitigates statistical errors from finite measurements.

Alleviates barren plateau effects to some extent.

Abstract

Variational Quantum Eigensolver (VQE) is widely used in near-term hardware. However, their performances remain limited by the poor trainability and are dependent on random parameter initialization. In this work, we propose a warm start method inspired by imaginary time evolution, allowing for determining initial parameters that prioritize lower energy states in a resource-efficient way. Using classical simulations, we demonstrate that this warm start method significantly improves the success rate and reduces the number of iterations required for the convergence of VQE. The numerical results also indicate that the warm start approach effectively mitigates statistical errors arising from a finite number of measurements, and to a certain extent alleviates the effect of barren plateaus.