Work

Warm start of variational quantum algorithms for quadratic unconstrained binary optimization problems

Year: 2025

Type: article

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.

Source: edoc Publication server (Humboldt University of Berlin)

DOI: 10.18452/36628

Authors: Chai, Yahui, Jansen, Karl, Kühn, Stefan, Schwägerl, Tim, Stollenwerk, Tobias

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7140272085

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