Dynamic Quantum Circuit Optimization via Variational Quantum Evolution

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Variational quantum algorithms (VQAs) hold significant promise for solving complex optimization problems in quantum computing. However, existing VQA approaches often suffer from premature convergence, struggling to effectively explore the high-dimensional optimization landscape and frequently settling into local minima. This paper introduces a novel strategy, Variational Quantum Evolution (VQE), designed to overcome these limitations. VQE utilizes a quantum-classical hybrid approach, continuously evolving the parameters of a quantum circuit through a simulated stochastic evolutionary process. A quantum process operator is employed to generate a range of quantum states, while a classical feedback loop, informed by the quantum measurements, dynamically adjusts the circuit parameters to promote exploration and escape local optima. The core innovation lies in integrating quantum evolution directly within the variational framework, harnessing quantum superposition and interference to drive a more robust and adaptive optimization trajectory. The proposed method offers a potential pathway to unlocking the full capabilities of VQAs by enabling a more thorough exploration of the solution space. We demonstrate, theoretically, the advantages of this approach and outline the key components required for its implementation.

DOI: 10.5281/zenodo.22272908

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