Hyperparameter Optimization via Evolutionary Quantum Annealing

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper proposes a novel approach to hyperparameter optimization for deep learning models, termed Evolutionary Quantum Annealing (EQA). Traditional hyperparameter optimization techniques, such as grid search and Bayesian optimization, often suffer from significant computational costs, particularly when dealing with complex model architectures and large hyperparameter spaces. EQA addresses this limitation by integrating the strengths of quantum annealing and evolutionary algorithms. Specifically, the algorithm utilizes quantum tunneling to efficiently explore the hyperparameter landscape, mitigating the problem of getting trapped in local optima that commonly plagues classical evolutionary methods. The core of EQA involves encoding the hyperparameter optimization problem as a quantum Ising model, followed by a simulated annealing process driven by an evolutionary algorithm. The evolutionary algorithm guides the quantum annealing process, refining the solution space and accelerating convergence. We demonstrate the efficacy of EQA through a theoretical analysis and outline its potential advantages over conventional methods. The key contribution lies in the synergistic combination of these two powerful optimization paradigms, offering a potentially faster and more robust solution for hyperparameter tuning in deep learning.

DOI: 10.5281/zenodo.22157768

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