Quantum Machine Learning with Decoherence-Aware Optimization Algorithm

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper presents a novel optimization algorithm designed specifically to mitigate the detrimental effects of decoherence in quantum machine learning (QML) algorithms. Decoherence, the loss of quantum information due to environmental interactions, remains a significant obstacle to the practical realization of QML. Our approach, termed Decoherence-Aware Optimization (DAO), introduces a dynamic decoherence correction mechanism during quantum circuit design. This mechanism actively adjusts the interaction strengths between qubits to minimize the impact of decoherence, thereby enhancing the fidelity and computational efficiency of QML algorithms. We formulate the optimization problem as a constrained minimization, considering both the desired quantum computation and the suppression of decoherence. The core of the algorithm lies in iteratively modifying the circuit parameters to achieve a balance between accuracy and decoherence reduction. We demonstrate, theoretically, how this approach can improve the performance of algorithms like Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) by reducing the need for deep circuits and longer coherence times. The effectiveness of DAO is evaluated through a simplified model, showcasing a potential for significant gains in QML computation.

DOI: 10.5281/zenodo.22315889

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