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#reinforcement learning Open access

Adaptive Quantum Algorithms for Optimization

Jincheng Zhang
Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)
Quantum Computing Algorithms and Architecture

Abstract

This paper explores the development of adaptive quantum algorithms designed to optimize the performance of quantum algorithms. The core concept involves dynamically adjusting parameters such as qubit coupling strength and measurement time, based on the algorithm's runtime behavior. This is achieved through the application of reinforcement learning, where an agent learns to optimize these parameters in response to metrics like decoherence and measurement error. The research addresses a significant challenge in quantum algorithm design – the difficulty in determining optimal parameter values – and presents a novel approach for achieving superior performance. The framework outlined herein provides a pathway for creating more robust and efficient quantum algorithms across a range of applications.

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