Adaptive Quantum Computing Simulations for Machine Learning

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

The simulation of quantum computers presents a formidable challenge due to the exponential growth in computational cost with the number of qubits. Current simulation methods often struggle to scale to even moderately sized quantum systems, severely limiting their applicability for machine learning applications. This paper introduces an adaptive simulation strategy designed to mitigate this issue. The core idea revolves around dynamically adjusting the simulation complexity—the level of detail—based on the demands of the underlying machine learning task. We propose a hierarchical simulation approach, where a coarse-grained representation is utilized initially, and the simulation is progressively refined, or "zoomed in," only when specific regions or interactions become critical for the learning process. This strategy minimizes unnecessary computations, leading to a significant reduction in overall simulation time and resource requirements. The effectiveness of this approach is demonstrated through theoretical analysis and the formulation of key equations governing the adaptive simulation process. The primary goal is to achieve a more efficient and practical approach to quantum computing simulations for machine learning, paving the way for the exploration of quantum algorithms within the machine learning domain.

DOI: 10.5281/zenodo.22272601

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