Hierarchical Quantum Simulation with Adaptive Sampling

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel hierarchical quantum simulation framework designed to significantly enhance the scalability and efficiency of quantum simulations. Traditional quantum simulation methods often struggle with large systems due to computational complexity. We propose a dynamically partitioned simulation space, leveraging a sophisticated partitioning algorithm to automatically divide the simulation space into sub-regions based on the inherent complexity of the underlying system. This adaptive partitioning strategy minimizes computational burden and allows for efficient exploration of the simulation space. The framework incorporates a novel weighting mechanism to prioritize simulation effort on regions exhibiting high complexity, resulting in a substantial reduction in the overall simulation time. We detail the implementation of this framework, analyze its performance across various benchmark problems, and discuss its potential impact on the field of quantum simulation.

DOI: 10.5281/zenodo.22195288

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