Quantum State Collapse as a Resource Allocation Problem

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum state collapse, the fundamental process by which a quantum system loses its superposition and enters a definite state, represents a crucial bottleneck in quantum computation. This paper explores the theoretical implications of this collapse as a resource allocation problem, investigating how optimizing collapse probabilities can lead to enhanced computational efficiency. We propose a novel framework that models collapse as a resource allocation problem, dynamically adjusting parameters to minimize energy expenditure while maximizing the probability of collapse, leveraging a learned model of the system. The core mechanism centers on a hierarchical optimization loop, iteratively refining the collapse probability based on a predictive model. This research aims to provide a deeper understanding of this fundamental process and identify potential avenues for improving quantum algorithm design.

DOI: 10.5281/zenodo.22301388

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