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Quantum State Collapse with Adaptive Resonance

Jincheng Zhang

Published Sep 6, 2026• 0 citations
Publisher

Abstract

Quantum state collapse is a fundamental process in quantum mechanics, representing the irreversible transformation of a quantum system from a superposition of states to a definite state. This paper explores a novel quantum state collapse protocol leveraging adaptive resonance principles to mitigate energy dissipation during collapse, thereby enhancing coherence times in quantum systems. We propose a dynamically adjusting collapse probability, guided by real-time monitoring of the system's energy state, offering a potentially more efficient and universal collapse strategy compared to traditional fixed mechanisms. The paper details the algorithm's design, initial results, and potential implications for quantum computing and quantum information processing.

DOI
10.5281/zenodo.22502782

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Quantum state collapse is a fundamental process in quantum mechanics, representing the irreversible transformation of a quantum system from a superposition of states to a definite state. This paper explores a novel quantum state collapse protocol leveraging adaptive resonance principles to mitigate energy dissipation during collapse, thereby enhancing coherence times in quantum systems. We propose a dynamically adjusting collapse probability, guided by real-time monitoring of the system's energy state, offering a potentially more efficient and universal collapse strategy compared to traditional fixed mechanisms. The paper details the algorithm's design, initial results, and potential implications for quantum computing and quantum information processing.
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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.
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