Quantum State Prediction Adaptive Learning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum State Prediction Adaptive Learning is a novel machine learning framework designed to automatically learn quantum state prediction models, utilizing the inherent adaptability of quantum states. This framework departs from traditional machine learning approaches by directly incorporating quantum state dynamics into the learning process. The core mechanism involves a tailored architecture that leverages quantum state self-information to establish a predictive model, enabling efficient and robust state estimation. The potential applications of this framework extend across diverse fields, including quantum computing and quantum information security. The study presents a comprehensive overview of the proposed approach, detailing the mathematical framework, implementation details, and preliminary results demonstrating its efficacy in quantum state prediction.

DOI: 10.5281/zenodo.22194779

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