A Hamiltonian-Guided Quantum-Inspired Neural Network: A New Paradigm for Deep Learning Optimization
Year: 2025
Type: article
Abstract: This paper introduces a novel approach to deep learning training that integrates a Hamiltonian framework with a Quantum-Inspired Neural Network (QINN) and a smooth transition to Backpropagation (BP). Our Hybrid QINN-BP model incorporates a dynamic time-field in a 5D space, optimizing weight transformations through a physics-driven mechanism. Experimental results on MNIST demonstrate improved loss convergence and final accuracy compared to standard BP, suggesting a significant advancement in AI training methodologies. We outline the theoretical framework, provide empirical validation, and discuss future directions for scaling this approach to larger datasets. Keywords: Hamiltonian Learning, Quantum-Inspired Neural Networks, Deep Learning Optimization, Backpropagation, Time-Field Theory, AI Efficiency © Nathaniel MacDonald, 2025.All rights reserved. This work, including but not limited to the theoretical framework, mathematical formulations, model architecture, experimental results, and all associated intellectual property, is the sole property of Nathaniel MacDonald. Unauthorized reproduction, distribution, modification, or commercial use of any part of this work, including but not limited to the Hybrid QINN-BP model and Hamiltonian-based AI optimization framework, is strictly prohibited without explicit written permission from the author. This work is protected under U.S. and international copyright laws. Any unauthorized use, reproduction, or distribution may result in legal action under applicable intellectual property laws.
Author: MacDonald, Nathaniel
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6911955566
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