Electrodynamic Intelligence in Quantum Memristive Neuromorphic Systems: Toward Quantum-Enhanced Learning
Year: 2025
Type: preprint
Abstract: We propose a physically grounded framework for learning in memristive neuromorphic systems through the concept of Electrodynamic Intelligence (EDI). EDI formalizes intelligence as an energy-information-propagation phenomenon, quantifying the maximum information-processing capacity of a physical substrate. Building on principles of electromagnetic field theory, memristor physics, and nanoscale quantum mechanics, we develop quantitative metrics linking memory capacity, propagation velocity, and efficiency into a universal measure-the Electrodynamic Intelligence Quotient (EIQ). The framework accounts for quantum tunneling, coherent transport, and interference in sub-5 nm memristors, providing testable predictions relating coherence, energy dissipation, and learning efficiency. Unlike classical neural networks that abstract away physical laws, EDI grounds learning in quantum electrodynamic processes governing charge transport and field interactions. This approach offers a fundamental measure of intelligence and a roadmap for ultra-efficient quantum neuromorphic computing.
Author: Dorian Aur
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414487803
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