Quantum Entanglement-Based Information Encoding in Neuromorphic Computing

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper explores a novel approach to neuromorphic computing by leveraging the unique properties of quantum entanglement. The core idea is to utilize multi-particle quantum entanglement states as the physical connections between artificial neurons, enabling efficient parallel information processing and mimicking biological neural networks. We propose a quantum neuromorphic chip architecture employing multiple superconducting quantum bits (Qubits) engineered to exhibit entanglement. The design focuses on manipulating the coherence time, topological structure, and combined operation of quantum gates to emulate synaptic transmission and learning within biological neural networks. Specifically, we investigate encoding information through entanglement and explore parallel computation facilitated by this quantum phenomenon. Furthermore, we address the challenges of quantum computation by integrating quantum decoherence and quantum error correction techniques. The potential for achieving high-performance, energy-efficient, and biologically inspired computing is discussed, representing a significant departure from traditional approaches and classical neural networks. The proposed framework offers a pathway towards scalable quantum neuromorphic systems with potential applications in areas such as pattern recognition, machine learning, and complex system modeling.

DOI: 10.5281/zenodo.22474868

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