Quantum Context-Aware Encoding for Neuromorphic Quantum Computing

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces a novel encoding scheme for quantum neural networks (QNNs) designed to leverage the inherent parallelism of neuromorphic quantum computing hardware and the contextual awareness of neuromorphic computation. The core idea is to develop a context-aware encoding method based on quantum state superposition and entanglement, where each quantum bit (qubit) carries contextual information related to its surrounding environment, including the states of neighboring qubits and the synaptic weights within the neuromorphic network. This contextual information is processed by the neuromorphic network to dynamically adjust the parameters of the QNN, thereby enhancing its learning efficiency and generalization capabilities, particularly in complex tasks. We demonstrate that this approach contrasts sharply with traditional QNN encoding techniques which predominantly rely on fixed parameter representations, and we argue that it represents a significant step towards overcoming performance limitations in QNNs. The proposed method offers the potential to significantly improve the learning speed and robustness of QNNs in scenarios with high data dimensionality and complex correlations, representing a new paradigm for quantum machine learning. ---

DOI: 10.5281/zenodo.22244028

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