Adaptive Quantum Entanglement Computation Based on Neuromorphic Networks

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper proposes a novel computational paradigm leveraging the inherent properties of neuromorphic networks and quantum entanglement. The core claim is to achieve adaptive and efficient parallelization of computational tasks by dynamically adapting the architecture and resource allocation based on entanglement characteristics. The proposed mechanism utilizes a neuromorphic network where connection weights are modulated by the strength and topology of quantum entanglement. Real-time adjustments are performed based on input data and the current computational state, facilitating parallel quantum computation and dynamic resource allocation. This approach overcomes limitations inherent in traditional neuromorphic networks and conventional quantum computing, offering a potentially transformative solution for complex computational problems. The system's adaptability offers a pathway toward scalable quantum computation, where entanglement resources can be optimized for specific tasks. This research explores the theoretical framework and key design considerations for such a system, highlighting its potential impact on diverse fields. ---

DOI: 10.5281/zenodo.22293722

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