Quantum Entanglement Reservoir Computing for Complex System Modeling

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel approach to complex system modeling utilizing quantum entanglement as a dynamically configurable reservoir. Traditional recurrent neural networks often struggle with high-dimensional data and capturing complex temporal dependencies. We propose a Quantum Entanglement Reservoir Computing (QERC) framework where system inputs are directly mapped to entangled qubit states. The reservoir's evolution is governed by weak, time-dependent interactions, enabling non-linear mapping and the emergence of rich temporal correlations. The correlated qubit output, upon measurement, directly reflects the system state. This method offers a potentially significant advantage over conventional approaches by exploiting the inherent redundancy and correlations within quantum entanglement, providing a more robust and efficient representation of complex dynamics. The core claim of this work is the utilization of quantum entanglement as a dynamically configurable reservoir to represent complex, high-dimensional system dynamics, bypassing the limitations of traditional recurrent neural networks. The core mechanism involves mapping system inputs directly to entangled qubit states, with reservoir evolution driven by weak, time-dependent interactions, facilitating non-linear mapping and emergent temporal correlations. Measurement of correlated qubit output directly reflects the system state. ---

DOI: 10.5281/zenodo.22463772

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