Neuro-Morphic Computing via Temporal Flow Sub-Quantization

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel approach to neuro-morphic computing, termed Temporal Flow Sub-Quantization (TFSQ), which addresses the critical limitation of existing neuro-morphic architectures: the inadequate handling of temporal information within time-series data. The core concept involves encoding temporal sequences into quantum states, leveraging the inherent properties of quantum mechanics – superposition and entanglement – to achieve parallel processing and efficient inference. We propose a model architecture where time series data is represented and computed in parallel using quantum bits (qubits), resulting in a significant improvement in processing speed and resource utilization compared to traditional sequential methods. The key innovation lies in the sub-quantization technique, which decomposes complex time series into manageable quantum states, optimizing both the representation and computational efficiency. This work lays the groundwork for a new paradigm in neuro-morphic computing, capable of tackling complex temporal data challenges across diverse applications, including anomaly detection, predictive maintenance, and signal processing. We present a theoretical framework and a foundational model, demonstrating the potential of TFSQ.

DOI: 10.5281/zenodo.22314410

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