Quantum-Inspired Hardware Acceleration for Tensor Decomposition

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Tensor decomposition is a fundamental operation in numerous machine learning tasks, including dimensionality reduction, recommendation systems, and signal processing. However, traditional algorithms for tensor decomposition often suffer from high computational complexity, particularly when dealing with large tensors. This paper proposes a novel hardware architecture inspired by quantum interference patterns to accelerate tensor decomposition. The core idea is to utilize precisely controlled analog circuits to mimic the interference effects observed in quantum systems, thereby reducing the computational burden. The architecture employs a series of interconnected analog components designed to perform tensor contractions and eigenvalue decompositions, the key steps in many tensor decomposition algorithms. We present a detailed description of the hardware design, including the underlying mathematical principles and the key components. The theoretical analysis demonstrates the potential for significant speedup compared to traditional software implementations. Furthermore, we discuss the challenges and future directions for this approach, including scaling the architecture to handle larger tensors and exploring different quantum-inspired techniques.

DOI: 10.5281/zenodo.22599348

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