QeHDC: Hyperdimensional Computing based on Quantum-enhanced binding and SuperClass Construction

2026-06-21

AI Summary

This work addresses the limitations of traditional hyperdimensional computing in representational capacity and noise robustness, as well as the training complexity and inefficiency of existing quantum-enhanced approaches. The authors propose a novel quantum-enhanced hyperdimensional computing framework that employs a single-shot training strategy, efficiently mapping classical data by integrating sine encoding with quantum amplitude encoding. They further introduce an innovative reference-state-based quantum binding circuit and a density-matrix-driven hyperclass generation mechanism, leveraging eigenvalue decomposition to extract salient quantum state features. Evaluated on standard benchmarks, the proposed method substantially outperforms both classical and current quantum approaches, achieving notable advances in classification accuracy, noise robustness, and computational feasibility.

Abstract

Hyperdimensional Computing (HDC) is a robust computational framework inspired by human cognition characterized by simple and efficient operations within high-dimensional vector spaces. Quantum-enhanced Hyperdimensional Computing (QeHDC) extends classical HDC by leveraging quantum mechanical properties to enhance computational efficiency. In this paper, we propose a novel Quantum HDC framework featuring a one-pass training method, leveraging sinusoidal and quantum encoding to project classical data into quantum amplitude states efficiently. Our framework introduces an innovative reference-state-based quantum binding operation realized via quantum circuits. Furthermore, we propose a density-matrix-based superclass generation strategy employing eigenvalue decomposition to extract critical quantum state features effectively, enabling a more accurate and robust class representation. Experimental evaluations conducted on standard benchmark datasets demonstrate our approach's superior performance, robustness to noise, and computational feasibility compared to traditional classical and existing quantum-enhanced approaches. The results highlight the practical benefits and potential of Quantum HDC for quantum-enhanced classification tasks and pave the way for future advancements in quantum-inspired computational paradigms.

Problem

• Hyperdimensional Computing
• Quantum-enhanced classification
• Quantum binding
• Superclass construction
• Quantum machine learning

Innovation

• Quantum-enhanced Hyperdimensional Computing
• quantum binding
• superclass construction
• density matrix
• one-pass training