QeHDC: Hyperdimensional Computing based on Quantum-enhanced binding and SuperClass Construction
This work provides a novel quantum-inspired approach for efficient and robust classification, targeting researchers in quantum machine learning and hyperdimensional computing.
QeHDC introduces a quantum-enhanced hyperdimensional computing framework with one-pass training, quantum binding, and density-matrix-based superclass generation, achieving superior classification accuracy and noise robustness on benchmark datasets compared to classical and existing quantum methods.
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.