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Hyperdimensional Computing for Distributed Quantum State Teleportation

Jincheng Zhang

Published Aug 28, 2026• 0 citations
Publisher

Abstract

This paper explores a novel approach to quantum state teleportation leveraging hyperdimensional computing (HDC). Traditional quantum state teleportation protocols face limitations due to qubit fidelity requirements and the complexity associated with distributing entangled qubits over long distances. We propose utilizing HDC – high-dimensional vectors characterized by near-orthogonal components – to represent and transmit quantum states. Our method significantly reduces the need for direct qubit entanglement, offering a potentially scalable and robust alternative. The core mechanism involves encoding the quantum state information into the high-dimensional vector components and subsequently extracting the state at the destination. We detail the mathematical framework underpinning this approach, focusing on vector projections, inner products, and state reconstruction. The results demonstrate a viable path towards distributed quantum state teleportation, addressing the key challenges of fidelity and entanglement distribution. We provide a detailed analysis of the computational complexity and potential advantages of this HDC-based teleportation scheme.

DOI
10.5281/zenodo.22138433

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Hyperdimensional Computing (HDC) is a brain-inspired computational paradigm that utilizes high-dimensional vector spaces to represent, bind, and process information efficiently. Its robustness, scalability, and adaptability make HDC particularly effective for machine learning tasks such as classification and pattern recognition. In this work, we introduce Quantum-StateHD (QSHD), a novel approach that leverages random quantum states as encoding mechanisms for high-dimensional spaces within the HDC framework. Quantum states, with their inherent properties of high-dimensionality, near-orthogonality, and probabilistic distributions, provide a natural substrate for constructing robust representations. Quantum-StateHD encodes classical data into the coefficients of random quantum states and seamlessly integrates these representations into the HDC pipeline. Experimental results on several benchmark classification datasets demonstrate that Quantum-StateHD achieves competitive or superior performance compared to traditional state-of-the-art HDC methods, showcasing its potential for robust and efficient high-dimensional information processing. This study bridges the gap between quantum information science and HDC, offering a promising direction for hybrid quantum-classical machine learning architectures.
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.
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Abstract High‐dimensional quantum states, or ‘qudits’, provide significant advantages over traditional qubits in quantum communication, such as increased information capacity, enhanced noise resilience, and reduced information loss. Despite these benefits, their implementation has been constrained by challenges in generation, transmission, and detection. This paper presents a novel theoretical framework for transmitting quantum information using qudit entanglement distribution over a superposition of causal orders in two quantum channels. Using this model, a quantum switch operation for 2‐qudit systems is introduced, which facilitates enhanced fidelity of entanglement distribution and quantum teleportation. The results demonstrate that the use of qudits in entanglement distribution achieves a fidelity improvement from 0.5 (for qubit‐based systems) to 0.94 for 20‐dimensional qudits, even under noisy channel conditions. This enhancement is achieved by exploiting the increased Hilbert space of high‐dimensional states and the inherent noise‐resilience properties of quantum switches operating in superpositions of causal orders. The findings underscore the potential of qudit‐based quantum systems in achieving robust and high‐fidelity communication in environments where traditional qubit‐based systems face limitations.