Quantum Hyperdimensional Computing: a foundational paradigm for quantum neuromorphic architectures

• URL: http://arxiv.org/abs/2511.12664v1
• Date: Sun, 16 Nov 2025 16:01:56 GMT
• Title: Quantum Hyperdimensional Computing: a foundational paradigm for quantum neuromorphic architectures
• Authors: Fabio Cumbo, Rui-Hao Li, Bryan Raubenolt, Jayadev Joshi, Abu Kaisar Mohammad Masum, Sercan Aygun, Daniel Blankenberg,
• Abstract summary: We introduce Quantum Hyperdimensional Computing (QHDC), a fundamentally new paradigm.<n>We demonstrate that the core operations of its classical counterpart, Hyperdimensional Computing (HDC), map with remarkable elegance and direct correspondence onto the native operations of a QC.<n>We present the first-ever implementation of this framework, validated through symbolic analogical reasoning and supervised classification tasks.
• Score: 0.5785414922972825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A significant challenge in quantum computing (QC) is developing learning models that truly align with quantum principles, as many current approaches are complex adaptations of classical frameworks. In this work, we introduce Quantum Hyperdimensional Computing (QHDC), a fundamentally new paradigm. We demonstrate that the core operations of its classical counterpart, Hyperdimensional Computing (HDC), a brain-inspired model, map with remarkable elegance and direct correspondence onto the native operations of a QC. This suggests HDC is exceptionally well-suited for a quantum-native implementation. We establish a direct, resource-efficient mapping: (i) hypervectors are mapped to quantum states, (ii) the bundling operation is implemented as a quantum-native averaging process using a Linear Combination of Unitaries (LCU) and Oblivious Amplitude Amplification (OAA), (iii) the binding operation is realized via quantum phase oracles, (iv) the permutation operation is implemented using the Quantum Fourier Transform (QFT), and (v) vector similarity is calculated using quantum state fidelity measurements based on the Hadamard Test. We present the first-ever implementation of this framework, validated through symbolic analogical reasoning and supervised classification tasks. The viability of QHDC is rigorously assessed via a comparative analysis of results from classical computation, ideal quantum simulation, and execution of a 156-qubit IBM Heron r3 quantum processor. Our results validate the proposed mappings and demonstrate the versatility of the framework, establishing QHDC as a physically realizable technology. This work lays the foundation for a new class of quantum neuromorphic algorithms and opens a promising avenue for tackling complex cognitive and biomedical problems intractable for classical systems.

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