Quantum Hyperdimensional Computing Maps Brain-inspired Models to Quantum Computers Using Resource-efficient, Native Operations

The pursuit of truly brain-inspired computing models faces significant hurdles, with many quantum approaches relying on adaptations of classical techniques. Fabio Cumbo, Rui-Hao Li, and Bryan Raubenolt, alongside colleagues from Cleveland Clinic Research, Case Western Reserve University, and the University of Louisiana at Lafayette, now present Quantum Hyperdimensional Computing (QHDC), a fundamentally new paradigm that directly maps the principles of brain-inspired Hyperdimensional Computing onto the native operations of a quantum computer. This research establishes a resource-efficient method for implementing core computational operations using quantum states, averaging processes, and phase transformations, and validates the framework through symbolic reasoning and classification tasks. Critically, the team demonstrates the viability of QHDC by executing a 156-qubit algorithm on an IBM quantum processor, establishing a physically realizable technology and paving the way for novel neuromorphic algorithms capable of tackling complex problems beyond the reach of classical systems.

Research highlights HDC’s potential for energy efficiency, compact data representation, and scalability, making it particularly valuable for edge computing and resource-constrained devices. Its distributed nature also provides inherent robustness to noise and failures. This computing approach finds significant application in bioinformatics and genomics, including efficient DNA sequencing, genome sketching, and analysis of large genomic datasets.

HDC also accelerates drug discovery by identifying potential candidates and predicting their properties, and aids cancer research through classification of cancer types based on DNA methylation data and analysis of microbial profiles. Beyond genomics, HDC supports multi-omics data fusion for personalized medicine and integrates data from multiple sensors, particularly in edge computing scenarios. It also demonstrates promise in predicting the toxicity of chemical compounds and serves as a potential alternative or complement to traditional machine learning algorithms for classification, pattern recognition, and data analysis. HDC’s efficiency and scalability position it as a bridge to quantum computing, offering an accessible and energy-efficient approach for certain tasks, and researchers are exploring its integration with quantum error mitigation techniques. Current trends focus on multi-modal data fusion, adaptive HDC systems that dynamically optimize performance, and utilizing HDC for graph encoding and analysis. This work addresses the challenge of adapting classical algorithms for quantum processors by introducing a paradigm fundamentally built upon quantum phenomena. For the first time, the team implemented this framework, moving beyond theoretical proposals to a physically realized and testable paradigm.

Hypervectors are represented as quantum states, bundling is achieved through superposition, and binding is natively realized via entanglement, establishing a resource-efficient mapping. Validation involved two distinct experiments: a symbolic analogical reasoning task and a supervised classification challenge, both successfully executed. Results demonstrate the versatility of QHDC through execution on a state-of-the-art 156-qubit IBM Heron r3 quantum processor. This implementation confirms the proposed mappings and establishes QHDC as a physically realizable technology. The framework’s success lays the foundation for a new class of quantum neuromorphic algorithms and opens a promising avenue for tackling complex cognitive and biomedical problems currently intractable for classical systems. Researchers successfully map hypervectors to quantum states and implement core HDC operations, bundling, binding, permutation, and similarity measurement, using quantum circuits and techniques like Linear Combination of Unitaries and the Hadamard Test. This represents a significant advancement as it moves beyond simply adapting classical machine learning for quantum processors and instead builds a computational model intrinsically suited to quantum hardware. The viability of QHDC has been rigorously demonstrated through implementation and validation on a 156-qubit IBM quantum processor, alongside comparisons with classical computation and ideal quantum simulation. Results from symbolic analogical reasoning and supervised classification tasks confirm the framework’s versatility and physical realizability, establishing QHDC as a promising technology for tackling complex problems currently intractable for classical systems. Future work will likely focus on scaling the framework and applying it to more challenging cognitive and biomedical problems, potentially opening new avenues for quantum neuromorphic algorithms and advanced computation.