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This paper proposes a qubit-efficient quantum framework for hyperdimensional computing decomposition that reduces representation cost from O(D) to O(log D) qubits while preserving the quadratic search advantage, achieving up to 2000x fewer qubits.
This paper proposes using Hyperdimensional Computing, specifically Holographic Reduced Representations, to embed tabular data rows for structured querying, enabling interpretable similarity thresholds and zero-match detection, outperforming a baseline method on row retrieval tasks.
D2H-AD is a novel anomaly detection framework using Hyperdimensional Computing (HDC) that combines distance-based and density-aware encoding. It outperforms five baselines across multiple benchmarks, offering lightweight, interpretable, and efficient performance for edge AI and IoT.
Introduces 'tastebud-memory', an open-source agent memory system using hyperdimensional computing to create reversible, searchable project fingerprints, with backtested validation and an MCP server for integration.
This paper proposes FedQHD, a novel federated Q-learning method using hyperdimensional random-feature state encoders with linear readouts to enable closed-form function-space aggregation, addressing the federation gap due to heterogeneous client encoders.