New Quantum Algorithm Slashes Qubit Needs 2,000x for HDC Search
A logarithmic encoding trick makes quantum search practical with dramatically fewer qubits.
Hyperdimensional Computing (HDC) uses high-dimensional binary vectors (hypervectors) for symbolic reasoning, but decomposing a target hypervector into its constituent parts requires searching over an exponentially large space of N^F candidates. While quantum algorithms can provide a quadratic speedup, they typically require O(D) qubits to represent each hypervector, making them impractical for large dimensions.
A team led by Sanggeon Yun introduced a logarithmic encoding scheme that cuts qubit requirements from O(D) to O(log D) — up to 2,000x fewer qubits in tested regimes. Their framework combines a reversible hypervector lookup operator with a modified Dürr-Høyer quantum search, maintaining the O(√N^F) query complexity of standard Grover search. Experimental results confirm accurate similarity computation and correct decomposition in executable regimes, opening the door to more practical quantum-accelerated HDC workloads. The paper has been accepted at ICCAD 2026.
- Reduces qubit representation cost from O(D) to O(log D) using logarithmic encoding
- Achieves up to 2,000× fewer qubits compared to baseline explicit encodings
- Preserves O(√N^F) search complexity via a modified Dürr-Høyer procedure
Why It Matters
Makes quantum search feasible for real-world hyperdimensional computing, drastically cutting hardware requirements for symbolic AI acceleration.