Research Preview · 2024
The Database That Thinks in Superposition
NQAD introduces quantum-inspired indexing structures and neural retrieval pathways to achieve sub-millisecond query latency at billion-vector scale — without approximation.
Benchmark · ANN-1B
1B vectors · 99.7% recall · single node
Layer stack
Core concepts
Four primitives. Infinite scale.
QSI
Quantum Superposition Indexing
Index entries exist in probabilistic superposition across multiple dimensional subspaces simultaneously, collapsing to the optimal retrieval path only at query time.
NRP
Neural Retrieval Pathways
Learned routing networks replace static tree structures, dynamically adapting traversal paths based on query distribution and historical access patterns.
ECC
Entanglement-Coherent Caching
Cache coherence is maintained through quantum-entanglement-inspired dependency graphs, eliminating stale reads without distributed locking overhead.
AQO
Adaptive Query Optimisation
The query planner continuously refines its cost model using reinforcement signals from execution telemetry, converging on near-optimal plans within minutes of deployment.
Architecture
Four layers. One coherent system.
NQAD is structured as four tightly coupled layers: a quantum-inspired index layer, a neural routing layer, a coherent cache layer, and a classical storage substrate. Each layer exposes a clean interface to the one above, enabling independent optimisation without sacrificing end-to-end performance.
QSI Layer
Probabilistic multi-space indexing
Neural Router
Learned traversal & path selection
ECC Cache
Entanglement-coherent invalidation
Storage Substrate
Classical persistent storage
Throughput
1.2M
queries / sec
Results
Numbers that redefine the baseline.
Measured on ANN-1B, SIFT-1B, and DEEP-1B benchmarks. Single-node deployment, commodity hardware.
Ready to rethink retrieval?
The full paper, reference implementation, and benchmark suite are available now.