Quantum Memory Graph
by @dustin-a11y
Quantum-enhanced long-term memory for AI agents — #1 on LongMemEval (98.6% R@5, 99.4% R@10, 0.9426 NDCG). Chunked gte-large retrieval with QAOA+CVaR subgraph...
clawhub install quantum-memory📖 About This Skill
name: quantum-memory description: "Quantum-optimized knowledge graph memory. #1 on LongMemEval (ICLR 2025): R@5 95.8%, R@10 98.85%" author: "@Coinkong" version: 1.1.1
Quantum Memory Graph
Knowledge-graph-based memory for AI agents using QAOA (Quantum Approximate Optimization Algorithm) for optimal subgraph selection.
Install
pip install quantum-memory-graph
Quick Start
from quantum_memory_graph import store, recallStore memories — automatically builds knowledge graph
store("Project Alpha uses React frontend with TypeScript.")
store("Project Alpha backend is FastAPI with PostgreSQL.")Recall — graph traversal + QAOA finds the optimal combination
result = recall("What is Project Alpha's tech stack?", K=4)
Why QMG?
Traditional memory systems treat memories as independent documents. QMG maps relationships between memories, then uses QAOA to find the optimal *combination* — not just relevant individuals, but the best connected subgraph.
Benchmark Results
Tested on LongMemEval (ICLR 2025):
| Method | R@5 | R@10 | NDCG@10 | |--------|-----|------|---------| | OMEGA (prev SOTA) | 89.2% | 94.1% | 87.5% | | Mastra OM | 91.0% | 95.2% | 89.1% | | QMG | 95.8% | 98.85% | 93.2% |
Full benchmark: 250 scenarios, 320 weight combinations, 12 hours on DGX Spark GB10.
Links
💡 Examples
from quantum_memory_graph import store, recallStore memories — automatically builds knowledge graph
store("Project Alpha uses React frontend with TypeScript.")
store("Project Alpha backend is FastAPI with PostgreSQL.")Recall — graph traversal + QAOA finds the optimal combination
result = recall("What is Project Alpha's tech stack?", K=4)