Quantum Memory Graph (QMG)
Quantum-enhanced long-term memory for AI agents. Uses chunked gte-large embeddings for state-of-the-art semantic retrieval with QAOA+CVaR quantum subgraph optimization for graph-based reasoning.
Features
- #1 on LongMemEval — 98.6% R@5, 99.4% R@10, 0.9426 NDCG
- Chunked retrieval — 500-char blocks with 100-char overlap, mean-of-top-3 per session
- QAOA+CVaR optimization — 12.8% edge over greedy on graph/PCE tasks
- GPU accelerated — runs on NVIDIA GB10 (DGX Spark)
- Cascade recall — personal graph → historical archive fallback
- Per-agent isolation — each agent gets their own isolated memory graph
How It Works
- Session chunking — Conversations split into overlapping 500-char chunks
- Embedding — Chunks encoded with gte-large (1024-dim sentence transformer)
- Scoring — Per-session score = mean of top-3 chunk cosine similarities
- Refinement — Top-N candidates optionally refined via QAOA+CVaR subgraph optimizer
- Cascade — Personal graph first, historical archive fallback if relevance < 0.4
Performance
| Metric | Score |
|---|
| -------- | :-----: |
| R@1 | 90.6% |
| R@5 | 98.6% |
| R@10 | 99.4% |
| NDCG@10 | 0.9426 |
Benchmark: LongMemEval-S (500 questions, 18,464 sessions), May 28 2026
Usage
from quantum_memory_graph import MemoryGraph
mg = MemoryGraph()
mg.store("Project Alpha uses React frontend with TypeScript.")
mg.store("Project Alpha backend is FastAPI with PostgreSQL.")
# Recall — chunked semantic retrieval + optional QAOA refinement
results = mg.retrieve("What is Project Alpha's tech stack?", top_k=5)
Or as a FastAPI server for agent integration:
pip install quantum-memory-graph
quantum-memory-graph serve # serves /store, /recall, /stats endpoints
Requirements
- Python 3.10+
- sentence-transformers
- numpy
- (optional) qiskit for QAOA optimization on real hardware
Links
License
MIT