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#

1. Session chunking — Conversations split into overlapping 500-char chunks
2. Embedding — Chunks encoded with gte-large (1024-dim sentence transformer)
3. Scoring — Per-session score = mean of top-3 chunk cosine similarities
4. Refinement — Top-N candidates optionally refined via QAOA+CVaR subgraph optimizer
5. Cascade — Personal graph first, historical archive fallback if relevance < 0.4

Performance#

MetricScoreR@190.6%**R@5****98.6%**R@1099.4%NDCG@100.9426

Benchmark: LongMemEval-S (500 questions, 18,464 sessions), May 28 2026

Usage#

python

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:

bash

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#

• GitHub: https://github.com/Dustin-a11y/quantum-memory-graph
• PyPI: https://pypi.org/project/quantum-memory-graph/

License#

MIT