shield_lock
JinDaGe
← Back to skills
extension
Category: OtherNo API key required

Quantum Agent Memory

QAOA-powered memory optimization for AI agents. Uses quantum computing (Qiskit) to solve three memory management problems: clustering related memories, selec...

personAuthor: dustin-a11yhubclawhub
open_in_newRepositorydownloadDownload package

Quantum Agent Memory

QAOA-optimized memory management for AI agents. Three quantum layers replace classical heuristics for clustering, compaction, and recall.

Quick Start

git clone https://github.com/Dustin-a11y/quantum-agent-memory.git
cd quantum-agent-memory
python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt
python -m quantum_agent_memory benchmark

Three Layers

Layer 1: Clustering

Group N memories into coherent clusters via balanced graph-cut QAOA.

  • Cost matrix: temporal (25%), relational (30%), categorical (25%), recency (20%)
  • 100% optimal for n≤14, speed crossover at n=20

Layer 2: Compaction

Select optimal K memories to keep from M total.

  • Maximizes coverage + coherence + value + recency with budget penalty
  • Beats greedy selection by ~1% consistently

Layer 3: Recall

Find the best K memories for a query — optimizes for synergy, not just individual relevance.

  • Finds memory combinations that Top-K similarity search misses
  • Individual relevance (40%) + pairwise synergy (30%) + diversity (20%) + recency (10%)

Integration with Mem0

Point the benchmark at a live Mem0 instance:

python -m quantum_agent_memory benchmark --mem0-url http://localhost:8500

For OpenClaw agent integration, see references/openclaw-plugin.md.

IBM Quantum Hardware

Submit circuits to real IBM quantum processors (free tier: 10 min/month):

pip install qiskit-ibm-runtime
python -m quantum_agent_memory submit --ibm-token YOUR_TOKEN

For scheduled hardware runs, see scripts/ibm_cron.py.

API Server

Run as a FastAPI server for live agent integration:

python scripts/quantum_api.py
# Endpoints: GET /, POST /quantum-recall, POST /quantum-compact

See references/api-setup.md for systemd service configuration and auth.

Benchmarking

Run the full 3-layer benchmark:

python -m quantum_agent_memory benchmark

Results save as JSON to results/benchmark_TIMESTAMP.json. Expected output:

  • Clustering: ~98-100% optimal
  • Compaction: 100% optimal
  • Recall: 100% optimal, quantum finds synergistic combos Top-K misses
  • Avg accuracy: ~99.7%

File Reference

  • scripts/ibm_cron.py — scheduled IBM hardware submission script
  • scripts/quantum_api.py — FastAPI server for quantum recall/compact endpoints
  • references/openclaw-plugin.md — OpenClaw mem0-bridge plugin integration guide
  • references/api-setup.md — API server setup, systemd, and auth configuration
  • references/whitepaper.md — full technical whitepaper