Quantum Memory Graph

Quantum-optimized memory retrieval for AI agents. Use when building agent memory systems, replacing Mem0/LangChain memory, or needing relationship-aware reca...

This skill is instruction-only and points you to install an external PyPI package (quantum-memory-graph) and to optionally use IBM quantum hardware and a shared API server. Before installing or deploying: 1) Inspect the actual PyPI package source (or the project's repo) to verify what code will run, which endpoints it contacts, and how it persists data. 2) Don’t run the package or shared server on systems containing private data until you confirm isolation and authorization controls—shared servers explicitly make different agents' memories available to each other. 3) Treat IBM_QUANTUM_TOKEN and any database credentials as sensitive; the metadata didn’t declare them even though the docs reference them. 4) Validate benchmarking claims and model/extra dependencies (large models require substantial RAM/GPU). If you can’t review the package source, run it in an isolated environment (container or dedicated VM) and avoid migrating sensitive production data there.

Type: OpenClaw Skill Name: quantum-memory Version: 0.4.0 The quantum-memory skill bundle provides instructions and documentation for integrating a relationship-aware memory system into AI agents using the 'quantum-memory-graph' library. It includes triggers for the agent, installation commands, and usage examples for memory storage and retrieval using knowledge graphs and QAOA optimization. The files (SKILL.md, deployment.md, and models.md) contain no evidence of malicious intent, data exfiltration, or harmful prompt injections, and the requested permissions (like the IBM Quantum token) are consistent with the stated functionality.