Cirq-RAG Code Assistant
Cirq-RAG Code Assistant
A research-grade AI system that translates natural-language prompts into executable, optimized, and validated Cirq quantum circuits using a hybrid RAG multi-agent orchestration pipeline.
Client Needs
Generating quantum computing code using large language models is highly prone to syntax errors and physical inaccuracies. Standard LLMs often fail to produce executable Cirq code, struggle to optimize circuits for NISQ-era hardware constraints (such as minimizing deep two-qubit gates), and lack grounding in specific quantum algorithms, leading to hallucinations.
Working Process
- 01Architected a multi-agent orchestration pipeline with staged execution, coordinating specialized agents: Designer, Validator, Optimizer, and Educational Output generator.
- 02Developed a hybrid RAG retrieval layer combining FAISS/Chroma embedding similarity with custom keyword-topic boost re-ranking to ground the system in verified quantum algorithms.
- 03Built an automated compile-simulate-analyze validation loop that executes the generated Cirq code on local simulators before confirming correctness, with an LLM-assisted self-correction loop.
- 04Implemented a hardware-aware optimization engine combining deterministic Cirq transformations and reinforcement learning-style reward search to reduce gate count and circuit depth.
Check & Launch
The Cirq-RAG Code Assistant achieved an end-to-end task success rate of 92.0% (up from a 52.0% baseline) and improved code validation rates to 90.0%. The optimization engine successfully reduced average circuit depth by ~43% and two-qubit gate usage by ~50%, resulting in highly optimized, executable quantum circuits ready for NISQ-era processors.
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