Graph RL router boosts quantum circuit fidelity using calibration data

Researchers have developed a new quantum circuit routing method using graph reinforcement learning that incorporates calibration data from quantum processors. This approach, trained with proximal policy optimization and evaluated on the Munich Quantum Toolkit Bench circuits, achieved a mean exact fidelity of 0.727. This significantly outperforms standard methods like SABRE-best20 (0.440) and target-aware SABRE (0.481), particularly for smaller qubit families, by optimizing routes based on real-time hardware calibration. The study demonstrates that calibration-aware routing can enhance quantum circuit fidelity beyond traditional gate-count-driven compilation. AI

IMPACT This research could lead to more efficient and accurate quantum computations by improving the compilation process for quantum programs.

RANK_REASON Academic paper detailing a new method for quantum circuit routing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• IBM Heron
• MQT Bench
• Munich Quantum Toolkit
• proximal policy optimization
• Sabre
• SABRE-best20
• Yash Tomar V

• paper
• infra

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →