Reinforcement Learning for Quantum Transpiling: Research Paper Published
Achieving near-optimal circuit synthesis and routing with RL
Reinforcement Learning for Quantum Transpiling
Excited to share research demonstrating the integration of Reinforcement Learning (RL) into quantum transpiling workflows for the Qiskit transpiler service! 🚀
This work achieves near-optimal circuit synthesis and routing with significant performance improvements over traditional optimization methods like SAT solvers.
Key achievements: ✅ Linear Function, Clifford, and Permutation circuit synthesis up to 65 qubits ✅ Substantial reductions in two-qubit gate depth for routing up to 133 qubits ✅ Performance advantages over SABRE routing heuristics ✅ Practical efficiency for quantum transpiling pipelines
This research represents a major step forward in making quantum computing more efficient and accessible through AI-powered optimization.
Big thanks to the amazing team: David Kremer, VÃctor Villar Pascual, Hanhee Paik, Ivan Duran Martinez, and Ismael Faro!
Read the paper: https://lnkd.in/dcnw4Zav
#qiskit #quantumcomputing #IBMQuantum
Originally shared on LinkedIn on June 17, 2024 - 48 reactions, 0 comments as of 11/12/2025
Juan Cruz-Benito
AI for Quantum Product Owner & Engineering Manager
Building the convergence of AI and Quantum Computing. Product Owner & Engineering Manager @ IBM Quantum