Reinforcement Learning for Quantum Transpiling: Research Paper Published
Reinforcement Learning for Quantum TranspilingExcited 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, Víctor 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
I research and develop AI-based systems to tackle complex problems in quantum computing and other relevant areas.
My work sits at the intersection of quantum computing, machine learning, and open source. I hold IBM Master Inventor status and have contributed to tools used by millions of developers worldwide.
I have a PhD in Computer Engineering from the University of Salamanca (2018), have authored 80+ publications, and received the 2019 SCIE-BBVA Award for best young researcher in Computer Science in Spain.