qiskit-ibm-transpiler

projects

AI-powered quantum circuit optimization that outperforms traditional heuristics. Uses reinforcement learning to achieve near-optimal synthesis of Linear Function, Clifford, and Permutation circuits—orders of magnitude faster than SAT solvers. Our Pauli Network synthesis delivers over 2× reduction in two-qubit gate count, with average improvements of 20% and up to 60% on the Benchpress benchmark.

Supports hardware-aware routing up to 133 qubits and works as a drop-in replacement for standard Qiskit transpilation. Available as both local execution (with our open-source RL models) and cloud-based services.

Achievements:

Quantum Computing AI Open Source Reinforcement Learning
Juan Cruz-Benito
Authors
Juan Cruz-Benito (he/him)
Quantum+AI Manager

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.