v1.0.0: Achieving fast and robust perfect entangling gates via RL

First public release of RLQuantOpt, the code accompanying:

L. Grech, M. G. Krauss, M. Consiglio, T. J. G. Apollaro, C. P. Koch, S. Hirlaender and G. Valentino,
"Achieving fast and robust perfect entangling gates via reinforcement learning",
Quantum Sci. Technol. 11, 015030 (2026). doi:10.1088/2058-9565/ae2c16

Contents

• ZCQPEE Gymnasium environment for shaping pulses that implement perfect entangling two-qubit gates on transmons, plus a domain-randomised variant (zc_qpee_wrd.py)
• Stable-Baselines3 training and evaluation scripts
• Quantum optimal control baselines (rlquantopt_mc/)
• Analysis notebooks: Weyl chamber, robustness against Krotov, generalisation sweeps, noisy simulation, training metrics
• Notebooks and data for the paper figures (rlquantopt/paper_plots/)

Notes

• Released under the MIT license. Please cite the paper if you use this code (see CITATION.cff).
• Large generated data (the ~4 GB noisy-simulation state dumps and raw generalisation sweep arrays) is not included. The corresponding notebooks regenerate it.