Optimizing transmon readout with dynamiqs, a library for GPU-accelerated and differentiable quantum simulations (2/2)

Superconducting qubit readout stands out as a main bottleneck of circuit quantum electrodynamics, with the fidelity and length of the operation typically lagging in comparison to state-of-the-art single and two qubit gates. In this two-part talk, we introduce a constant memory numerical method to compute arbitrary gradients of a master equation, that scales favorably with system size and evolution time. This approach enables the realization of quantum optimal control on arbitrary dissipative quantum systems with a large Hilbert space. Here, we demonstrate it on the optimization of a dispersive transmon readout that includes both a readout resonator and a Purcell filter. We obtain robust and interpretable pulses and show a reduction of readout times by up to a factor 2 compared to standard readout for our realistic system parameters. This optimization is achieved using dynamiqs (https://www.dynamiqs.org/), an open-source Python library powered by PyTorch. The library offers GPU-accelerated and differentiable solvers for closed and open quantum systems. It is designed for large-scale quantum simulations, optimal control, and parameter estimation, thus enabling straightforward replication of this work.

In the second part of this talk, we introduce the dynamiqs library and present its main features.