"Integrating Quantum Processor Device and Control Optimization in a Gradient-based Framework"

A quantum processor design workflow goes through multiple steps, each of which plays a crucial role in the final performance of the device. This work demonstrates that the figure of merit reflecting a design goal can be made differentiable for parameters from the device and the control. In addition, we can compute the gradient of the design objective in a single reverse run, then utilize the gradient to optimize the design and the control parameters jointly and efficiently, extending the scope of quantum optimal control to superconducting device design. To the best of our knowledge, this work is the first attempt to extend gradient optimization to superconducting device design. We also demonstrate the viability of reverse gradient-based joint optimization over device and control parameters through a few examples.