Integrating Quantum Processor Device and Control Optimization in a Gradient-based Framework
This work addresses the problem of efficiently exploring large design spaces for quantum processors, which is incremental as it extends existing gradient-based methods to include device parameters.
The authors tackled the challenge of optimizing both device design and control parameters in quantum processors by making the figure of merit differentiable and computing gradients efficiently, similar to back-propagation, enabling joint optimization and extending quantum optimal control to superconducting device design.
In a quantum processor, the device design and external controls together contribute to the quality of the target quantum operations. As we continuously seek better alternative qubit platforms, we explore the increasingly large device and control design space. Thus, optimization becomes more and more challenging. In this work, we demonstrate that the figure of merit reflecting a design goal can be made differentiable with respect to the device and control parameters. In addition, we can compute the gradient of the design objective efficiently in a similar manner to the back-propagation algorithm and then utilize the gradient to optimize the device and the control parameters jointly and efficiently. This extends the scope of the quantum optimal control to superconducting device design. We also demonstrate the viability of gradient-based joint optimization over the device and control parameters through a few examples.