Integrating quantum processor device and control optimization in a gradient-based framework

Abstract

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. Therefore, our work extends the scope of the quantum optimal control to device design and provides an efficient optimization method. We also demonstrate the viability of gradient-based joint optimization over the device and control parameters through a few examples based on the superconducting qubits.