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

TL;DR

This paper presents a gradient-based framework for jointly optimizing quantum processor device design and control parameters, enabling more efficient and scalable improvements in quantum operation quality.

Contribution

It introduces a differentiable figure of merit and an efficient gradient computation method for simultaneous device and control optimization in quantum processors.

Findings

Gradient-based joint optimization is feasible and effective.

The method extends quantum optimal control to device design.

Demonstrated improvements in quantum operation quality.

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. 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.