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

arXiv: https://arxiv.org/abs/2112.12509 · HTML: https://mlsanity.com/paper/2112.12509/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 61 references that are in the map.
- First posted on arXiv 23 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient and Modular Implicit Differentiation](https://mlsanity.com/paper/2105.15183/) (2021)

## Abstract (arXiv)

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.