One significant advantage of superconducting processors is their extensive design flexibility, which encompasses various types of qubits and interactions. Given the large number of tunable parameters of a processor, the ability to perform gradient optimization would be highly beneficial. Efficient backpropagation for gradient computation requires a tightly integrated software library, for which no open-source implementation is currently available. In this work, we introduce SuperGrad, a simulator that accelerates the design of superconducting quantum processors by incorporating gradient computation capabilities. SuperGrad offers a user-friendly interface for constructing Hamiltonians and computing both static and dynamic properties of composite systems. This differentiable simulation is valuable for a range of applications, including optimal control, design optimization, and experimental data fitting. In this paper, we demonstrate these applications through examples and code snippets.
@article{arxiv.2406.18155,
title = {SuperGrad: a differentiable simulator for superconducting processors},
author = {Ziang Wang and Feng Wu and Hui-Hai Zhao and Xin Wan and Xiaotong Ni},
journal= {arXiv preprint arXiv:2406.18155},
year = {2025}
}
Comments
25 pages, 8 figures, 5 tables, the code is available at https://github.com/iqubit-org/supergrad