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arXiv · 2406.18155

SuperGrad: a differentiable simulator for superconducting processors

Ziang Wang·Feng Wu·Hui-Hai Zhao·Xin Wan·Xiaotong Ni
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Abstract

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.

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Ziang Wang, Feng Wu, Hui-Hai Zhao, Xin Wan, Xiaotong Ni. 2024-06-26. SuperGrad: a differentiable simulator for superconducting processors. https://doi.org/10.22331/q-2025-04-24-1722

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