SuperGrad: a differentiable simulator for superconducting processors

TL;DR

SuperGrad is a novel differentiable simulator designed for superconducting quantum processors, enabling efficient gradient-based optimization and design improvements in a user-friendly manner.

Contribution

It introduces SuperGrad, the first open-source, differentiable simulator for superconducting processors, facilitating gradient computations for design and control optimization.

Findings

Enables gradient-based optimization of superconducting qubit systems

Provides a flexible interface for Hamiltonian construction and analysis

Demonstrates applications in control, design, and data fitting

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.