Automated, physics-guided, multi-parameter design optimization for superconducting quantum devices

TL;DR

This paper introduces an automated, physics-guided optimization method for superconducting quantum circuits that reduces manual effort and integrates electromagnetic simulations with open-source tools.

Contribution

The authors develop a novel, efficient optimization approach using physics-informed models and provide an open-source Python package for automated superconducting circuit design.

Findings

Significantly reduces manual intervention in circuit design

Integrates electromagnetic simulations with optimization workflows

Supports modular analysis and extensibility

Abstract

The design of nonlinear superconducting quantum circuits often relies on time-consuming iterative electromagnetic simulations requiring manual intervention. These interventions entail, for example, adjusting design variables such as resonator lengths or Josephson junction energies to meet target parameters such as mode frequencies, decay rates, and coupling strengths. Here, we present a method to efficiently automate the optimization of superconducting circuits, which significantly reduces the need for manual intervention. The method's efficiency arises from user-defined, physics-informed, nonlinear models that guide parameter updates toward the desired targets. Additionally, we provide a full implementation of our optimization method as an open-source Python package, QDesignOptimizer. The package automates the design workflow by combining high-accuracy electromagnetic simulations in Ansys HFSS and Energy Participation Ratio (pyEPR) analysis integrated with the design tool Qiskit-Metal. Our implementation supports modular and flexible subsystem-level analysis and is easily extensible to optimize for additional parameters. The method is not specific to superconducting circuits; as such, it can be applied to a range of nonlinear optimization problems across science and technology.