QDesignOptimizer based on ANMod: an automated, physics-guided, multi-parameter design optimizer for superconducting quantum devices

Axel Martin Eriksson Lundström; Lukas Splitthoff; Harsh Vardhan Upadhyay; Pietro Campana; Oscar Lundström; Niranjan Pittan Narendiran; Kunal Dhanraj Helambe; Linus Andersson; Simone Gasparinetti

doi:10.1088/2058-9565/ae7ab6

QDesignOptimizer based on ANMod: an automated, physics-guided, multi-parameter design optimizer for superconducting quantum devices
Journal article, 2026

Designing superconducting quantum circuits involves optimizing the layout to achieve certain target parameters. This optimization process usually depends on iterative electromagnetic simulations, which are computationally expensive and require manual intervention to adjust the layout parameters. 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 approximate nonlinear model-driven (ANMod) parameter updates, which are constructed from the user’s physical knowledge. Additionally, we provide a full implementation using the ANMod-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 quantum-metal (formerly known as Qiskit-Metal). Our implementation supports modular and flexible subsystem-level analysis and is easily extensible to optimize for additional parameters. The ANMod-method is not specific to superconducting circuits; as such, it can be applied to a range of nonlinear optimization problems across science and technology.

ANMod

HFSS

quantum circuits

automated design

energy participation ratio

nonlinear optimization

superconducting circuits