We present an open-source database of superconducting quantum device designs that may be used as the starting point for customized devices. Each design can be generated programmatically using the open-source Qiskit Metal package, and simulated using finite-element electromagnetic solvers. We present a robust workflow for achieving high accuracy on design simulations. Many designs in the database are experimentally validated, showing excellent agreement between simulated and measured parameters. Our database includes a front-end interface that allows users to generate ``best-guess'' designs based on desired circuit parameters. This project lowers the barrier to entry for research groups seeking to make a new class of devices by providing them a well-characterized starting point from which to refine their designs.
We present an open-source database of superconducting quantum device designs that may be used as the starting point for customized devices. Each design can be generated programmatically using the open-source Qiskit Metal package, and simulated using finite-element electromagnetic solvers. We present a robust workflow for achieving high accuracy on design simulations. Many designs in the database are experimentally validated, showing excellent agreement between simulated and measured parameters. Our database includes a front-end interface that allows users to generate ``best-guess'' designs based on desired circuit parameters. This project lowers the barrier to entry for research groups seeking to make a new class of devices by providing them a well-characterized starting point from which to refine their designs.
Abstract 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.
Electromagnetic simulations form an indispensable part of the design and optimization process for superconducting quantum devices. Although several commercial platforms exist, open-source alternatives optimized for high-performance computing remain limited. To address this gap, we introduce SQDMetal, a Python-based API that integrates Qiskit Metal (IBM), Gmsh, Palace (AWS), and Paraview (Kitware) into an open-source, highly parallel simulation workflow for superconducting quantum circuits. SQDMetal enables accurate, efficient, and scalable simulations while remaining community-driven and free from commercial constraints. In this work, we validate SQDMetal through mesh convergence studies which benchmark SQDMetal against COMSOL Multiphysics and Ansys, demonstrating excellent agreement for both eigenmode and electrostatic (capacitance) simulations. Furthermore, we simulate superconducting resonators and transmon qubits, showing reasonable agreement with experimental measurements. SQDMetal also supports advanced capabilities, including Hamiltonian extraction via the energy participation ratio (EPR) method, incorporation of kinetic inductance effects, and full 3D modelling of device geometry for improved predictive accuracy. By unifying open-source tools into a single framework, SQDMetal lowers the barriers to entry for community members seeking to access high-performance simulations to assist in the design and optimization of their devices.
Accurate electromagnetic (EM) feature extraction, including element characterization, eigenmodes, and field distributions, is essential for superconducting quantum circuit design. To streamline this process, we present a workflow built around Palace, an open-source, high-performance finite element method solver tailored for quantum applications. Starting from circuit layouts, the workflow automates mesh generation, multiple EM solver processing, and EM-to-Hamiltonian post-processing. We benchmark the workflow on a chip with bare resonators and qubits coupled with readout resonators, achieving resonator frequencies prediction within 0.3% and 3 out of 4 external couplings within 16% of cryogenic measurements. These results demonstrate open-source EM tools can match commercial accuracy while offering scalable, license-free analysis. Our Palace-based workflow provides an accessible and extensible foundation for rapid superconducting circuit development and materials-loss studies.
We propose SPICE-Q, a SPICE-inspired design-technology co-optimization framework for superconducting quantum processors. Rather than replacing tools such as HFSS, Qiskit Metal, pyEPR, SQcircuit, SQuADDS, scqubits, or QuTiP, SPICE-Q aims to connect them through a unified, traceable data chain spanning process rules, layout, electromagnetic simulation, energy-participation-ratio and circuit quantization, Hamiltonian extraction, noise analysis, cryogenic test, and manufacturing feedback. The central mapping is from process and PDK constraints to layout geometry, electromagnetic modes, equivalent circuit parameters, effective Hamiltonians, and finally metrics such as frequency, coupling, anharmonicity, decoherence, readout performance, and yield. This flow must capture Josephson-junction variability, transmon frequency allocation, resonator and Purcell constraints, coupler crosstalk, microwave routing, 3D interconnects, material/interface loss, package modes, and wafer-scale process statistics. By introducing standardized model interfaces, statistical parameter models, model cards, version governance, and closed-loop calibration from cryogenic and fabrication data, SPICE-Q frames superconducting quantum-chip design as an engineering workflow rather than a collection of isolated simulations. We argue that scalable and fault-tolerant quantum processors will require such a continuous model chain from device physics and electromagnetic fields to quantum dynamics, noise, manufacturability, and system-level yield.
Translating superconducting quantum chip layouts into physically meaningful simulation records is a critical step in quantum hardware development, yet current datasets rarely capture the electromagnetic (EM) effects involved in this process. We propose SQChip-EM, an open layout to EM simulation dataset based on Qiskit Metal that links parameterized layout designs to Ansys HFSS and Q3D simulation results. Unlike abstract circuit datasets, SQChip-EM preserves layout dependent EM evidence through GDSII files, raw simulation outputs, and JSON annotations covering frequencies, anharmonicity, dispersive shifts, and coupling parameters. These annotations form reusable simulation level ''virtual chip'' records for downstream workflows such as initialization, calibration tuning, and control simulation. SQChip-EM also includes three illustrative benchmark tasks, including design to metric regression, specification feasibility classification, and inverse design retrieval, together with one downstream workflow use case. By releasing data, code, and evaluation protocols, SQChip-EM provides a standardized data resource for ML assisted quantum electronic design automation.
Superconducting quantum circuit design is reliant on accurately mapping design parameters to a quantum Hamiltonian. Designers typically rely on computationally intensive finite-element electromagnetic simulations. However, effective circuit-level models can in principle capture much of the relevant physics for these systems, reducing the need for finite-element simulations. A barrier to implementing these simpler models has been the prevalence of distributed elements such as coplanar waveguides resonators in device designs. Techniques for modeling these distributed elements have been developed, but may be difficult to scale, reduce intuition, and give inaccurate results under strong coupling. In this work we describe a simple effective circuit-level model that faithfully reproduces the scattering parameters, and subsequently can predict certain Hamiltonian parameters, for a coupled, distributed element within a broader two-port network even up to strong coupling. Our approach does not explicitly require an electromagnetic simulation. Our model, along with other common lumped models used in black box quantization, is publicly available to researchers via an open-source code package, simpleLOMs.