QDesignOptimizer based on ANMod: an automated, physics-guided, multi-parameter design optimizer for superconducting quantum devices
Axel Martin Eriksson, Lukas Johannes Splitthoff, Harsh Vardhan Upadhyay, P Campana, Oscar Lundström, Niranjan P Narendiran, Kunal Dhanraj Helambe, Linus Andersson, Simone Gasparinetti
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.
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.
This paper presents an electromagnetic-to-Hamiltonian workflow for modeling a two-qubit coupled transmon superconducting device circuit. The device layout, comprising two coupled transmon qubits with individual readout resonators, is designed and parameterized in Qiskit Metal and analyzed using 3D eigenmode simulations in Ansys HFSS. Adaptive-pass refinement converges the eigenfrequencies and electromagnetic fields, yielding reliable modal data for quantization. Energy-participation ratio values from HFSS are processed through the pyEPR–Qiskit Metal flow to extract effective capacitances, anharmonicities, and qubit–resonator couplings. These parameters define a multimode Duffing–Jaynes–Cummings Hamiltonian that captures the transmon nonlinearities and their interactions with the resonator modes. Frequency-convergence checks and coupling diagnostics verify a consistent, physically accurate mapping from the layout geometry to the device circuit-level model. The Hamiltonian workflow offers a reproducible and scalable framework for modeling and analyzing superconducting transmon architectures in quantum chip design.
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.
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.
This work presents a comprehensive analytical and simulation-based study of superconducting quantum chip architectures, spanning both 2D planar and 3D-integrated designs. We first design and simulate a planar quantum chip layout using Qiskit Metal, which is imported into Ansys HFSS (High Frequency Structure Simulator) to construct a 3D model and perform electromagnetic simulations. A detailed analysis using Driven Modal, Eigenmode, Magnetostatic, and Q3D Capacitance Extraction solvers ensures our simulations accurately reflect physical behavior. The results are validated against analytical models from microwave engineering. pyEPR (Python Energy Participation Ratio) is used to extract key quantum metrics, including eigenfrequencies, quality factors, decoherence times, anharmonicity, cross-Kerr interactions, and participation ratios. Building on this planar design, we extend our study to a 3D-integrated flip-chip architecture featuring two superconducting qubits fabricated on separate high-resistivity silicon substrates. Using HFSS and analytical modeling, we show that detuned qubits maintain stable eigenfrequencies, quality factors, decoherence times, anharmonicity, and cross-Kerr interactions over varying substrate separation values. However, the introduction of dielectric loss tangents reduces quality factors which degrades coherence times and introduces an additional dephasing. These findings highlight the potential of 3D quantum integration, given minimal material imperfections.The planar and 3D designs together form a cohesive investigation into next-generation quantum hardware, with the 2D layout currently taped-out and under fabrication.
We present current developments in Keysight Pathwave Quantum Technology to analyze superconducting quantum circuits. Particularly, Pathwave ADS and its most recent Electromagnetic (EM) simulation package, QuantumPro, enable investigating nonlinear effects such as cavity quantum electrodynamics and automated computation of the quantum parameters including the qubit anharmonicities and cross-Kerr strengths. Under the low power assumption, superconducting qubits are treated as linear lumped circuit elements in the EM simulation environment. To study the nonlinear regime, Pathwave ADS provides a co-simulation approach that enables combining the linear frequency response from the full-wave EM simulation with the nonlinear circuit analysis in the ADS schematic. To assess the accuracy of Pathwave ADS, we compare the results to the two coupled transmons showcase example from the IBM Quantum Qiskit Metal framework. In addition, we also demonstrate power-dependent nonlinear bending of the frequency response by sweeping the input power into the superconducting circuits in Pathwave ADS.
Quantum computing is advancing rapidly, with superconducting quantum computing (SQC) emerging as one of the most promising platforms for scalable quantum information processing. In contrast to fabrication-focused approaches prevalent in current literature, this work presents a fully simulation-based design and tuning methodology for a small-scale superconducting quantum chip - addressing practical limitations posed by fabrication resource constraints - and consequently excludes physical chip fabrication or real-time performance evaluation (e.g., fidelity and error rate measurements). A meticulous, step-by-step electromagnetic simulation was conducted on the chip, designed using Qiskit Metal and Ansys Electronics, with qubit parameters tuned and verified using the Lumped Oscillator Model (LOM) and Energy Participation Ratio (EPR) methods. The design achieves good parameter convergence with relative tuning error rates (Δ%) below 0.05% after 15 adaptive passes and approximately 50,000 solved mesh elements, yielding an Energy Participation Ratio (EPR) of 91.8 % for the chip substrate (Silicon). Furthermore, the study includes a theoretical comparison between two fundamental qubit-qubit coupling schemes. A key contribution of this work lies in systematically analyzing how geometric variations in qubit design affect critical quantum parameters such as qubit frequencies, anharmonicities, coupling strengths, and dispersive shifts - thereby offering a comprehen-sive, simulation-driven framework for early-stage quantum chip prototyping.