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.
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.
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.
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.
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.
Designing a superconducting qubit to realize specific Hamiltonian parameters typically requires iterating through a time and compute-intensive forward loop in which the designer chooses a layout geometry, simulates it, extracts circuit parameters such as capacitances, and refines the geometry. We study the inverse version of this task using a neural-network workflow that maps target Hamiltonian parameters directly to component-level layout parameters, which we subsequently demonstrate on a planar transmon layout. During training, we pair the inverse model with a frozen forward surrogate model and evaluate the loss in Hamiltonian space rather than in layout-parameter space. In validation against a conventional EM solver, 97% of generated designs produce usable geometries, and the inverse-plus-surrogate pipeline reaches mean percent errors of 0.73% for qubit frequency and 1.58% for anharmonicity, comparable to or below the fabrication and simulation-to-measurement uncertainty expected for academic-process transmon devices of this type. A single pipeline query takes ~60 ms on CPU, versus ~2 min for a conventional EM capacitance extraction on the same hardware, a speedup of approximately 2,000x. Batching minimizes the AI model inference overhead, reducing the runtime to 3.1 microseconds per sample on CPU and 2.6 microseconds per sample on GPU at a batch size of 2048, resulting in speedups of 3.9 x 10^7 and 4.6 x 10^7, respectively, relative to a single conventional CPU EM extraction. Our results indicate that component-level inverse design usefully extends and complements conventional EM simulation, including for small datasets on the order of 1,000 samples.
This thesis describes the investigation of superconducting multi-mode cavity architecture for superconducting transmon-based quantum computing. The dissertation highlights useful features of radio-frequency cavities used for quantum computing, with a brief discussion on the advantages of superconducting cavities. In the subsequent section, the concept of transmon is introduced and the mechanism of coupling with a cavity is analyzed. Once the foundations of the topic are laid down, the thesis focuses on optimizing a multi-mode superconducting rf cavity design originally developed for high-energy physics applications. Such section articulates in two main parts, the first part concerning the "bare" cavity remake via finite-elements eigenmode simulations using a computer-aided design software called CST Studio Suite®. In the second part, a transmon qubit is physically inserted into the modified cavity to assess the qubit-cavity coupling of the new design. In evaluating said coupling, two distinct analysis methods are used, namely the black-box quantization method and the energy participation ratio method, both implemented using Ansys® High-Frequency Electromagnetic-Field Simulator, or HFSS™. Results from the two evaluations, compared together, show that the optimized design meets the requisites to be used for quantum computing purposes.
The quest to design a realizable quantum computer is a dream for many for the past few decades. At the moment, one of the promising designs is the transmon qubit, which is a superconducting qubit is the focus of this work. In order to analyze and design different components of a quantum chip, an understanding of the circuitry is required. By varying the circuit parameters, one can design the system according to requirements by constructing the required physical geometry that incorporates the design parameters. In this thesis work, part of the focus was to understand the transmon qubit circuit parameters, extract the circuit parameters such as capacitances from a physical geometry, build analytic and numerical 3D FEM models. Using a circuit model of the qubit system, the interdependence of the resonators that connects qubits with each other can be studied. Their resonances play a role in the qubit Hamiltonian and can be estimated by constructing accurate simulation models. This problem statement has been dealt with in the thesis work by constructing a hybrid circuitry of standard circuit components and 3D FEM simulated qubit unit cells. The quality factor of the measurement readout resonator of a qubit system needs to be characterized in a qubit system in order to target the rate and resolution at which the measurements can be done. In order to do so, the circuit parameters that affect the quality factor needs to be investigated. This problem statement has also been treated in this work.