Qubit readout is one of the most important operations in quantum computers. In superconducting quantum computers, the success of readout depends on many parameters and is difficult to optimize due to the high dimensionality of the problem. In this work, a rapid simulation framework that comprises an analytical model, a neural network (NN), and optimizers using the NN as a surrogate model is proposed. The analytical model is calibrated to the experimental result and allows rapid simulations to generate enough data to train NNs. Single and multi-objective optimizations are performed. It is shown that a better solution can be found using the optimizer than human optimization. Moreover, the framework can find designs with out-of-the-training-range parameters.
Qubit readout is one of the most important operations in quantum computers. In superconducting quantum computers, the success of readout depends on many parameters and is difficult to optimize due to the high dimensionality of the problem. In this work, a rapid simulation framework that comprises an analytical model, a neural network (NN), and optimizers using the NN as a surrogate model is proposed. The analytical model is calibrated to the experimental result and allows rapid simulations to generate enough data to train NNs. Single and multi-objective optimizations are performed. It is shown that a better solution can be found using the optimizer than human optimization. Moreover, the framework can find designs with out-of-the-training-range parameters.
Qubit readout is a critical part of any quantum computer including the superconducting-qubit-based one. The readout fidelity is affected by the readout pulse width, readout pulse energy, resonator design, qubit design, qubit-resonator coupling, and the noise generated along the readout path. It is thus important to model and predict the fidelity based on various design parameters along the readout path. In this work, a simulation methodology for superconducting qubit readout fidelity is proposed and implemented using Matlab and Ansys HFSS to allow co-optimization in the readout path. As an example, parameters are taken from an actual superconducting-qubit-based quantum computer. Without any calibrations, the model is able to predict the readout error of the system as a function of the readout pulse power. It is found that the system can still maintain high fidelity even if the input power is reduced by 7 dB. This can be used to guide the design and optimization of a superconducting qubit readout system.
Superconducting qubits require rapid and high-fidelity multiplexed readout to enable effective quantum computers. However, predicting the fidelity of the physical implementation of a readout chain is challenging, limiting the success of experimental devices. While analytical tools are currently used to support these design efforts, mapping their outputs into a physical design is difficult and prone to error. In this work, we use multiphysics numerical methods to model the effect of individual component values on fidelity, which can more easily guide the design of a physical device geometry. This procedure is utilized here to optimize a pair of key capacitance values, but it is applicable to broader parameterization of a readout chain.
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.
Superconducting qubits require rapid and high-fidelity multiplexed readout to enable effective quantum computers. However, predicting the fidelity of the physical implementation of a readout chain is challenging, limiting the success of experimental devices. While analytical tools are currently used to support these design efforts, mapping their outputs into a physical design is difficult and prone to error. In this work, we use multiphysics numerical methods to model the effect of individual component values on fidelity, which can more easily guide the design of a physical device geometry. This procedure is utilized here to optimize a pair of key capacitance values, but it is applicable to broader parameterization of a readout chain.
Abstract When physical architectures become too complex for analytical study, numerical simulation proves essential to investigate quantum network behavior. Although highly informative, these simulations involve intricate numerical functions without known analytical forms, making traditional optimization techniques that assume continuity, differentiability, or convexity inapplicable. We introduce a more efficient computational framework that employs machine learning models as surrogates for the objective function. We demonstrate the effectiveness of our approach by applying it to three well-known optimization problems in quantum networking: allocating quantum memory across multiple nodes, tuning an experimental parameter in every physical link of a quantum entanglement switch, and finding effective protocol configurations in a large asymmetric quantum network. Our algorithm consistently outperforms Simulated Annealing and Bayesian optimization within the allotted time, improving results by up to 29% and 28%, respectively. Our framework will thus allow for more comprehensive quantum network studies, integrating surrogate-assisted optimization with existing quantum network simulators.
Designing a qubit architecture is one of the most critical challenges in achieving scalable and fault-tolerant quantum computing as the performance of a quantum computer is heavily dependent on the coherence times, connectivity and low noise environments. Superconducting qubits have emerged as a frontrunner among many competing technologies, primarily because of their speed of operations, relatively well-developed and offer a promising path toward scalability. Here, we address the challenges of optimizing superconducting qubit hardware through the development of a comprehensive theoretical framework that spans the entire process - from design to the calibration of hardware through quantum gate execution. We develop this framework in four key steps: circuit design, electromagnetic analysis, spectral analysis, and pulse sequencing with calibration. We first refine the qubit's core components - such as capacitance, Josephson junctions, and resonators - to set the foundation for strong performance. The electromagnetic analysis, using the Lumped Oscillator model, allows us to map out the capacitance matrix, ensuring that we minimize spectral dispersion while maximizing coherence times. Following this, we conduct spectral analysis to fine-tune the qubit's frequency spectrum and coherence properties, ensuring that the qubit parameters are optimized. Finally, we focus on pulse sequencing, including pulse-width optimization, DRAG optimization, and randomized benchmarking, to achieve high gate fidelity. We applied this framework to both Transmon and Fluxonium qubits, obtaining results that closely match those found in experimental studies. This work provides a detailed and practical approach to the design, optimization, and calibration of superconducting qubits, contributing to the broader effort to develop scalable quantum computing technologies.