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arXiv:2207.08394 · Solid-State Electronics

A simulation methodology for superconducting qubit readout fidelity

Hiu Yung Wong, Prabjot Dhillon, Kristin M. Beck, Yaniv Jacob Rosen

Published Dec 29, 2022• 10 citations
arXivHTMLPDF

Abstract

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.

DOI
10.1016/j.sse.2022.108582

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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.
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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.
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.
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 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.