Rapid Simulation Framework for Superconducting Qubit Readout System Inverse Design and Optimization
Publication Date
1-1-2024
Document Type
Conference Proceeding
Publication Title
International Conference on Simulation of Semiconductor Processes and Devices, SISPAD
DOI
10.1109/SISPAD62626.2024.10733335
Abstract
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.
Funding Number
2125906
Funding Sponsor
National Science Foundation
Keywords
Machine Learning, Measurement, Optimizer, Quantum Computing, Readout, Superconducting Qubit
Department
Electrical Engineering
Recommended Citation
Albert Lu and Hiu Yung Wong. "Rapid Simulation Framework for Superconducting Qubit Readout System Inverse Design and Optimization" International Conference on Simulation of Semiconductor Processes and Devices, SISPAD (2024). https://doi.org/10.1109/SISPAD62626.2024.10733335
