Automatic multi-parameter design optimization for superconducting quantum devices
ORAL
Abstract
We present an automated framework for the precise optimization towards user-specified Hamiltonian parameter targets in superconducting quantum circuits, crucial for both small and large-scale quantum implementations.
Our design optimization package extends Qiskit Metal’s popular design environment and the ANSYS HFSS solver, to enable an efficient multi-component circuit design of qubits, resonators, and their linear and non-linear couplings, also to readout and control lines.
Through a combination of eigenmode analysis, capacitance extraction, and participation ratio studies, we realize an iterative physics-guided multi-parameter optimization wrapping around high-accuracy simulations and accounting for potential parameter interdependencies. The framework can break large-scale circuit layouts into smaller studies for efficient simulations on desktop computers.
Additionally, we provide a practical example of a multi-qubit chip, complementing our online available design optimization package.
The package should offer an accessible, low-effort entry point for students and researchers designing superconducting quantum circuits.
Our design optimization package extends Qiskit Metal’s popular design environment and the ANSYS HFSS solver, to enable an efficient multi-component circuit design of qubits, resonators, and their linear and non-linear couplings, also to readout and control lines.
Through a combination of eigenmode analysis, capacitance extraction, and participation ratio studies, we realize an iterative physics-guided multi-parameter optimization wrapping around high-accuracy simulations and accounting for potential parameter interdependencies. The framework can break large-scale circuit layouts into smaller studies for efficient simulations on desktop computers.
Additionally, we provide a practical example of a multi-qubit chip, complementing our online available design optimization package.
The package should offer an accessible, low-effort entry point for students and researchers designing superconducting quantum circuits.
–
Publication: Eriksson et al., Automatic multi-parameter design optimization for superconducting quantum devices, github (https://github.com/202Q-lab/QDesignOptimizer) (2024)
Presenters
-
Lukas J Splitthoff
- Chalmers University of Technology
- Delft University of Technology