A General Framework for Gradient-Based Optimization of Superconducting Quantum Circuits using Qubit Discovery as a Case Study
The work introduces a framework to optimize superconducting quantum circuits using gradient-based methods and automatic differentiation within SQcircuit software. It solves the challenge of calculating gradients for large Hamiltonians by using perturbation theory approximations for eigenvalues and eigenvectors, successfully applying this technique to discover qubit designs with superior performance metrics.