Data-driven Hamiltonian correction for qubits for design of gates

TL;DR

This paper introduces a data-driven method to refine the Hamiltonian model of transmon qubits, improving the accuracy of quantum gate predictions by fitting correction terms using real hardware data and machine learning techniques.

Contribution

It presents a novel approach combining experimental data, an ansatz for Hamiltonian correction, and gradient-based optimization to enhance quantum gate modeling.

Findings

Corrected Hamiltonian accurately predicts hardware evolution.

Method improves fidelity of quantum gate simulations.

Effective use of scientific machine learning for quantum system modeling.

Abstract

We obtain correction terms for the standard Hamiltonian of 2 transmons driven by microwaves in cross resonance. Data is obtained from a real transmon system, namely ibm kyiv on the IBM quantum platform. Various data points obtained correspond to different microwave amplitudes and evolution times. We have an ansatz for the correction term and a correction operator whose matrix elements are parameters to be optimized for. We use adjoint sensitivity and gradient descent to obtain these parameters. We see a good fit in the predictions from the corrected Hamiltonian and hardware results demonstrating the effectiveness of scientific machine learning for fine tuning theoretical models to faithfully reproduce observed data on time evolution multiple qubit systems.