Optimization of Controlled-Z Gate with Data-Driven Gradient Ascent Pulse Engineering in a Superconducting Qubit System

TL;DR

This paper demonstrates the successful experimental optimization of a two-qubit controlled-Z gate in a superconducting system using data-driven gradient ascent pulse engineering, achieving high fidelity.

Contribution

It introduces and experimentally validates two data-driven GRAPE protocols for optimizing CZ gates in superconducting qubits, focusing on operator and state fidelity.

Findings

Gate fidelity around 99% after optimization

Effective improvement with imperfect initial pulses

Validation of data-driven GRAPE protocols in experiments

Abstract

The experimental optimization of a two-qubit controlled-Z (CZ) gate is realized following two different data-driven gradient ascent pulse engineering (GRAPE) protocols in the aim of optimizing the gate operator and the output quantum state, respectively. For both GRAPE protocols, the key computation of gradients utilizes mixed information of the input Z-control pulse and the experimental measurement. With an imperfect initial pulse in a flattop waveform, our experimental implementation shows that the CZ gate is quickly improved and the gate fidelities subject to the two optimized pulses are around 99%. Our experimental study confirms the applicability of the data-driven GRAPE protocols in the problem of the gate optimization.