Machine-learning-inspired quantum optimal control of nonadiabatic geometric quantum computation via reverse engineering

TL;DR

This paper introduces a machine-learning-inspired method using neural networks to optimize control parameters for nonadiabatic geometric quantum gates, achieving high fidelity and robustness, and enabling scalable multi-qubit gate implementation.

Contribution

The paper presents a novel neural network-based approach for quantum control optimization, improving fidelity and robustness of geometric quantum gates compared to traditional methods.

Findings

Achieved >99.99% fidelity in phase gates

Demonstrated robustness against noise and decoherence

Enabled scalable multi-qubit gate implementation

Abstract

Quantum control plays an irreplaceable role in practical use of quantum computers. However, some challenges have to be overcome to find more suitable and diverse control parameters. We propose a promising and generalizable average-fidelity-based machine-learning-inspired method to optimize the control parameters, in which a neural network with periodic feature enhancement is used as an ansatz. In the implementation of a single-qubit gate by cat-state nonadiabatic geometric quantum computation via reverse engineering, compared with the control parameters in the simple form of a trigonometric function, our approach can yield significantly higher-fidelity ($>99.99\%$) phase gates, such as the $\pi / 8$ gate (T gate). Single-qubit gates are robust against systematic noise, additive white Gaussian noise and decoherence. We numerically demonstrate that the neural network possesses the ability to expand the model space. With the help of our optimization, we provide a feasible way to implement cascaded multi-qubit gates with high quality in a bosonic system. Therefore, the machine-learning-inspired method may be feasible in quantum optimal control of nonadiabatic geometric quantum computation.