English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

Quantum control plays an irreplaceable role in practical use of quantumcomputers. However, some challenges have to be overcome to find more suitableand diverse control parameters. We propose a promising and generalizableaverage-fidelity-based machine-learning-inspired method to optimize the controlparameters, in which a neural network with periodic feature enhancement is usedas an ansatz. In the implementation of a single-qubit gate by cat-statenonadiabatic geometric quantum computation via reverse engineering, comparedwith the control parameters in the simple form of a trigonometric function, ourapproach can yield significantly higher-fidelity ($>99.99\%$) phase gates, suchas the $\pi / 8$ gate (T gate). Single-qubit gates are robust againstsystematic noise, additive white Gaussian noise and decoherence. We numericallydemonstrate that the neural network possesses the ability to expand the modelspace. With the help of our optimization, we provide a feasible way toimplement cascaded multi-qubit gates with high quality in a bosonic system.Therefore, the machine-learning-inspired method may be feasible in quantumoptimal control of nonadiabatic geometric quantum computation.

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