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/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Natural evolutionary strategies (NES) are a family of gradient-free black-boxoptimization algorithms. This study illustrates their use for the optimizationof randomly-initialized parametrized quantum circuits (PQCs) in the region ofvanishing gradients. We show that using the NES gradient estimator theexponential decrease in variance can be alleviated. We implement two specificapproaches, the exponential and separable natural evolutionary strategies, forparameter optimization of PQCs and compare them against standard gradientdescent. We apply them to two different problems of ground state energyestimation using variational quantum eigensolver (VQE) and state preparationwith circuits of varying depth and length. We also introduce batch optimizationfor circuits with larger depth to extend the use of evolutionary strategies toa larger number of parameters. We achieve accuracy comparable tostate-of-the-art optimization techniques in all the above cases with a lowernumber of circuit evaluations. Our empirical results indicate that one can useNES as a hybrid tool in tandem with other gradient-based methods foroptimization of deep quantum circuits in regions with vanishing gradients.

Natural Evolutionary Strategies for Variational Quantum Computation