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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose a hybrid quantum-classical approach to model continuous classicalprobability distributions using a variational quantum circuit. The architectureof the variational circuit consists of two parts: a quantum circuit employed toencode a classical random variable into a quantum state, called the quantumencoder, and a variational circuit whose parameters are optimized to mimic atarget probability distribution. Samples are generated by measuring theexpectation values of a set of operators chosen at the beginning of thecalculation. Our quantum generator can be complemented with a classicalfunction, such as a neural network, as part of the classical post-processing.We demonstrate the application of the quantum variational generator using agenerative adversarial learning approach, where the quantum generator istrained via its interaction with a discriminator model that compares thegenerated samples with those coming from the real data distribution. We showthat our quantum generator is able to learn target probability distributionsusing either a classical neural network or a variational quantum circuit as thediscriminator. Our implementation takes advantage of automatic differentiationtools to perform the optimization of the variational circuits employed. Theframework presented here for the design and implementation of variationalquantum generators can serve as a blueprint for designing hybridquantum-classical architectures for other machine learning tasks on near-termquantum devices.

Variational quantum generators: Generative adversarial quantum machine learning for continuous distributions