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

A significant challenge in the field of quantum machine learning (QML) is toestablish applications of quantum computation to accelerate common tasks inmachine learning such as those for neural networks. Ridgelet transform has beena fundamental mathematical tool in the theoretical studies of neural networks,but the practical applicability of ridgelet transform to conducting learningtasks was limited since its numerical implementation by conventional classicalcomputation requires an exponential runtime $\exp(O(D))$ as data dimension $D$increases. To address this problem, we develop a quantum ridgelet transform(QRT), which implements the ridgelet transform of a quantum state within alinear runtime $O(D)$ of quantum computation. As an application, we also showthat one can use QRT as a fundamental subroutine for QML to efficiently find asparse trainable subnetwork of large shallow wide neural networks withoutconducting large-scale optimization of the original network. This applicationdiscovers an efficient way in this regime to demonstrate the lottery tickethypothesis on finding such a sparse trainable neural network. These resultsopen an avenue of QML for accelerating learning tasks with commonly usedclassical neural networks.

Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation