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

Quantum reinforcement learning (QRL) is one promising algorithm proposed fornear-term quantum devices. Early QRL proposals are effective at solvingproblems in discrete action space, but often suffer from the curse ofdimensionality in the continuous domain due to discretization. To address thisproblem, we propose a quantum Deep Deterministic Policy Gradient algorithm thatis efficient at solving both classical and quantum sequential decision problemsin the continuous domain. As an application, our method can solve the quantumstate-generation problem in a single shot: it only requires a one-shotoptimization to generate a model that outputs the desired control sequence forarbitrary target state. In comparison, the standard quantum control methodrequires optimizing for each target state. Moreover, our method can also beused to physically reconstruct an unknown quantum state.

Quantum reinforcement learning in continuous action space