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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

To characterize the dynamical behavior of many-body quantum systems, one isusually interested in the evolution of so-called order-parameters rather thanin characterizing the full quantum state. In many situations, these quantitiescoincide with the expectation value of local observables, such as themagnetization or the particle density. In experiment, however, theseexpectation values can only be obtained with a finite degree of accuracy due tothe effects of the projection noise. Here, we utilize a machine-learningapproach to infer the dynamical generator governing the evolution of localobservables in a many-body system from noisy data. To benchmark our method, weconsider a variant of the quantum Ising model and generate syntheticexperimental data, containing the results of $N$ projective measurements at $M$sampling points in time, using the time-evolving block-decimation algorithm. Aswe show, across a wide range of parameters the dynamical generator of localobservables can be approximated by a Markovian quantum master equation. Ourmethod is not only useful for extracting effective dynamical generators frommany-body systems, but may also be applied for inferring decoherence mechanismsof quantum simulation and computing platforms.

Inferring interpretable dynamical generators of local quantum observables from projective measurements through machine learning