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 present a method to model a discretized time evolution of probabilisticnetworks on gate-based quantum computers. We consider networks of nodes, whereeach node can be in one of two states: good or failed. In each time step,probabilities are assigned for each node to fail (switch from good to failed)or to recover (switch from failed to good). Furthermore, probabilities areassigned for failing nodes to trigger the failure of other, good nodes. Ourmethod can evaluate arbitrary network topologies for any number of time steps.We can therefore model events such as cascaded failure and avalanche effectswhich are inherent to financial networks, payment and supply chain networks,power grids, telecommunication networks and others. Using quantum amplitudeestimation techniques, we are able to estimate the probability of anyconfiguration for any set of nodes over time. This allows us, for example, todetermine the probability of the first node to be in the good state after thelast time step, without the necessity to track intermediate states. We presentthe results of a low-depth quantum amplitude estimation on a simulator with arealistic noise model. We also present the results for running this example onthe AQT quantum computer system PINE. Finally, we introduce an error model thatallows us to improve the results from the simulator and from the experiments onthe PINE system.

Quantum amplitude estimation with error mitigation for time-evolving probabilistic networks