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/

Zendy Plus

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

Zendy Tools

Zendy Open

A mixed-integer linear programming (MILP) formulation is presented forparameter estimation of the Potts model. Two algorithms are developed; thefirst method estimates the parameters such that the set of ground statesreplicate the user-prescribed data set; the second method allows the user toprescribe the ground states multiplicity. In both instances, the optimizationprocess ensures that the bandgap is maximized. Consequently, the modelparameter efficiently describes the user data for a broad range oftemperatures. This is useful in the development of energy-based graph models tobe simulated on Quantum annealing hardware where the exact simulationtemperature is unknown. Computationally, the memory requirement in this methodgrows exponentially with the graph size. Therefore, this method can only bepractically applied to small graphs. Such applications include learning ofsmall generative classifiers and spin-lattice model with energy described byIsing hamiltonian. Learning large data sets poses no extra cost to this method;however, applications involving the learning of high dimensional data are outof scope.

Bandgap optimization in combinatorial graphs with tailored ground states: Application in Quantum annealing