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

Many computational problems involve optimization over discrete variables withquadratic interactions. Known as discrete quadratic models (DQMs), theseproblems in general are NP-hard. Accordingly, there is increasing interest inencoding DQMs as quadratic unconstrained binary optimization (QUBO) models toallow their solution by quantum and quantum-inspired hardware witharchitectures and solution methods designed specifically for such problemtypes. However, converting DQMs to QUBO models often introduces invalidsolutions to the solution space of the QUBO models. These solutions must bepenalized by introducing appropriate constraints to the QUBO objective functionthat are weighted by a tunable penalty parameter to ensure that the globaloptimum is valid. However, selecting the strength of this parameter isnon-trivial, given its influence on solution landscape structure. Here, weinvestigate the effects of choice of encoding and penalty strength on thestructure of QUBO DQM solution landscapes and their optimization, focusingspecifically on one-hot and domain-wall encodings.

Discrete quadratic model QUBO solution landscapes