Testing the performance of tensor network renormalization algorithms

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dc.contributor.authorPartridge, Eliza
dc.date.accessioned2025-04-25T15:14:18Z
dc.date.available2025-04-25T15:14:18Z
dc.date.issued2025
dc.description.abstractA variety of tensor network renormalization algorithms programmed in the julia language were compared to determine an optimum algorithm for different use cases. The theory behind these algorithms was also researched. Of the algorithms tested, TRG was the fastest and most accurate algorithm for 2D lattices at high bond dimension. For other cases, results were less clear-cut as different algorithms had different advantages. A more complete ranking of algorithms will require further analysis.
dc.description.reviewstatusReviewed
dc.description.scholarlevelUndergraduate
dc.description.sponsorshipJamie Cassels Undergraduate Research Awards (JCURA)
dc.identifier.urihttps://hdl.handle.net/1828/22016
dc.language.isoen
dc.publisherUniversity Of Victoria
dc.subjectquantum algorithm
dc.subjectrenormalization
dc.subjecttensor network
dc.subjectTRG
dc.subjectplanar graph
dc.subjectIsing
dc.titleTesting the performance of tensor network renormalization algorithms
dc.typePoster

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