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oai:keep.lib.asu.edu:node-1904172024-12-19T19:04:43Zoai_pmh:alloai_pmh:repo_items

190417 https://hdl.handle.net/2286/R.2.N.190417 http://rightsstatements.org/vocab/InC/1.0/ http://creativecommons.org/licenses/by-nc-sa/4.0 2023-12 18 pages Lynch, Brian De Luca, Gennaro Chen, Yinong Barrett, The Honors College Computer Science and Engineering Program Text The goal of this project is to measure the effects of the use of dynamic circuit technology within quantum neural networks. Quantum neural networks are a type of neural network that utilizes quantum encoding and manipulation techniques to learn to solve a problem using quantum or classical data. In their current form these neural networks are linear in nature, not allowing for alternative execution paths, but using dynamic circuits they can be made nonlinear and can execute different paths. We measured the effects of these dynamic circuits on the training time, accuracy, and effective dimension of the quantum neural network across multiple trials to see the impacts of the nonlinear behavior. Quantum computing Neural Networks Machine learning AI dynamic circuits quantum neural networks Measuring the use of dynamic circuits on performance metrics of Quantum Neural Networks