2026-08-02T09:50:43Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:1660 2023-02-28T13:46:44Z community-mcarchive openaire_data

Yu, Sunkyu Park, Namkyoo Yu, Sunkyu Park, Namkyoo 2023-02-28 Achieving high-fidelity photonic circuits for universal unitaries has been a critical issue for classical and quantum computing applications. The basic strategy for realizing U(n) in photonic systems is to find the algorithm to decompose U(n) into a set of SU(2) operations. While various methods have been implemented for such decomposition, the resulting U(n) may not be optimized for high fidelity, especially when we assume noises in the constituent elements. The programs of this archive describe the analysis of achieving the artificial photonic materials having universal unitary operations, quantifying heavy-tailed distributions in photonic circuits and platforms, examining pruning performance in unitary operations, and applying the pruning to deep neural network applications in order to achieve high-fidelity operations. application/zip text/markdown https://doi.org/10.24435/materialscloud:gj-y4 oai:materialscloud.org:1660 mcid:2023.30 eng Materials Cloud https://doi.org/10.48550/arXiv.2208.02251 https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:k0-y2 info:eu-repo/semantics/openAccess Creative Commons Attribution Non Commercial 4.0 International https://creativecommons.org/licenses/by-nc/4.0/legalcode photonic circuit pruning deep learning unitary operation Pruning photonic circuits for realizing artificial materials with efficient universal unitary operators info:eu-repo/semantics/other