Sunkyu Yu^1*, Namkyoo Park^1*

1 Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea

* Corresponding authors emails: sunkyu.yu@snu.ac.kr, nkpark@snu.ac.kr

DOI10.24435/materialscloud:gj-y4 [version v1]

Publication date: Feb 28, 2023

How to cite this record

Sunkyu Yu, Namkyoo Park, Pruning photonic circuits for realizing artificial materials with efficient universal unitary operators, Materials Cloud Archive 2023.30 (2023), doi: 10.24435/materialscloud:gj-y4.

Description

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.

Materials Cloud sections using this data

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Program_HT_Pruning_230224.zip MD5md5:bdb7135918dc1577339c21359814fccc	61.2 KiB	Please see README.txt

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Keywords

photonic circuit pruning deep learning unitary operation