Zobrazeno 1 - 10
of 115
pro vyhledávání: '"Logan G. Wright"'
Autor:
Alen Senanian, Sridhar Prabhu, Vladimir Kremenetski, Saswata Roy, Yingkang Cao, Jeremy Kline, Tatsuhiro Onodera, Logan G. Wright, Xiaodi Wu, Valla Fatemi, Peter L. McMahon
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-9 (2024)
Abstract Quantum reservoir computing (QRC) has been proposed as a paradigm for performing machine learning with quantum processors where the training takes place in the classical domain, avoiding the issue of barren plateaus in parameterized-circuit
Externí odkaz:
https://doaj.org/article/1f9ad954968f4210b6b235dca912ea81
Autor:
Tianyu Wang, Shi-Yuan Ma, Logan G. Wright, Tatsuhiro Onodera, Brian C. Richard, Peter L. McMahon
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-8 (2022)
Though theory suggests that highly energy efficient optical neural networks (ONNs) based on optical matrix-vector multipliers are possible, an experimental validation is lacking. Here, the authors report an ONN with >90% accuracy image classification
Externí odkaz:
https://doaj.org/article/8f7be604dbfb439da26123da394bbeec
Autor:
Tianyu Wang, Mandar M. Sohoni, Shi-Yuan Ma, Logan G. Wright, Tatsuhiro Onodera, Martin M. Stein, Maxwell Anderson, Brian C. Richard, Peter L. McMahon
Publikováno v:
Emerging Digital Micromirror Device Based Systems and Applications XV.
Autor:
Tianyu Wang, Mandar M. Sohoni, Logan G. Wright, Tatsuhiro Onodera, Shi-Yuan Ma, Maxwell Anderson, Peter L. McMahon
Publikováno v:
AI and Optical Data Sciences IV.
Autor:
Logan G. Wright, Tatsuhiro Onodera, Martin M. Stein, Tianyu Wang, Darren T. Schachter, Zoey Hu, Peter L. McMahon
Publikováno v:
AI and Optical Data Sciences IV.
We experimentally demonstrate multilayer neural networks using ultrafast nonlinear optics, to perform audio and image classification. The proposed framework for constructing and training neural networks is general and applicable to other complex non-
Autor:
Fei Xia, Ziao Wang, Logan G. Wright, Tatsuhiro Onodera, Martin M. Stein, Jianqi Hu, Peter L. McMahon, Sylvain Gigan
Publikováno v:
AI and Optical Data Sciences IV.
Autor:
Logan G. Wright, Tatsuhiro Onodera, Martin M. Stein, Tianyu Wang, Darren T. Schachter, Zoey Hu, Peter L. McMahon
Publikováno v:
Nature
Deep-learning models have become pervasive tools in science and engineering. However, their energy requirements now increasingly limit their scalability1. Deep-learning accelerators2–9 aim to perform deep learning energy-efficiently, usually target
Autor:
Mandar Sohoni, Tianyu Wang, Logan G. Wright, Tatsuhiro Onodera, Martin M. Stein, Shiyuan Ma, Maxwell Anderson, Peter L. McMahon
Publikováno v:
Emerging Topics in Artificial Intelligence (ETAI) 2022.
Photonic simulators using synthetic frequency dimensions have enabled flexible experimental analogues of condensed-matter systems, realizing phenomena that are impractical to observe in real-space systems. However, to date such photonic simulators ha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c158f29db1241cc6d8d52aa36fb061a6
http://arxiv.org/abs/2208.05088
http://arxiv.org/abs/2208.05088
Here, we include the data and code needed to reproduce the figures in "Programmable large-scale simulation of lattices with photonic synthetic frequency dimensions". The data and codes include all that's needed to reproduce the figures in the main-te
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8d8749ecd90558cdc25020dfd87923a9