Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Tatsuhiro Onodera"'
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:
Krishanu Sankar, Artur Scherer, Satoshi Kako, Sam Reifenstein, Navid Ghadermarzy, Willem B. Krayenhoff, Yoshitaka Inui, Edwin Ng, Tatsuhiro Onodera, Pooya Ronagh, Yoshihisa Yamamoto
Publikováno v:
npj Quantum Information, Vol 10, Iss 1, Pp 1-21 (2024)
Abstract We study the performance scaling of three quantum algorithms for combinatorial optimization: measurement-feedback coherent Ising machines (MFB-CIM), discrete adiabatic quantum computation (DAQC), and the Dürr–Høyer algorithm for quantum
Externí odkaz:
https://doaj.org/article/a7149b52594e4d1692d46edd650d9c81
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:
Edwin Ng, Tatsuhiro Onodera, Satoshi Kako, Peter L. McMahon, Hideo Mabuchi, Yoshihisa Yamamoto
Publikováno v:
Physical Review Research, Vol 4, Iss 1, p 013009 (2022)
We show that the nonlinear stochastic dynamics of a measurement-feedback-based coherent Ising machine (MFB-CIM) in the presence of quantum noise can be exploited to sample degenerate ground and low-energy spin configurations of the Ising model. We fo
Externí odkaz:
https://doaj.org/article/ba04a8922b1d44cea63e47fba78b5ffe
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.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f79cc9c1ad6a94dd2ce89ce14d79f63
https://doi.org/10.1117/12.2649385
https://doi.org/10.1117/12.2649385
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.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::befbfc10a6e90c0995fb1596ecc7f11f
https://doi.org/10.1117/12.2650289
https://doi.org/10.1117/12.2650289
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-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::440e584b1c2bc0bedfe1f64cbef3b46f
https://doi.org/10.1117/12.2648103
https://doi.org/10.1117/12.2648103
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.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6e204c6bbfb8b3a06ae89526d733ab6b
https://doi.org/10.1117/12.2646861
https://doi.org/10.1117/12.2646861
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c1772ee7c92b096487064e4b035e7b6
http://europepmc.org/articles/PMC8791835
http://europepmc.org/articles/PMC8791835
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.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ff927c1a2774f73be28377769f81c7ea
https://doi.org/10.1117/12.2646545
https://doi.org/10.1117/12.2646545