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Isaac L. Chuang
Researcher at Massachusetts Institute of Technology
Publications - 318
Citations - 70398
Isaac L. Chuang is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Quantum computer & Quantum information. The author has an hindex of 64, co-authored 299 publications receiving 65269 citations. Previous affiliations of Isaac L. Chuang include Bell Labs & University of California, Santa Barbara.
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Journal Article
Effects of electrode surface roughness on motional heating of trapped ions
TL;DR: In this paper, the effects of rough surface curvature on electric-field noise were investigated by deriving a rough surface Green's function and evaluating its effects on adsorbate-surface binding energies.
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6.00x Introduction to Computer Science and Programming MITx on edX Course Report - 2013 Spring
Daniel T. Seaton,Justin Reich,Sergiy O. Nesterko,Tommy Mullaney,James H. Waldo,Andrew D. Ho,Isaac L. Chuang +6 more
TL;DR: This report describes 6.00x: Introduction to Computer Science and Programming, one of the first 11 courses offered by MITx on edX, a platform for delivering massive open online courses (MOOCs).
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A cryogenic surface-electrode elliptical ion trap for quantum simulation
TL;DR: In this paper, a surface-electrode elliptical ion trap is proposed to produce a 2D ion crystal and is amenable to microfabrication, which would enable higher simulated coupling rates, as well as interactions based on magnetic forces generated by currents which may be incorporated into the trap structure.
Architectures for Multinode Superconducting Quantum Computers
James A. Ang,Gabriella Carini,Yanzuo Chen,Isaac L. Chuang,Michael A. DeMarco,Sophia E. Economou,Alec Eickbusch,Andrei Faraon,Kai-Mei C. Fu,Steven Girvin,Michael Hatridge,Andrew Houck,P. Hilaire,Kevin Krsulich,Ang Li,Chenxu Liu,Yuan Liu,Margaret Martonosi,David McKay,James A. Misewich,Marc K. Ritter,Robert Schoelkopf,Samuel A. Stein,Sara Sussman,Hong X. Tang,Weihua Tang,Teague Tomesh,Norm M. Tubman,Chen Wang,Nathan Wiebe,Yongxin Yao,Dillon C. Yost,Yiyu Zhou +32 more
TL;DR: In this paper , the authors employ a co-design inspired approach to quantify overall multinode quantum computer performance in terms of hardware models of internode links, entanglement distillation, and local architecture.
Learnability for the Information Bottleneck
TL;DR: In this paper, the authors define the concept of IB-learnability and prove several sufficient conditions for IB-Learnability, which provides theoretical guidance for choosing a good β. And they further show that IBlearnability is determined by the largest confident, typical and imbalanced subset of the examples (the conspicuous subset).