no code implementations • 11 Feb 2021 • Moji Ghadimi, Alexander Zappacosta, Jordan Scarabel, Kenji Shimizu, Erik W Streed, Mirko Lobino
Here we demonstrate the compensation of stray electric fields using a gradient descent algorithm and a machine learning technique, which trained a deep learning network.
Quantum Physics
no code implementations • 5 Feb 2021 • Galan Moody, Volker J. Sorger, Paul W. Juodawlkis, William Loh, Cheryl Sorace-Agaskar, Marcelo Davanco, Lin Chang, John E. Bowers, Niels Quack, Christophe Galland, Igor Aharonovich, M. A. Wolff, C. Schuck, Neil Sinclair, Marko Lončar, Tin Komljenovic, David Weld, Shayan Mookherjea, Sonia Buckley, Marina Radulaski, Stephan Reitzenstein, Benjamin Pingault, Bartholomeus Machielse, Debsuvra Mukhopadhyay, Alexey Akimov, Aleksei Zheltikov, Girish S. Agarwal, Kartik Srinivasan, Juanjuan Lu, Hong X. Tang, Wentao Jiang, Timothy P. McKenna, Amir H. Safavi-Naeini, Stephan Steinhauer, Ali W. Elshaari, Val Zwiller, Paul S. Davids, Nicholas Martinez, Michael Gehl, John Chiaverini, Karan K. Mehta, Jacquiline Romero, Navin B. Lingaraju, Andrew M. Weiner, Daniel Peace, Robert Cernansky, Mirko Lobino, Eleni Diamanti, Luis Trigo Vidarte, Ryan M. Camacho
The reduction in size, weight, power, and improvement in stability that will be enabled by QPICs will play a key role in increasing the degree of complexity and scale in quantum demonstrations.
Quantum Physics
