Characterization of Small Superconducting Rings and Its Possible Application to New Single Flux Quantum Devices

1998 ◽  
Author(s):  
Akinobu Kanda ◽  
Martin C. Geisler ◽  
Masaki Suzuki ◽  
Koji Ishibashi ◽  
Yoshinobu Aoyagi ◽  
...  
1999 ◽  
Author(s):  
Konstantin K. Likharev ◽  
P. Bunyk ◽  
W. Chao ◽  
T. Filippov ◽  
Y. Kameda
Keyword(s):  

2020 ◽  
Vol 30 (7) ◽  
pp. 1-4
Author(s):  
Tiantian Liang ◽  
Guofeng Zhang ◽  
Wentao Wu ◽  
Yongliang Wang ◽  
Lu Zhang ◽  
...  

2016 ◽  
Vol 65 (8) ◽  
pp. 1827-1835 ◽  
Author(s):  
Marco Lorenzo Valerio Tagliaferri ◽  
Alessandro Crippa ◽  
Simone Cocco ◽  
Marco De Michielis ◽  
Marco Fanciulli ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
C.-Y. Pan ◽  
M. Hao ◽  
N. Barraza ◽  
E. Solano ◽  
F. Albarrán-Arriagada

AbstractThe characterization of observables, expressed via Hermitian operators, is a crucial task in quantum mechanics. For this reason, an eigensolver is a fundamental algorithm for any quantum technology. In this work, we implement a semi-autonomous algorithm to obtain an approximation of the eigenvectors of an arbitrary Hermitian operator using the IBM quantum computer. To this end, we only use single-shot measurements and pseudo-random changes handled by a feedback loop, reducing the number of measures in the system. Due to the classical feedback loop, this algorithm can be cast into the reinforcement learning paradigm. Using this algorithm, for a single-qubit observable, we obtain both eigenvectors with fidelities over 0.97 with around 200 single-shot measurements. For two-qubits observables, we get fidelities over 0.91 with around 1500 single-shot measurements for the four eigenvectors, which is a comparatively low resource demand, suitable for current devices. This work is useful to the development of quantum devices able to decide with partial information, which helps to implement future technologies in quantum artificial intelligence.


2003 ◽  
Vol 13 (2) ◽  
pp. 3667-3670 ◽  
Author(s):  
X. Granados ◽  
S. Sena ◽  
E. Bartolome ◽  
A. Palau ◽  
T. Puig ◽  
...  

AIP Advances ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 125122
Author(s):  
Seong Woo Oh ◽  
Artem O. Denisov ◽  
Pengcheng Chen ◽  
Jason R. Petta

2002 ◽  
Vol 15 (6) ◽  
pp. 952-955 ◽  
Author(s):  
P Febvre ◽  
H T$ouml$pfer ◽  
T Ortlepp ◽  
A Kidiyarova-Shevchenko ◽  
G J Gerritsma

2015 ◽  
Vol 91 (2) ◽  
Author(s):  
Jean-Daniel Bancal ◽  
Miguel Navascués ◽  
Valerio Scarani ◽  
Tamás Vértesi ◽  
Tzyh Haur Yang

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