scholarly journals Noise resilience of variational quantum compiling

2020 ◽  
Vol 22 (4) ◽  
pp. 043006 ◽  
Author(s):  
Kunal Sharma ◽  
Sumeet Khatri ◽  
M Cerezo ◽  
Patrick J Coles
Keyword(s):  
Noise Resilience

scholarly journals Evaluating the noise resilience of variational quantum algorithms

2021 ◽  
Vol 104 (2) ◽  
Author(s):  
Enrico Fontana ◽  
Nathan Fitzpatrick ◽  
David Muñoz Ramo ◽  
Ross Duncan ◽  
Ivan Rungger
Keyword(s):  
Quantum Algorithms ◽  
Noise Resilience

scholarly journals Enhanced noise resilience of the surface–Gottesman-Kitaev-Preskill code via designed bias

2020 ◽  
Vol 102 (5) ◽  
Author(s):  
Lisa Hänggli ◽  
Margret Heinze ◽  
Robert König
Keyword(s):  
Noise Resilience

scholarly journals Variational Embedding Multiscale Sample Entropy: A Tool for Complexity Analysis of Multichannel Systems

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 26
Author(s):  
Hongjian Xiao ◽  
Danilo P. Mandic
Keyword(s):  
Real World ◽  
Complexity Analysis ◽  
Structural Complexity ◽  
Conditional Entropy ◽  
Sample Entropy ◽  
Embedding Dimension ◽  
World Systems ◽  
Entropy Methods ◽  
Data Channel ◽  
Noise Resilience

Entropy-based methods have received considerable attention in the quantification of structural complexity of real-world systems. Among numerous empirical entropy algorithms, conditional entropy-based methods such as sample entropy, which are associated with amplitude distance calculation, are quite intuitive to interpret but require excessive data lengths for meaningful evaluation at large scales. To address this issue, we propose the variational embedding multiscale sample entropy (veMSE) method and conclusively demonstrate its ability to operate robustly, even with several times shorter data than the existing conditional entropy-based methods. The analysis reveals that veMSE also exhibits other desirable properties, such as the robustness to the variation in embedding dimension and noise resilience. For rigor, unlike the existing multivariate methods, the proposed veMSE assigns a different embedding dimension to every data channel, which makes its operation independent of channel permutation. The veMSE is tested on both stimulated and real world signals, and its performance is evaluated against the existing multivariate multiscale sample entropy methods. The proposed veMSE is also shown to exhibit computational advantages over the existing amplitude distance-based entropy methods.

scholarly journals How discord underlies the noise resilience of quantum illumination

2016 ◽  
Vol 18 (4) ◽  
pp. 043027 ◽  
Author(s):  
Christian Weedbrook ◽  
Stefano Pirandola ◽  
Jayne Thompson ◽  
Vlatko Vedral ◽  
Mile Gu
Keyword(s):  
Noise Resilience
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