scholarly journals Time-varying quantum channel models for superconducting qubits

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Josu Etxezarreta Martinez ◽  
Patricio Fuentes ◽  
Pedro Crespo ◽  
Javier Garcia-Frias

AbstractThe decoherence effects experienced by the qubits of a quantum processor are generally characterized using the amplitude damping time (T1) and the dephasing time (T2). Quantum channel models that exist at the time of writing assume that these parameters are fixed and invariant. However, recent experimental studies have shown that they exhibit a time-varying (TV) behaviour. These time-dependant fluctuations of T1 and T2, which become even more pronounced in the case of superconducting qubits, imply that conventional static quantum channel models do not capture the noise dynamics experienced by realistic qubits with sufficient precision. In this article, we study how the fluctuations of T1 and T2 can be included in quantum channel models. We propose the idea of time-varying quantum channel (TVQC) models, and we show how they provide a more realistic portrayal of decoherence effects than static models in some instances. We also discuss the divergence that exists between TVQCs and their static counterparts by means of a metric known as the diamond norm. In many circumstances this divergence can be significant, which indicates that the time-dependent nature of decoherence must be considered, in order to construct models that capture the real nature of quantum devices.

2018 ◽  
Vol 38 (8) ◽  
pp. 904-916 ◽  
Author(s):  
Aasthaa Bansal ◽  
Patrick J. Heagerty

Many medical decisions involve the use of dynamic information collected on individual patients toward predicting likely transitions in their future health status. If accurate predictions are developed, then a prognostic model can identify patients at greatest risk for future adverse events and may be used clinically to define populations appropriate for targeted intervention. In practice, a prognostic model is often used to guide decisions at multiple time points over the course of disease, and classification performance (i.e., sensitivity and specificity) for distinguishing high-risk v. low-risk individuals may vary over time as an individual’s disease status and prognostic information change. In this tutorial, we detail contemporary statistical methods that can characterize the time-varying accuracy of prognostic survival models when used for dynamic decision making. Although statistical methods for evaluating prognostic models with simple binary outcomes are well established, methods appropriate for survival outcomes are less well known and require time-dependent extensions of sensitivity and specificity to fully characterize longitudinal biomarkers or models. The methods we review are particularly important in that they allow for appropriate handling of censored outcomes commonly encountered with event time data. We highlight the importance of determining whether clinical interest is in predicting cumulative (or prevalent) cases over a fixed future time interval v. predicting incident cases over a range of follow-up times and whether patient information is static or updated over time. We discuss implementation of time-dependent receiver operating characteristic approaches using relevant R statistical software packages. The statistical summaries are illustrated using a liver prognostic model to guide transplantation in primary biliary cirrhosis.


2002 ◽  
Vol 40 (3) ◽  
pp. 339-345 ◽  
Author(s):  
Daisuke Segawa ◽  
Per-Ove Sjöquist ◽  
Qing-Dong Wang ◽  
Adrian Gonon ◽  
Lars Rydén

Author(s):  
B. L. Boyce ◽  
T. D. Nguyen ◽  
R. E. Jones

Most previous experimental studies and mechanical cornea models have ignored time-dependence of the cornea’s modulus, with only a few notable exceptions [1–3]. The purpose of the present work was to evaluate the time-dependent properties of cornea tissue independent of scleral contributions in a condition that is as physiologically-relevant as possible without resorting to costly and difficult in vivo characterization. A non-contact 3-dimensional displacement mapping tool was employed to image the entire deformation field across the entire cornea in real-time during pressurization. Unlike prior inflation-based studies, the present study’s unique approach permits dynamic real-time full-field mapping of deformation during inflation for the examination of viscoelasticity, isotropy, and homogeneity.


2014 ◽  
Vol 625 ◽  
pp. 229-232 ◽  
Author(s):  
Abul Hassan Ali ◽  
Atif Muhammad Ashraf ◽  
Azmi Mohd Shariff ◽  
Saibal Ganguly

The paper presents the concept of cryogenic growth kinetics during separation of CO2from natural gas using Avrami nucleation model. The interface frost layer on the glass packing of cryogenic bed is assumed asgerm nuclei. The bed porosity is considered time dependent. The expression for time varying bed porosity is derived based on Avrami model. The experimentation was conducted to validate the model and the resulting simulation studies show good resemblance with experimental results.


Author(s):  
Julian W. Gardner ◽  
James A. Covington ◽  
Fauzan Khairi Che Harun

In this chapter, the authors discuss a new concept that involves the development of a new type of sensor array and a new type of time-dependent signal processing method that they call an artificial (or electronic) olfactory mucosa. This so-called e-mucosa employs large sets of spatially distributed odour sensors and gas chromatographic-like retentive micro-columns. It has been inspired by the architecture of the human nose with the olfactory epithelium region located in the upper turbinate. The authors describe the fabrication of an e-mucosa and the use of a convolution method to analyse the time-varying signals generated by it and thus classify different odours. They believe that as this concept evolves it could result in a superior instrument to the sensor-based e-noses currently available today.


HPB ◽  
2019 ◽  
Vol 21 ◽  
pp. S541
Author(s):  
J.A. Vogel ◽  
E. van Veldhuisen ◽  
P.A. Agnass ◽  
J. Crezee ◽  
F. Dijk ◽  
...  

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