The Experts below are selected from a list of 1725 Experts worldwide ranked by ideXlab platform
Qin Wang - One of the best experts on this subject based on the ideXlab platform.
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improved statistical fluctuation analysis for measurement device independent quantum key distribution with four intensity decoy state method
Optics Express, 2018Co-Authors: Xingyu Zhou, Chunmei Zhang, Chunhui Zhang, Qin WangAbstract:Recently Zhang et al [ Phys. Rev. A95, 012333 (2017)] developed a new approach to estimate the failure probability for the decoy-state BB84 QKD system when taking finite-size key effect into account, which offers security comparable to Chernoff Bound, while results in an improved key rate and transmission distance. Based on Zhang et al’s work, now we extend this approach to the case of the measurement-device-independent quantum key distribution (MDI-QKD), and for the first time implement it onto the four-intensity decoy-state MDI-QKD system. Moreover, through utilizing joint constraints and collective error-estimation techniques, we can obviously increase the performance of practical MDI-QKD systems compared with either three- or four-intensity decoy-state MDI-QKD using Chernoff Bound analysis, and achieve much higher level security compared with those applying Gaussian approximation analysis.
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improved statistical fluctuation analysis for measurement device independent quantum key distribution
Physical Review A, 2012Co-Authors: Huajian Ding, Chunmei Zhang, Qin WangAbstract:Measurement-device-independent quantum key distribution (MDI-QKD) is a promising protocol for realizing long-distance secret keys sharing. However, its key rate is relatively low when the finite-size effect is taken into account. In this paper, we consider statistical fluctuation analysis for the three-intensity decoy-state MDI-QKD system based on the recent work (Zhang et al. in Phys Rev A 95:012333, 2017) and further compare its performance with that of applying the Gaussian approximation technique and the Chernoff Bound method. The numerical simulations demonstrate that the new method has apparent enhancement both in key generation rate and transmission distance than using Chernoff Bound method. Meanwhile, the present work still shows much higher security than Gaussian approximation analysis.
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statistical fluctuation analysis for measurement device independent quantum key distribution
Physical Review A, 2012Co-Authors: Huajian Ding, Chunmei Zhang, Chenchen Mao, Qin WangAbstract:Measurement-device-independent quantum key distribution (MDI-QKD) is a promising protocol for realizing long-distance secret keys sharing. However, its key rate is relatively low when the finite-size effect is taken into account. In this paper, we consider statistical fluctuation analysis for the three-intensity decoy-state MDI-QKD system based on the recent work (Zhang et al. in Phys Rev A 95:012333, 2017) and further compare its performance with that of applying the Gaussian approximation technique and the Chernoff Bound method. The numerical simulations demonstrate that the new method has apparent enhancement both in key generation rate and transmission distance than using Chernoff Bound method. Meanwhile, the present work still shows much higher security than Gaussian approximation analysis.
Zhao Song - One of the best experts on this subject based on the ideXlab platform.
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quadratic suffices for over parametrization via matrix Chernoff Bound
arXiv: Learning, 2019Co-Authors: Zhao Song, Xin YangAbstract:We improve the over-parametrization size over two beautiful results [Li and Liang' 2018] and [Du, Zhai, Poczos and Singh' 2019] in deep learning theory.
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a matrix Chernoff Bound for strongly rayleigh distributions and spectral sparsifiers from a few random spanning trees
arXiv: Probability, 2018Co-Authors: Rasmus Kyng, Zhao SongAbstract:Strongly Rayleigh distributions are a class of negatively dependent distributions of binary-valued random variables [Borcea, Branden, Liggett JAMS 09]. Recently, these distributions have played a crucial role in the analysis of algorithms for fundamental graph problems, e.g. Traveling Salesman Problem [Gharan, Saberi, Singh FOCS 11]. We prove a new matrix Chernoff Bound for Strongly Rayleigh distributions. As an immediate application, we show that adding together the Laplacians of $\epsilon^{-2} \log^2 n$ random spanning trees gives an $(1\pm \epsilon)$ spectral sparsifiers of graph Laplacians with high probability. Thus, we positively answer an open question posed in [Baston, Spielman, Srivastava, Teng JACM 13]. Our number of spanning trees for spectral sparsifier matches the number of spanning trees required to obtain a cut sparsifier in [Fung, Hariharan, Harvey, Panigraphi STOC 11]. The previous best result was by naively applying a classical matrix Chernoff Bound which requires $\epsilon^{-2} n \log n$ spanning trees. For the tree averaging procedure to agree with the original graph Laplacian in expectation, each edge of the tree should be reweighted by the inverse of the edge leverage score in the original graph. We also show that when using this reweighting of the edges, the Laplacian of single random tree is Bounded above in the PSD order by the original graph Laplacian times a factor $\log n$ with high probability, i.e. $L_T \preceq O(\log n) L_G$. We show a lower Bound that almost matches our last result, namely that in some graphs, with high probability, the random spanning tree is $\it{not}$ Bounded above in the spectral order by $\frac{\log n}{\log\log n}$ times the original graph Laplacian. We also show a lower Bound that in $\epsilon^{-2} \log n$ spanning trees are necessary to get a $(1\pm \epsilon)$ spectral sparsifier.
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a matrix Chernoff Bound for strongly rayleigh distributions and spectral sparsifiers from a few random spanning trees
Foundations of Computer Science, 2018Co-Authors: Rasmus Kyng, Zhao SongAbstract:Strongly Rayleigh distributions are a class of negatively dependent distributions of binary-valued random variables [Borcea, Branden, Liggett JAMS 09]. Recently, these distributions have played a crucial role in the analysis of algorithms for fundamental graph problems, e.g. Traveling Salesman Problem [Gharan, Saberi, Singh FOCS 11]. We prove a new matrix Chernoff Bound for Strongly Rayleigh distributions. As an immediate application, we show that adding together the Laplacians of e^-2 log^2 n random spanning trees gives an (1± e) spectral sparsifiers of graph Laplacians with high probability. Thus, we positively answer an open question posted in [Baston, Spielman, Srivastava, Teng JACM 13]. Our number of spanning trees for spectral sparsifier matches the number of spanning trees required to obtain a cut sparsifier in [Fung, Hariharan, Harvey, Panigraphi STOC 11]. The previous best result was by naively applying a classical matrix Chernoff Bound which requires e^-2 n log n spanning trees. For the tree averaging procedure to agree with the original graph Laplacian in expectation, each edge of the tree should be reweighted by the inverse of the edge leverage score in the original graph. We also show that when using this reweighting of the edges, the Laplacian of single random tree is Bounded above in the PSD order by the original graph Laplacian times a factor log n with high probability, i.e. L_T ≤ O(log n) L_G. We show a lower Bound that almost matches our last result, namely that in some graphs, with high probability, the random spanning tree is not Bounded above in the spectral order by log n/loglog n times the original graph Laplacian. We also show a lower Bound that in e^-2 log n spanning trees are necessary to get a (1± e) spectral sparsifier.
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a matrix expander Chernoff Bound
Symposium on the Theory of Computing, 2018Co-Authors: Ankit Garg, Zhao Song, Yin Tat Lee, Nikhil SrivastavaAbstract:We prove a Chernoff-type Bound for sums of matrix-valued random variables sampled via a random walk on an expander, confirming a conjecture due to [Wigderson and Xiao 06]. Our proof is based on a new multi-matrix extension of the Golden-Thompson inequality which improves upon the inequality in [Sutter, Berta and Tomamichel 17], as well as an adaptation of an argument for the scalar case due to [Healy 08]. Our new multi-matrix Golden-Thompson inequality could be of independent interest. Secondarily, we also provide a generic reduction showing that any concentration inequality for vector-valued martingales implies a concentration inequality for the corresponding expander walk, with a weakening of parameters proportional to the squared mixing time.
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A Matrix Expander Chernoff Bound
arXiv: Probability, 2017Co-Authors: Ankit Garg, Zhao Song, Yin Tat Lee, Nikhil SrivastavaAbstract:We prove a Chernoff-type Bound for sums of matrix-valued random variables sampled via a random walk on an expander, confirming a conjecture due to Wigderson and Xiao. Our proof is based on a new multi-matrix extension of the Golden-Thompson inequality which improves in some ways the inequality of Sutter, Berta, and Tomamichel, and may be of independent interest, as well as an adaptation of an argument for the scalar case due to Healy. Secondarily, we also provide a generic reduction showing that any concentration inequality for vector-valued martingales implies a concentration inequality for the corresponding expander walk, with a weakening of parameters proportional to the squared mixing time.
Tudor A Marian - One of the best experts on this subject based on the ideXlab platform.
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probing light polarization with the quantum Chernoff Bound
Physical Review A, 2010Co-Authors: Iulia Ghiu, Gunnar Bjork, Paulina Marian, Tudor A MarianAbstract:We recall the framework of a consistent quantum description of polarization of light. Accordingly, the degree of polarization of a two-mode state $\mathrm{\ensuremath{\rho}\ifmmode \hat{}\else \^{}\fi{}}$ of the quantum radiation field can be defined as a distance of a related state ${\mathrm{\ensuremath{\rho}\ifmmode \hat{}\else \^{}\fi{}}}_{b}$ to the convex set of all SU(2)-invariant two-mode states. We explore a distance-type polarization measure in terms of the quantum Chernoff Bound and derive its explicit expression. A comparison between the Chernoff and Bures degrees of polarization leads to interesting conclusions for some particular states chosen as illustrative examples.
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quantum Chernoff Bound as a measure of nonclassicality for one mode gaussian states
Physical Review A, 2009Co-Authors: Muadualina Boca, Iulia Ghiu, Paulina Marian, Tudor A MarianAbstract:We evaluate a Gaussian distance-type degree of nonclassicality for a single-mode Gaussian state of the quantum radiation field by use of the recently discovered quantum Chernoff Bound. The general properties of the quantum Chernoff overlap and its relation to the Uhlmann fidelity are interestingly illustrated by our approach.
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quantum Chernoff Bound as a measure of nonclassicality for one mode gaussian states
Physical Review A, 2009Co-Authors: Muadualina Boca, Iulia Ghiu, Paulina Marian, Tudor A MarianAbstract:Centre for Advanced Quantum Physics, University of Bucharest,P.O.Box MG-11, R-077125 Bucharest-M˘agurele, Romania(Dated: June 30, 2009)We evaluate a Gaussian distance-type degree of nonclassicality for a single-mode Gaussian state ofthe quantum radiation field by use of the recently discovered quantum Chernoff Bound. The generalproperties of the quantum Chernoff overlap and its relation to the Uhlmann fidelity are interestinglyillustrated by our approach.
Chunmei Zhang - One of the best experts on this subject based on the ideXlab platform.
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improved statistical fluctuation analysis for measurement device independent quantum key distribution with four intensity decoy state method
Optics Express, 2018Co-Authors: Xingyu Zhou, Chunmei Zhang, Chunhui Zhang, Qin WangAbstract:Recently Zhang et al [ Phys. Rev. A95, 012333 (2017)] developed a new approach to estimate the failure probability for the decoy-state BB84 QKD system when taking finite-size key effect into account, which offers security comparable to Chernoff Bound, while results in an improved key rate and transmission distance. Based on Zhang et al’s work, now we extend this approach to the case of the measurement-device-independent quantum key distribution (MDI-QKD), and for the first time implement it onto the four-intensity decoy-state MDI-QKD system. Moreover, through utilizing joint constraints and collective error-estimation techniques, we can obviously increase the performance of practical MDI-QKD systems compared with either three- or four-intensity decoy-state MDI-QKD using Chernoff Bound analysis, and achieve much higher level security compared with those applying Gaussian approximation analysis.
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improved statistical fluctuation analysis for measurement device independent quantum key distribution
Physical Review A, 2012Co-Authors: Huajian Ding, Chunmei Zhang, Qin WangAbstract:Measurement-device-independent quantum key distribution (MDI-QKD) is a promising protocol for realizing long-distance secret keys sharing. However, its key rate is relatively low when the finite-size effect is taken into account. In this paper, we consider statistical fluctuation analysis for the three-intensity decoy-state MDI-QKD system based on the recent work (Zhang et al. in Phys Rev A 95:012333, 2017) and further compare its performance with that of applying the Gaussian approximation technique and the Chernoff Bound method. The numerical simulations demonstrate that the new method has apparent enhancement both in key generation rate and transmission distance than using Chernoff Bound method. Meanwhile, the present work still shows much higher security than Gaussian approximation analysis.
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statistical fluctuation analysis for measurement device independent quantum key distribution
Physical Review A, 2012Co-Authors: Huajian Ding, Chunmei Zhang, Chenchen Mao, Qin WangAbstract:Measurement-device-independent quantum key distribution (MDI-QKD) is a promising protocol for realizing long-distance secret keys sharing. However, its key rate is relatively low when the finite-size effect is taken into account. In this paper, we consider statistical fluctuation analysis for the three-intensity decoy-state MDI-QKD system based on the recent work (Zhang et al. in Phys Rev A 95:012333, 2017) and further compare its performance with that of applying the Gaussian approximation technique and the Chernoff Bound method. The numerical simulations demonstrate that the new method has apparent enhancement both in key generation rate and transmission distance than using Chernoff Bound method. Meanwhile, the present work still shows much higher security than Gaussian approximation analysis.
Huajian Ding - One of the best experts on this subject based on the ideXlab platform.
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improved statistical fluctuation analysis for measurement device independent quantum key distribution
Physical Review A, 2012Co-Authors: Huajian Ding, Chunmei Zhang, Qin WangAbstract:Measurement-device-independent quantum key distribution (MDI-QKD) is a promising protocol for realizing long-distance secret keys sharing. However, its key rate is relatively low when the finite-size effect is taken into account. In this paper, we consider statistical fluctuation analysis for the three-intensity decoy-state MDI-QKD system based on the recent work (Zhang et al. in Phys Rev A 95:012333, 2017) and further compare its performance with that of applying the Gaussian approximation technique and the Chernoff Bound method. The numerical simulations demonstrate that the new method has apparent enhancement both in key generation rate and transmission distance than using Chernoff Bound method. Meanwhile, the present work still shows much higher security than Gaussian approximation analysis.
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statistical fluctuation analysis for measurement device independent quantum key distribution
Physical Review A, 2012Co-Authors: Huajian Ding, Chunmei Zhang, Chenchen Mao, Qin WangAbstract:Measurement-device-independent quantum key distribution (MDI-QKD) is a promising protocol for realizing long-distance secret keys sharing. However, its key rate is relatively low when the finite-size effect is taken into account. In this paper, we consider statistical fluctuation analysis for the three-intensity decoy-state MDI-QKD system based on the recent work (Zhang et al. in Phys Rev A 95:012333, 2017) and further compare its performance with that of applying the Gaussian approximation technique and the Chernoff Bound method. The numerical simulations demonstrate that the new method has apparent enhancement both in key generation rate and transmission distance than using Chernoff Bound method. Meanwhile, the present work still shows much higher security than Gaussian approximation analysis.
