scholarly journals Symplectic decomposition from submatrix determinants

Author(s):  
Jason L. Pereira ◽  
Leonardo Banchi ◽  
Stefano Pirandola

An important theorem in Gaussian quantum information tells us that we can diagonalize the covariance matrix of any Gaussian state via a symplectic transformation. While the diagonal form is easy to find, the process for finding the diagonalizing symplectic can be more difficult, and a common, existing method requires taking matrix powers, which can be demanding analytically. Inspired by a recently presented technique for finding the eigenvectors of a Hermitian matrix from certain submatrix eigenvalues, we derive a similar method for finding the diagonalizing symplectic from certain submatrix determinants, which could prove useful in Gaussian quantum information.

2011 ◽  
Vol 09 (04) ◽  
pp. 1081-1090
Author(s):  
XIAO-YU CHEN ◽  
LI-ZHEN JIANG

Quantum capacity of the lossy Gaussian quantum channel remains an open problem in quantum information theory, although the upper and lower bounds are well-known. We show that for the n-use of the channel, the input of entangled commutative Gaussian state does not improve the lower bound of the capacity. When the total energy is limited, an unfair distribution of the energy among the n-use will improve the lower bound.


2014 ◽  
Vol 21 (01n02) ◽  
pp. 1440001 ◽  
Author(s):  
Gerardo Adesso ◽  
Sammy Ragy ◽  
Antony R. Lee

The study of Gaussian states has arisen to a privileged position in continuous variable quantum information in recent years. This is due to vehemently pursued experimental realisations and a magnificently elegant mathematical framework. In this paper, we provide a brief, and hopefully didactic, exposition of Gaussian state quantum information and its contemporary uses, including sometimes omitted crucial details. After introducing the subject material and outlining the essential toolbox of continuous variable systems, we define the basic notions needed to understand Gaussian states and Gaussian operations. In particular, emphasis is placed on the mathematical structure combining notions of algebra and symplectic geometry fundamental to a complete understanding of Gaussian informatics. Furthermore, we discuss the quantification of different forms of correlations (including entanglement and quantum discord) for Gaussian states, paying special attention to recently developed measures. The paper is concluded by succinctly expressing the main Gaussian state limitations and outlining a selection of possible future lines for quantum information processing with continuous variable systems.


Vestnik MEI ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 86-94
Author(s):  
Gennadiy F. Filaretov ◽  
◽  
Pavel S. Simonenkov ◽  

The article presents a cumulative sum algorithm intended to detect a sudden step-like change in the probabilistic characteristics of a monitored time series when such a change (“disorder”) is associated with a simultaneous change in both the location characteristics and the dispersion characteristics of the corresponding distribution functions. In the general case of a multidimensional time series, the disorder is associated with a jump in the values of the mathematical expectation vector (the vector of means) and covariance matrix entries. To solve this problem, it is proposed to use a preliminary linear transformation of the time series values, as a result of which the covariance matrix is transformed to the unity form before disordering and to the diagonal form after disordering. The change in the vector of means is analyzed, and the main relations describing the considered detection algorithm are derived. It is noted that by using the above-mentioned linear transformation it is possible to simplify the obtaining of the reference data necessary for synthesizing the monitoring algorithm with the predetermined properties. As an example, a particular case of a one-dimensional time series and a disorder in the form of a simultaneous change in the mean and variance is considered. For this case, reference data obtained by applying the simulation method are given, using which it is possible to find the monitoring algorithm triggering threshold and estimate the average delay time of detecting the specified disorder from the given interval between false alarms. This study is a logical continuation and further development of the approach to construction of multidimensional algorithms for detecting disorders [1].


2003 ◽  
Vol 50 (6-7) ◽  
pp. 901-913 ◽  
Author(s):  
H. Ollivier ◽  
F. Yamaguchi ◽  
M. Brune ◽  
J. M. Raimond ◽  
S. Haroche ◽  
...  

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