IDEAL QUANTUM READING OF OPTICAL MEMORIES

2012 ◽  
Vol 10 (08) ◽  
pp. 1241010 ◽  
Author(s):  
MICHELE DALL'ARNO ◽  
ALESSANDRO BISIO ◽  
GIACOMO MAURO D'ARIANO
Keyword(s):  
Optical Memory ◽  
Optimal Strategies ◽  
High Accuracy ◽  
Classical Information ◽  
Minimal Error ◽  
Quantum Properties ◽  
Optical Memories ◽  
Ideal Quantum ◽  
Perfect Discrimination ◽  
The Ideal

Quantum reading is the art of exploiting the quantum properties of light to retrieve classical information stored in an optical memory with low energy and high accuracy. Focusing on the ideal scenario where noise and loss are negligible, we review previous works on the optimal strategies for minimal-error retrieving of information (ambiguous quantum reading) and perfect but probabilistic retrieving of information (unambiguous quantum reading). The optimal strategies largely overcome the optimal coherent protocols (reminiscent of common CD readers), further allowing for perfect discrimination. Experimental proposals for optical implementations of optimal quantum reading are provided.

Quantum reading for the practical man

2014 ◽  
Vol 12 (07n08) ◽  
pp. 1560018
Author(s):  
Michele Dall'Arno
Keyword(s):  
Coherent States ◽  
Optical Memory ◽  
High Energy ◽  
High Accuracy ◽  
Optical Technology ◽  
Probability Of Error ◽  
Classical Information ◽  
Quantum Properties ◽  
Energy Discrimination ◽  
Quantum Optical

Quantum reading is the art of exploiting the quantum properties of light to retrieve classical information stored in an optical memory with low energy and high accuracy. It was shown that the optimal strategy for quantum reading of beamsplitters largely outperforms coherent strategies, further allowing for perfect quantum reading, but requires a source challenging from the experimental viewpoint. Focusing on probes — i.e. BAT states and entangled coherent states (ECS) — which are experimentally feasible with present quantum optical technology, but still allow for perfect quantum reading, we evaluate the tradeoffs between the energy and the probability of error (failure) in ambiguous (unambiguous) quantum reading. It turns out that BAT states outperform ECS in any regime except for the case of high-energy discrimination of two beamsplitters with similar reflectivities.

Optical demonstrations of statistical decision theory for quantum systems

2004 ◽  
Vol 4 (6&7) ◽  
pp. 450-459
Author(s):  
S.M. Barnett
Keyword(s):  
Information Theory ◽  
Communication Systems ◽  
Quantum Information Theory ◽  
Quantum Systems ◽  
Quantum Limit ◽  
Statistical Decision Theory ◽  
Statistical Decision ◽  
Ideal Quantum ◽  
Optical Polarisation ◽  
The Ideal

The work of Holevo and other pioneers of quantum information theory has given us limits on the performance of communication systems. Only recently, however, have we been able to perform laboratory demonstrations approaching the ideal quantum limit. This article presents some of the known limits and bounds together with the results of our experiments based on optical polarisation.

Approaching the ideal quantum key distribution with two-intensity decoy states

2015 ◽  
Vol 92 (2) ◽  
Author(s):  
Chun-Hui Zhang ◽  
Sun-Long Luo ◽  
Guang-Can Guo ◽  
Qin Wang
Keyword(s):  
Quantum Key Distribution ◽  
Key Distribution ◽  
Ideal Quantum ◽  
The Ideal

scholarly journals A kinetic beam scheme for ideal quantum gas dynamics

2006 ◽  
Vol 462 (2069) ◽  
pp. 1553-1572 ◽  
Author(s):  
Jaw-Yen Yang ◽  
Yu-Hsin Shi
Keyword(s):  
Excited States ◽  
Gas Dynamics ◽  
Local Thermodynamic Equilibrium ◽  
Spatial Dimension ◽  
Quantum Gas ◽  
Quantum Distribution ◽  
Spatial Dimensions ◽  
Ideal Quantum ◽  
Bose Einstein ◽  
The Ideal

A novel kinetic beam scheme for the ideal quantum gas is presented for the computation of quantum gas dynamical flows. The quantum Boltzmann equation approach is adopted and the local thermodynamic equilibrium quantum distribution is assumed. Both Bose–Einstein and Fermi–Dirac gases are considered. Formulae for one spatial dimension is first derived and the resulting beam scheme is tested for shock tube flows. Implementation of high-order methods is also outlined. We only consider the system in the normal phase consisting of particles in excited states and both the classical limit and the nearly degenerate limit are computed. The flow structures can all be accurately captured by the present beam scheme. Formulations for multiple spatial dimensions are also included.

A model to estimate the Self-Organizing Maps grid dimension for Prototype Generation

2021 ◽  
Vol 25 (2) ◽  
pp. 321-338
Author(s):  
Leandro A. Silva ◽  
Bruno P. de Vasconcelos ◽  
Emilio Del-Moral-Hernandez
Keyword(s):  
Nearest Neighbor ◽  
High Accuracy ◽  
Classification Performance ◽  
K Nearest Neighbor ◽  
Self Organizing Maps ◽  
Ideal Number ◽  
K Nearest Neighbor Algorithm ◽  
Public Datasets ◽  
The Ideal ◽  
Self Organizing

Due to the high accuracy of the K nearest neighbor algorithm in different problems, KNN is one of the most important classifiers used in data mining applications and is recognized in the literature as a benchmark algorithm. Despite its high accuracy, KNN has some weaknesses, such as the time taken by the classification process, which is a disadvantage in many problems, particularly in those that involve a large dataset. The literature presents some approaches to reduce the classification time of KNN by selecting only the most important dataset examples. One of these methods is called Prototype Generation (PG) and the idea is to represent the dataset examples in prototypes. Thus, the classification process occurs in two steps; the first is based on prototypes and the second on the examples represented by the nearest prototypes. The main problem of this approach is a lack of definition about the ideal number of prototypes. This study proposes a model that allows the best grid dimension of Self-Organizing Maps and the ideal number of prototypes to be estimated using the number of dataset examples as a parameter. The approach is contrasted with other PG methods from the literature based on artificial intelligence that propose to automatically define the number of prototypes. The main advantage of the proposed method tested here using eighteen public datasets is that it allows a better relationship between a reduced number of prototypes and accuracy, providing a sufficient number that does not degrade KNN classification performance.

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