scholarly journals CMOS charge qubits and qudits: entanglement entropy and mutual information as an optimization method to construct CNOT and SWAP gates

2021 ◽  
Author(s):  
Panagiotis Giounanlis ◽  
Xutong Wu ◽  
Andrii Sokolov ◽  
Nikolaos Petropoulos ◽  
Eugene Koskin ◽  
...  
Keyword(s):  
Mutual Information ◽  
Entanglement Entropy ◽  
Optimization Method ◽  
Charge Qubits

scholarly journals Target space entanglement in quantum mechanics of fermions and matrices

2021 ◽  
Vol 2021 (8) ◽  
Author(s):  
Sotaro Sugishita
Keyword(s):  
Mutual Information ◽  
Ground State ◽  
Entanglement Entropy ◽  
Algebraic Approach ◽  
Upper Bounds ◽  
Wave Functions ◽  
Target Space ◽  
Single Interval ◽  
Area Law ◽  
Formula Expression

Abstract We consider entanglement of first-quantized identical particles by adopting an algebraic approach. In particular, we investigate fermions whose wave functions are given by the Slater determinants, as for singlet sectors of one-matrix models. We show that the upper bounds of the general Rényi entropies are N log 2 for N particles or an N × N matrix. We compute the target space entanglement entropy and the mutual information in a free one-matrix model. We confirm the area law: the single-interval entropy for the ground state scales as $$ \frac{1}{3} $$ 1 3 log N in the large N model. We obtain an analytical $$ \mathcal{O}\left({N}^0\right) $$ O N 0 expression of the mutual information for two intervals in the large N expansion.

scholarly journals Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Martina Marinelli ◽  
Vincenzo Positano ◽  
Francesco Tucci ◽  
Danilo Neglia ◽  
Luigi Landini
Keyword(s):  
Mutual Information ◽  
Optimization Method ◽  
Global Maximum ◽  
Similarity Function ◽  
Spatial Transformation ◽  
Registration Method ◽  
Registration Algorithm ◽  
Positron Emission ◽  
Pet Ct ◽  
Artery Disease

Hybrid PET/CT scanners can simultaneously visualize coronary artery disease as revealed by computed tomography (CT) and myocardial perfusion as measured by positron emission tomography (PET). Manual registration is usually required in clinical practice to compensate spatial mismatch between datasets. In this paper, we present a registration algorithm that is able to automatically align PET/CT cardiac images. The algorithm bases on mutual information (MI) as registration metric and on genetic algorithm as optimization method. A multiresolution approach was used to optimize the processing time. The algorithm was tested on computerized models of volumetric PET/CT cardiac data and on real PET/CT datasets. The proposed automatic registration algorithm smoothes the pattern of the MI and allows it to reach the global maximum of the similarity function. The implemented method also allows the definition of the correct spatial transformation that matches both synthetic and real PET and CT volumetric datasets.

Efficient LLR Optimization Based on GMI for LDPC Coded BICM Systems

2014 ◽  
Vol 519-520 ◽  
pp. 1016-1020
Author(s):  
Ping Huang ◽  
Yue Heng Li ◽  
Mei Yan Ju
Keyword(s):  
Mutual Information ◽  
Performance Improvement ◽  
Ldpc Codes ◽  
Optimization Method ◽  
Reliable Information ◽  
Channel Changes ◽  
The Real ◽  
On Line ◽  
Simulation Results ◽  
Output Information

In this paper, A new on-line LLR optimization method based on the generalized mutual information (GMI) maximization is proposed for LDPC coded BICM systems. As the BP decoder is sensitive to imperfect soft demodulator output information, it is necessary to correct the LLRs to provide the reliable information for BP decoder. The proposed method searched the scalar factors to maximize the GMI for each bit channel, and applied them to the demodulator output LLRs. Different from the previous reference, the proposed found the factors by on-line computation with minor complexity. This characteristic makes it is more effective in the real systems where the channel changes rapidly. Numerical and simulation results verify that the proposed method achieves noticeable performance improvement for MIMO-BICM systems with LDPC codes.

scholarly journals Joint Optimization Method of Airborne MIMO Radar Track and Radiated Power Based on Mutual Information

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 136278-136295
Author(s):  
Xiangyu Fan ◽  
Peng Bai ◽  
Hongwei Wang ◽  
Jiaqiang Zhang ◽  
Huanyu Li
Keyword(s):  
Mutual Information ◽  
Optimization Method ◽  
Mimo Radar ◽  
Joint Optimization ◽  
Radiated Power

scholarly journals Entanglement entropy inequalities in BCFT by holography

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Chia-Jui Chou ◽  
Bo-Han Lin ◽  
Bin Wang ◽  
Yi Yang
Keyword(s):  
Field Theory ◽  
Conformal Field Theory ◽  
Mutual Information ◽  
Entanglement Entropy ◽  
Conformal Field ◽  
Holographic Entanglement Entropy ◽  
Entropy Inequalities

Abstract We study entanglement entropy inequalities in boundary conformal field theory (BCFT) by holographic correspondence. By carefully classifying all the configurations for different phases, we prove the strong subadditiviy and the monogamy of mutual information for holographic entanglement entropy in BCFT at both zero and finite temperatures.

Quantum Fisher information of two superconducting charge qubits under dephasing noisy channel

2018 ◽  
Vol 32 (22) ◽  
pp. 1850245 ◽  
Author(s):  
Hamada Abdel-Hameed ◽  
Nour Zidan ◽  
Nasser Metwally
Keyword(s):  
Mutual Information ◽  
Quantum Cryptography ◽  
Fisher Information ◽  
Quantum Fisher Information ◽  
Upper Bounds ◽  
Noisy Channel ◽  
Information Displays ◽  
Charge Qubits ◽  
Gradual Decay

The Quantum Fisher information (QFI) of a two charged qubits system that interacts locally with a dephasing channel is quantified with respect to the charged qubits’ and the channel’s parameters. The behavior of Fisher information displays different phenomena as; the sudden changes (increasing/decreasing); the gradual decay and freezing of the QFI. It is shown that the QFI of the dephasing parameter decays gradually and vanishes completely as one increases the initial energies of the qubits and their mutual energy. The possibility of estimating the charged qubits’ energies may be maximized at smaller values of the initial dephasing’s parameter. The estimation degree of the mutual information reaches its upper bounds by increasing the initial energies. It is depicted that, by controlling the qubits’ and the channel’s parameters, the QFI may be frozen. We expect that this result may be interesting in the context of quantum encoding and quantum cryptography.

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