High fidelity quantum teleportation assistance with quantum neural network

2014 ◽  
Vol 28 (24) ◽  
pp. 1450189 ◽  
Author(s):  
Chunhui Huang ◽  
Bichun Wu
Keyword(s):  
Neural Network ◽  
Master Equation ◽  
Error Correction ◽  
Quantum Teleportation ◽  
Qubit State ◽  
The Other ◽  
High Fidelity ◽  
Noisy Environment ◽  
Random Series ◽  
Quantum Neural Network

In this paper, a high fidelity scheme of quantum teleportation based on quantum neural network (QNN) is proposed. The QNN is composed of multi-bit control-not gates. The quantum teleportation of a qubit state via two-qubit entangled channels is investigated by solving the master equation in Lindblad operators with a noisy environment. To ensure the security of quantum teleportation, the indirect training of QNN is employed. Only 10% of teleported information is extracted for the training of QNN parameters. Then the outputs are corrected by the other QNN at Bob's side. We build a random series of numbers ranged in [0, π] as inputs and simulate the properties of our teleportation scheme. The results show that the fidelity of quantum teleportation system is significantly improved to approach 1 by the error-correction of QNN. It illustrates that the distortion can be eliminated perfectly and the high fidelity of quantum teleportation could be implemented.

An Enhanced Cloud Security using Quantum Teleportation

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
D. Sowmya ◽  
S. Sivasankaran
Keyword(s):  
Quantum Key Distribution ◽  
Quantum Teleportation ◽  
Quantum Physics ◽  
Communication Channel ◽  
Data Transfer ◽  
Cloud Security ◽  
The Other ◽  
Cloud Environment ◽  
Quantum Bits ◽  
Quantum Parallelism

In the cloud environment, it is difficult to provide security to the monolithic collection of data as it is easily accessed by breaking the algorithms which are based on mathematical computations and on the other hand, it takes much time for uploading and downloading the data. This paper proposes the concept of implementing quantum teleportation i.e., telecommunication + transportation in the cloud environment for the enhancement of cloud security and also to improve speed of data transfer through the quantum repeaters. This technological idea is extracted from the law of quantum physics where the particles say photons can be entangled and encoded to be teleported over large distances. As the transfer of photons called qubits allowed to travel through the optical fiber, it must be polarized and encoded with QKD (Quantum Key Distribution) for the security purpose. Then, for the enhancement of the data transfer speed, qubits are used in which the state of quantum bits can be encoded as 0 and 1 concurrently using the Shors algorithm. Then, the Quantum parallelism will help qubits to travel as fast as possible to reach the destination at a single communication channel which cannot be eavesdropped at any point because, it prevents from creating copies of transmitted quantum key due to the implementation of no-cloning theorem so that the communication parties can only receive the intended data other than the intruders.

scholarly journals Artificial Neural Network Assisted Error Correction for MLC NAND Flash Memory

Micromachines ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 879
Author(s):  
Ruiquan He ◽  
Haihua Hu ◽  
Chunru Xiong ◽  
Guojun Han
Keyword(s):  
Neural Network ◽  
Error Correction ◽  
Error Detection ◽  
Flash Memory ◽  
Nand Flash ◽  
Data Reliability ◽  
Nand Flash Memory ◽  
Process Technology ◽  
Noise Mitigation ◽  
Memory Channel

The multilevel per cell technology and continued scaling down process technology significantly improves the storage density of NAND flash memory but also brings about a challenge in that data reliability degrades due to the serious noise. To ensure the data reliability, many noise mitigation technologies have been proposed. However, they only mitigate one of the noises of the NAND flash memory channel. In this paper, we consider all the main noises and present a novel neural network-assisted error correction (ANNAEC) scheme to increase the reliability of multi-level cell (MLC) NAND flash memory. To avoid using retention time as an input parameter of the neural network, we propose a relative log-likelihood ratio (LLR) to estimate the actual LLR. Then, we transform the bit detection into a clustering problem and propose to employ a neural network to learn the error characteristics of the NAND flash memory channel. Therefore, the trained neural network has optimized performances of bit error detection. Simulation results show that our proposed scheme can significantly improve the performance of the bit error detection and increase the endurance of NAND flash memory.

An improved grey Markov chain model with ANN error correction and its application in gross domestic product forecasting

2021 ◽  
pp. 1-11
Author(s):  
Yuan Zou ◽  
Daoli Yang ◽  
Yuchen Pan
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Gross Domestic Product ◽  
Error Correction ◽  
Historical Data ◽  
Markov Chain Model ◽  
Back Propagation ◽  
The United States ◽  
Domestic Product ◽  
Chain Model

Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality.

scholarly journals Fast Accurate and Automatic Brushstroke Extraction

2021 ◽  
Vol 17 (2) ◽  
pp. 1-24
Author(s):  
Yunfei Fu ◽  
Hongchuan Yu ◽  
Chih-Kuo Yeh ◽  
Tong-Yee Lee ◽  
Jian J. Zhang
Keyword(s):  
Neural Network ◽  
Efficient Algorithm ◽  
Deep Neural Network ◽  
High Efficiency ◽  
State Of The Art ◽  
High Reliability ◽  
The Other ◽  
Manual Annotation ◽  
Stroke Extraction ◽  
Art Research

Brushstrokes are viewed as the artist’s “handwriting” in a painting. In many applications such as style learning and transfer, mimicking painting, and painting authentication, it is highly desired to quantitatively and accurately identify brushstroke characteristics from old masters’ pieces using computer programs. However, due to the nature of hundreds or thousands of intermingling brushstrokes in the painting, it still remains challenging. This article proposes an efficient algorithm for brush Stroke extraction based on a Deep neural network, i.e., DStroke. Compared to the state-of-the-art research, the main merit of the proposed DStroke is to automatically and rapidly extract brushstrokes from a painting without manual annotation, while accurately approximating the real brushstrokes with high reliability. Herein, recovering the faithful soft transitions between brushstrokes is often ignored by the other methods. In fact, the details of brushstrokes in a master piece of painting (e.g., shapes, colors, texture, overlaps) are highly desired by artists since they hold promise to enhance and extend the artists’ powers, just like microscopes extend biologists’ powers. To demonstrate the high efficiency of the proposed DStroke, we perform it on a set of real scans of paintings and a set of synthetic paintings, respectively. Experiments show that the proposed DStroke is noticeably faster and more accurate at identifying and extracting brushstrokes, outperforming the other methods.

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3068
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-González ◽  
Radim Burget
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
The Other ◽  
Support Vector ◽  
Learning Approaches ◽  
Microscopic Images ◽  
Trained Neural Network ◽  
Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

Quantum Teleportation of Five-qubit State

2017 ◽  
Vol 56 (9) ◽  
pp. 2710-2715 ◽  
Author(s):  
Min Li ◽  
Nan Zhao ◽  
Nan Chen ◽  
Chang-hua Zhu ◽  
Chang-xing Pei
Keyword(s):  
Quantum Teleportation ◽  
Qubit State

A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Short Term ◽  
Bp Network ◽  
Quantum Neural Network ◽  
Short Term Load Forecasting ◽  
Selection For

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

Fault Detection for an Aileron Actuator under Variable Conditions Based on Bi-Step Neural Network

2015 ◽  
Vol 764-765 ◽  
pp. 740-746
Author(s):  
Hang Yuan ◽  
Chen Lu ◽  
Ze Tao Xiong ◽  
Hong Mei Liu
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Simulation Model ◽  
Working Conditions ◽  
Detection Method ◽  
Fault Tolerant ◽  
Adaptive Threshold ◽  
Residual Error ◽  
The Other ◽  
Aerodynamic Loads

Fault detection for aileron actuators mainly involves the enhancement of reliability and fault tolerant capability. Considering the complexity of the working conditions of aileron actuators, a fault detection method for an aileron actuator under variable conditions is proposed in this study. A bi-step neural network is utilized for fault detection. The first neural network, which is employed as the observer, is established to monitor the aileron actuator and generate the residual error. The other neural network generates the corresponding adaptive threshold synchronously. Faults are detected by comparing the residual error and the threshold. In considering of the variable conditions, aerodynamic loads are introduced to the bi-step neural network. The training order spectrums are designed. Finally, the effectiveness of the proposed scheme is demonstrated by a simulation model with different faults.

scholarly journals Chaos and complexity from quantum neural network. A study with diffusion metric in machine learning

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Sayantan Choudhury ◽  
Ankan Dutta ◽  
Debisree Ray
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Quantum Chaos ◽  
Geometric Approach ◽  
Universal Function ◽  
Stochastic Gradient Descent ◽  
Generalization Capability ◽  
Steady State Condition ◽  
Quantum Neural Network ◽  
Function Approximator

Abstract In this work, our prime objective is to study the phenomena of quantum chaos and complexity in the machine learning dynamics of Quantum Neural Network (QNN). A Parameterized Quantum Circuits (PQCs) in the hybrid quantum-classical framework is introduced as a universal function approximator to perform optimization with Stochastic Gradient Descent (SGD). We employ a statistical and differential geometric approach to study the learning theory of QNN. The evolution of parametrized unitary operators is correlated with the trajectory of parameters in the Diffusion metric. We establish the parametrized version of Quantum Complexity and Quantum Chaos in terms of physically relevant quantities, which are not only essential in determining the stability, but also essential in providing a very significant lower bound to the generalization capability of QNN. We explicitly prove that when the system executes limit cycles or oscillations in the phase space, the generalization capability of QNN is maximized. Finally, we have determined the generalization capability bound on the variance of parameters of the QNN in a steady state condition using Cauchy Schwartz Inequality.

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