The objective of this book is to communicate advancements of knowledge and help disseminate results concerning recent applications and case studies in the area of quantum computing among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines. This book will allow students to explore knowledge in quantum computing to produce serviceable and innocuous systems as well as purposeful systems with cutting-edge technology. To yield computer systems with decent usability, developers must attempt to understand the factors that determine how people use technology.
This book will cater to an extensive cross-sectional and multi-disciplinary readership ranging from academics, business delegates, CEOs, communication designers, computer scientists, digital customers, e-decision makers, eLearning environment designers, industrial leaders, industry consultants, key workers, law enforcement agencies, managers, practitioners, professionals, professors, profit/non-profit e-organizations, programmers, R&D/professional research communities, security architects, stakeholders, students, support staff and university researchers/scholars of various communities, such as artificial intelligence, cyber-physical systems, ethics, robotics, safety engineering, safety-critical systems, standardization and certification digital forensics and application domain communities such as aerospace, agriculture, automotive, critical infrastructures, healthcare, manufacturing, retail, smart transports, smart cities and smart healthcare, using real case studies and projecting outcomes, showing the intricate details of quantum computing in these real-life scenarios. As a final point, this book will provide up-to-date, premeditated, and creative information to those engrossed in the field of quantum computing.
Author(s): R. Anandan
Series: Computer Science, Technology and Applications
Publisher: Nova Science Publishers
Year: 2022
Language: English
Pages: 265
Contents
Preface
Acknowledgments
Chapter 1
Programming a Quantum Computer Using Python
Abstract
1. Introduction
2. Need for Quantum Computers
3. Fundamentals of Quantum Computing
4. Where Does the Concept of Bits Come From?
5. Properties of Quantum Computing
5.1. Superposition
5.2. Entanglement
5.3. Interference
6. Python Programming Language
7. QISKit
7.1. Installation
7.2. Importing Qiskit
7.3. Version
7.4. Quantum Circuit
7.5. Connecting to IBM Quantum Computing Prototype
7.6. Executing a Measurement on the IBM Quantum Computing Prototype
7.7. Random Number Generation using IBM Quantum Computer
8. Major Challenges in Quantum Computing
Conclusion and Future Scope
References
Chapter 2
Blockchain-Based Quantum Key Distribution Approach
Abstract
1. Introduction
1.1. Key Organization for Information Encryption in WSN Environment
1.2. The Extensive Keys Construction of the WSN IEEE 802.1 Equipment
2. Related Works
3. Proposed Methodology
3.1. Sender
3.2. Receiver
3.3. Data Encryption
3.4. BB84 Procedure
3.5. Encryption Process of Messages
4. Results
Conclusion
References
Chapter 3
Quantum Computing of PMS Using Machine Learning Algorithms for Revenue Management in Front Office Operations
Abstract
1. Introduction
2. Literature Review
3. Quantum Algorithm for Property Management System
3.1. Central Reservation System (CRS)
3.2. Applications of PMS
3.3. Reservation Module
3.4. Revenue Management System (RMS)
3.4.1. Capacity Management
3.4.2. Discount Allocation Based on Demand
3.4.3. Duration Control
3.5. Global Distribution System (GDS)
3.6. Online Travel Agency (OTA)
3.7. Quantum Property Management System with OTA
3.8. Grover’s Algorithm
Conclusion
References
Chapter 4
Bernstein Vazirani and Deutsch Algorithm: Made Easy in Qiskit
Abstract
1. Introduction
1.1. Fundamentals of Quantum Computing
1.1.1. Transformations of Qubits Using the Bloch Sphere
1.2. Simulating Polarisation of Light on a Quantum Computer
1.3. Entanglement and Teleportation Using Qiskit
1.3.1. Quantum Teleportation Implementation in Qiskit
1.4. Entanglement of Qubits
1.4.1. Bell States and Composing Quantum Circuits
1.4.2. Measurement in the Bell Basis
2. Deutsch Algorithm
2.1. What Does a Classical Algorithm Do?
2.2. Quantum Representation of the above Problem
2.3. Quantum Representation: Deutsch Algorithm
2.4. Qiskit Implementation
2.4.1. Constant Oracle
2.4.2. Balanced Oracle
3. Bernstein-Vazirani Algorithm
Conclusion
References
Chapter 5
Quantum Aided Deep Learning Framework for Motif Structure Prediction
Abstract
1. Introduction
2. Background
2.1. Motifs
2.1.1. Evolution of Motif Classifiers
2.1.2. Importance of Structure Prediction
Protein Structures
2.2. Need for Current Research Work
2.3. Map Reduce Framework
2.3.1. API Layer
2.3.2. Hadoop Distributed File System (HDFS)
Name Node
Data Node
2.3.3. Essential Attributes of HDFS
2.3.4. Hive
2.4. Deep Learning Model
2.5. Proposed Methodology
2.6. Word Embedding’s - Vectorisation
3. AI Techniques for Motif Prediction
3.1. Input Dataset
3.2. Feature Extraction
3.3. Predict Helix Turn Helix - AI Techniques
4. Deep Neural Network Implementation
4.1. Convolutional Neural Network
5. Experimental Results
5.1. Confusion Matrix Results
Conclusion
References
Chapter 6
Quantum Behaved Translation Invariant Feature Extraction for Chromosome Classification
Abstract
1. Introduction
2. Rotation and Translation Invariant Feature Extraction
2.1. Wavelet Orthonormal Decomposition into Subpatterns
2.2. Performance Analysis
3. Microarray Data
3.1. Cluster Performance Analysis
3.2. Protein Dataset
3.3. Feature Engineering
3.4. Model Creation—SOM
3.5. Average Accuracy Error Rate
3.6. Prediction Results
4. Proposed Adaptive Threshold for Denoising
Conclusion
References
Chapter 7
Quantum Based Dynamic Clustering of Pharmacovigilance Data
Abstract
1. Introduction
1.1. K-Means Partitioning
2. Dynamic Document Clustering
2.1. Description of Proposed Dynamic Clustering Algorithm
3. Proposed Clustering Using Maximum Resemblance Data Labeling (MARDL) Technique
3.1. Implementation Code
3.1.1. K-Means Clustering
3.1.2. Bisecting K-Means
3.1.3. Weight Matrix
3.1.4. Proposed Algorithm
4. Results and Discussion
4.1. Bisecting K-Means
4.2. Proposed Dynamic Algorithm
4.2.1. Dynamic Algorithm for Forming New Cluster
4.3. Performance Analysis
4.3.1. Comparison Concerning the Time of Static Bisecting K-Means Algorithm and Proposed Dynamic Document Concerning the Time
4.3.2. Static Bisecting K-Means Algorithm and Proposed Dynamic Algorithm in Purity
4.3.3. Static Bisecting K-Means Algorithm and Proposed Dynamic Algorithm in Intracluster Similarity
4.3.4 Static Bisecting K-Means Algorithm and Proposed Dynamic Algorithm in Inter-Cluster Similarity
Conclusion
References
Chapter 8
Quantum-Based Deep Learning for Multi-Level Grading of Mangoes
Abstract
1. Introduction
1.1. Current Grading Methods
1.2. Current Grading Technology
1.3. Limitations of the Current Grading Methods and Technology
1.4. The Need for Artificial Intelligence
1.5. The Proposed Solution for Fruit Grading
1.6. Mango Supply and Demand
1.7. Factors Affecting Mango Quality
1.8. Objective Quality Evaluation
1.9. The Significant Contribution of This Research
2. Literature Survey
2.1. Factors Influencing the Fruit Quality Assessment
2.2. Assessment Based on External Appearance Using Computer Vision, Image Processing and Conventional Neural Network Methods
2.3. Assessment Based on Internal Attributes Using Visible/Near-Infrared Spectroscopy
2.4. Quality Grading Using Classification and Regression Algorithms
2.5. Quality Grading Using Different Devices
3. Materials and Methods
3.1. Research Objective and Methodology
3.2. Data Acquisition
3.3. Process and Steps
3.3.1. Research Objective 1: Microscopic Grading
Pre-Processing and Feature Extraction
Model Design
3.3.2. Research Objective 2: External Grading
Pre-Processing and Feature Extraction
Model Design
3.3.3. Research Objective 3: Internal Grading
Pre-Processing and Feature Extraction
Model Design
3.3.4. Research Objective 4: Combined Multi-Level Grading
4. Data Analysis and Findings
4.1. Step 1: Creation of Dataset
4.1.1. Model Development and Execution Process Flow
Microscopic Grading
External Grading
4.2. Step 2: Train the Model
4.3. Step 3: Evaluate and Classification of Variety and Quality
4.3.1. Step 1: Model Development and Execution Process Flow – Internal Grading
4.3.2. Step 2: Modelling Using Multivariate Algorithms
4.3.3. Step 3: Evaluate and Classify Based on Sweetness/TSS
4.4. Step 4: Compare with Previous Works
4.4.1. Model Development and Execution Process Flow – Combined Multi-Level Grading
5. Discussion of the Findings
5.1. Research Objective
5.2. Research Methodology for Multi-Level Grading
5.3. Data Acquisition
5.4. Pre-Processing and Feature Extraction
5.5. Model Design
5.6. Model Development and Execution Process Flow
5.7. Compare with Previous Works
Summary and Conclusion
Contribution of the Current Work
Limitations
Recommendations for Future
References
Chapter 9
Efficient Quantum-Based Secure Route Creation and Data Transfer in Mobile Ad-Hoc Networks Using Multi-User Co-Operative Motion Mechanism
Abstract
1. Introduction
2. Literature Survey
2.1. Research
2.1.1. Gap Identified from the Literature Survey
2.1.2. Objectives
2.1.3. Contribution
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5
3. Proposed Algorithm
4. Proposed Work
4.1. Secure Route Creation for ADN Using RFR Algorithm
4.1.1. RFA
Design
Algorithm
4.1.2. Performance Analysis
Participation without Authorization
Route Signaling Spoof
Routing Message Alteration
RFA
4.2. Multi-User Efficiency Mechanism
4.2.1. Efficient Cooperation Model
4.2.2. Multi-User Co-Operative
Method
Motion with Broadcast
4.2.3 Working Mechanism of Cooperative Method
Theorem 1
4.3. An Efficient Cooperative Motion - Quality of Service in Mobile Adhoc Networks
4.4. Mobile Ad-Hoc Network Intrusion Detection in Cooperative Motion
Conclusion
References
Chapter 10
Pattern Recognition Accuracy of Echocardiogram Images Using Deep Learning Techniques
Abstract
1. Introduction
1.1. The Objective of the Research Work
1.2. Problem Definition
1.3. Contribution of Research Work
2. Literature Review
2.1. Optimization Methods Based Disease Diagnosis
2.2. Gravitational Search and Heuristic Search Methods for Disease Diagnosis
2.3. Machine Learning Technique for Diagnosis Performance Enhancement
2.4. Deep Neural Learning Method for Disease Prediction
2.5. Convolution Neural Network-Based Disease Diagnosis
2.6. Different Classification Methods with ECG Information
3. Proposed Methodology
3.1. Hierarchical Elitism Gene Gravitational Search Method
3.1.1. Additive Kuan Speckle Noise Filtering Model
3.1.2. Hierarchical Elitism Gene GSO Optimization of MNN
3.2. Frost Filtration Fuzzified Gravitational Search Based Shift-Invariant Deep Structure Feature Learning Technique
4. Simulations and Performance Metric Analysis
4.1. Measure of Pattern Recognition Accuracy
4.2. Measure of Computational Time
Conclusion
References
About the Editor
Index
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