Frontiers in Quantum Computing: New Research

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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