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Quantum Computing Unleashed

Design, Models and Applications
Edited by Shiwani Gupta, Hemant Kasturiwale, Sujata Alegavi, Suman Lata Tripathi, and Utku Kose
Copyright: 2026   |   Expected Pub Date: 2026
ISBN: 9781394467334  |  Hardcover  |  
339 pages
Price: $225 USD
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One Line Description
Discover how quantum computing is set to revolutionize cybersecurity, drug discovery, and optimization, and gain the essential, multi-field foundation you need to lead the next technological leap.

Description
As quantum computing continues to advance, interdisciplinary collaboration is increasingly essential for unlocking its full potential. This book offers a comprehensive exploration of this cutting-edge technology and its transformative potential across various fields. It covers fundamental concepts of quantum mechanics, quantum computing models, algorithms, and hardware architectures, providing readers with a solid foundation to understand the principles underlying quantum computation. The book delves into practical applications of quantum computing, ranging from cryptography and cybersecurity to optimization problems, drug discovery, and materials science. Through detailed case studies and examples, readers gain insights into how quantum computing can revolutionize industries and address complex real-world challenges, making it a valuable resource for individuals seeking to stay informed about the latest developments in quantum computing.

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Author / Editor Details
Shiwani Gupta, PhD is an Associate Professor at the Thakur College of Engineering and Technology with more than 20 years of experience. She has published 10 books, two patents, and more than 1000 articles in national and international journals and conferences. Her areas of interest include AI, Machine Learning, and algorithms.

Hemant Kasturiwale, PhD is a Professor and Head of the Department of Electronics and Computer Science at Thakur College of Engineering and Technology, Mumbai, India. He has over 32 years of experience in teaching, research, and academic administration and has published research papers in reputed journals and conferences. He is a recognized PhD supervisor of the University of Mumbai. His research interests include Artificial Intelligence and Machine Learning, Robotics and Automation, Embedded Systems and IoT, and Healthcare Technologies.

Sujata Alegavi, PhD is an Associate Professor and Head of Department at the Thakur College of Engineering and Technology, Mumbai, Maharashtra. She has published more than 20 research papers in reputed journals and conferences. Her research interests include remote sensing, image processing, artificial intelligence, and deep learning.

Suman Lata Tripathi, PhD is working a Professor at Lovely Professional University with more than 22 years of experience in academics and research. She has published more than 19 books, more than 125 research papers in refereed journals, conference proceedings, and e-books, 13 Indian patents, and four copyrights. Her area of expertise includes microelectronics device modeling and characterization, low-power VLSI circuit design, VLSI design testing, and advanced FET design for IoT.

Utsu Kose, PhD is an Associate Professor at Suleyman Demirel University, Turkey. He has more than 200 publications, including articles, authored and edited books, conference proceedings, and reports. His research interests include artificial intelligence, machine ethics, artificial intelligence safety, intelligent systems for biomedical applications, intelligent optimization, and chaos theory.

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Table of Contents
Preface
1. Quantum Computing Unleashed: Design Models and Applications
Shiwani Gupta, Hemant Kasturiwale, Sujata Alegavi, Suman Lata Tripathi and Utku Kose
1.1 Introduction
1.1.1 An Introduction to Quantum Computing
1.1.2 Principles of Quantum Mechanics in Computing
1.1.3 Quantum Sensors and Metrology
1.2 Quantum Computing Design Models
1.2.1 Quantum Gate Model
1.2.2 Adiabatic Quantum Computing (AQC)
1.2.3 Topological Quantum Computing
1.3 Applications of Quantum Computing
1.3.1 Cryptography and Quantum Key Distribution
1.3.2 Optimization in Logistics and Networks
1.3.3 Quantum Machine Learning
1.3.4 Quantum Block Chain and Security
1.3.5 Public-Key Cryptography Vulnerability
1.3.6 Hash Function Susceptibility
1.3.6.1 Integration of Post-Quantum Cryptography
1.3.6.2 Quantum-Resistant Signature Schemes
1.3.6.3 Hash-Based Blockchain Architectures
1.3.6.4 Quantum Key Distribution (QKD) Integration
1.3.6.5 Performance Evaluation of Quantum-Resistant Blockchains
1.3.7 Material and Drug Discovery
1.4 Hybrid Quantum-Classical Computing
1.4.1 Quantum Biology and Chemistry
1.4.2 Integration Strategies
1.5 Challenges and Future Prospects
1.5.1 Error Correction Techniques
1.5.2 Scalability and Hardware Limitations
1.5.2.1 Quantum Internet and Quantum Communication Networks
1.6 Comparison of Different Quantum Computing Approaches
1.7 Conclusion and Future Scope
References
2. An Introductory Bridging Approach for Quantum Computing
with Artificial Intelligence
Tiyas Sarkar, Manik Rakhra, Gagandeep Singh Cheema, Ramandeep Sandhu and Deepika Ghai
2.1 Introduction
2.2 Quantum Computing Triggering Circumstances
2.2.1 Advancement in Artificial Intelligence
2.2.2 Privacy Needs and Digital Health
2.2.3 Discovery of the Solution Methodologies for Urban Issues
2.2.4 Cyber MIS Applications and E-Government
2.2.5 Society 5.0
2.3 Reciprocation between Quantum Computing and Artificial Intelligence
2.3.1 Semantical Study
2.3.2 Interlocking Keywords in Natural Linguistics
2.3.3 Quantum Transitions and Quantum Activities Awareness and Classification
2.3.4 Quantum Transitions and Procedures
2.4 Quantum Computing Products: Their Emergence
2.5 Related Work
2.6 Scenario Investigations on Quantum Computing with Artificial Intelligence
2.7 Applications of Quantum Computing
2.8 Conclusion
References
3. Unveiling the Efficiency and Advantages of Quantum Algorithms: A Comparative Study
Kalpita Ajinkya Wagaskar and Amiya Kumar Tripathy
3.1 Introduction
3.2 Background
3.3 Proposed System
3.3.1 Objectives
3.3.2 Methodology
3.4 Implementation Details
3.5 Technical Concepts
3.5.1 Quantum Superposition and Parallelism
3.5.2 Quantum Entanglement for Efficiency
3.5.3 Circuit Optimization and Error Correction
3.5.4 Quantum-Specific Challenges
3.6 Results and Analysis
3.6.1 Graphs
3.6.2 Key Observations
3.6.3 Findings
3.7 Applications
3.7.1 Machine Learning
3.7.2 Cryptography
3.7.3 Optimization Problems
3.8 Future Directions and Challenges
3.9 Conclusion
References
4. Novel Approach of Quantum Computing Models
Priti Subramanium, Gajanan Uttam Patil, Tushar Hrishikesh Jaware and Anna (Hanna) Karkanitsa
4.1 Introduction
4.2 Literature Survey
4.2.1 Quantum States Grow Exponentially
4.2.2 Turning Difficulty into Opportunity
4.2.3 Requirements to Build a Quantum Computer
4.2.4 Understanding Quantum Phenomena
4.2.4.1 Superposition
4.2.4.2 Entanglement
4.3 Objectives and Proposed Work
4.4 Result and Discussion
4.4.1 Quantum Full Adder
4.4.2 Copilot in Azure Quantum
4.4.2.1 Copilot in Azure Quantum
4.4.2.2 Azure Quantum Code
4.4.2.3 Quantum Elements
4.4.2.4 Katas Quantum
4.4.2.5 Samples of Code
4.4.2.6 Quantum Concepts
4.5 Launch a Quantum Application
4.6 Conclusion
References
5. Study of Quantum Error Correction Methods
Amar Shree V., Venkatesan. M. and Prabhavathy P.
5.1 Introduction
5.2 Problem Statement
5.3 Significance of Quantum Error Correction
5.4 Approaches for Quantum Error Correction
5.4.1 General QEC Process
5.4.2 Key Reconciliation Technique
5.4.3 Risk Reduction through Key Division
5.4.4 Tree Parity Machine (TPM) for Error Correction
5.4.5 Asymmetric Quantum Error Correction (A-QEC)
5.4.6 Asymmetric Entanglement-Assisted Quantum Error Correcting Code (AE-AQECC)
5.4.7 Entanglement-Assisted Quantum Error Correction (EA-AQEC)
5.4.8 Quantum Homomorphic Encryption (QHE) with QEC
5.4.9 Quantum Three-Stage Protocol (QTSP) with QEC
5.4.10 Enhanced Error Correction in BB84 Protocol
5.4.11 Prior Error Rate Estimation (PERE) with Asymmetric Error Correction (A-EC)
5.4.12 Quantum Error Correcting Code (QECC) for Decoherence
5.4.13 Quantum-GRAND for Quantum Random Linear Codes (QRLC)
5.4.14 QKD Protocol with Entanglement Purification and A-QEC
5.4.15 Weight One Error Channel for Code-Based Cryptosystems
5.4.16 RISC-V Galois Field IS Algorithm
5.5 Applications and Recent Trends
5.5.1 Secured QKD
5.5.2 Design of QEC Codes
5.5.3 Fault-Tolerant Quantum Cryptography
5.5.4 PQC (Post-Quantum Cryptography)
5.6 Comparative Analysis
5.7 Critical Analysis
5.8 Challenges and Future Recommendation
5.9 Conclusion
References
6. Quantum Optimization Algorithms and Application
N. Manglani, Dhruv Solanki and Ankit Das
6.1 Introduction to Quantum Optimization
6.1.1 Complexity of Optimization Problems
6.1.2 Scalability and Efficiency
6.1.3 Incorporating Uncertainty
6.2 Classification of Quantum Optimization Algorithms
6.2.1 Quantum Annealing
6.2.2 Hybrid Approaches
6.2.3 Search and Exploration-Based Approaches
6.3 Quantum Optimization Algorithms
6.3.1 Simulated Quantum Annealing
6.3.1.1 Introduction
6.3.1.2 Steps of Simulated Quantum Annealing
6.3.1.3 Use Cases
6.3.1.4 Key Characteristics
6.3.1.5 Challenges
6.3.2 Quantum Approximate Optimization Algorithm (QAOA)
6.3.2.1 Introduction
6.3.2.2 Steps of Quantum Approximate Optimization Algorithm (QAOA)
6.3.2.3 Use Cases
6.3.2.4 Key Characteristics
6.3.2.5 Challenges
6.3.3 Variational Quantum Eigensolver (VQE)
6.3.3.1 Introduction
6.3.3.2 Steps of Variational Quantum Eigensolver (VQE)
6.3.3.3 Use Cases
6.3.3.4 Key Characteristics
6.3.3.5 Challenges
6.3.4 Grover’s Algorithm
6.3.4.1 Introduction
6.3.4.2 Steps of Algorithm
6.3.4.3 Use Cases
6.3.4.4 Key Characteristics
6.3.4.5 Challenges
6.4 Conclusion
Acknowledgement
References
7. Quantum Computing in Finance: Algorithms and Applications
Anoop Patel, Mayank Sharma, Manasvi Nayak and Megharani Patil
7.1 Introduction
7.2 Methodology
7.2.1 Quantum Algorithms in Finance
7.2.1.1 Quantum Annealing for Optimization Problems
7.2.1.2 Shor’s Algorithm for Cryptography and Security
7.2.1.3 Search-Related Tasks Using Grover’s Algorithm
7.2.1.4 Quantum Monte Carlo Methods for Risk Analysis
7.2.2 Applications in Portfolio Optimization
7.2.2.1 Challenges in Classical Portfolio Optimization
7.2.2.2 How Quantum Algorithms Address These Challenges
7.2.2.3 Real-World Use Cases
7.2.2.4 Advantages of Quantum Portfolio Optimization
7.2.2.5 Challenges and Future Directions
7.2.3 Risk Management and Option Pricing
7.2.3.1 Challenges in Classical Risk Management and Option Pricing
7.2.3.2 How Quantum Algorithms Address These Challenges
7.2.3.3 Real World Use Cases
7.2.3.4 Advantages of Quantum Risk Management and Option Pricing
7.2.3.5 Challenges and Limitations
7.2.3.6 Future Directions
7.2.4 Cryptography and Security in Finance
7.2.4.1 Challenges in Classical Cryptography
7.2.4.2 How Quantum Algorithms Address These Challenges
7.2.4.3 Real-World Use Cases
7.2.4.4 Advantages of Quantum-Resistant Cryptography
7.2.4.5 Challenges and Limitations
7.2.4.6 Future Directions
7.2.5 Fraud Detection and Data Analysis
7.2.5.1 Challenges in Classical Fraud Detection
7.2.5.2 How Quantum Machine Learning (QML) Addresses These Challenges
7.2.5.3 Real-World Use Cases
7.2.5.4 Advantages of Quantum Machine Learning in Fraud Detection
7.2.5.5 Challenges and Limitations
7.2.5.6 Future Directions
7.2.6 Overall Current Challenges and Limitations
7.2.6.1 Hardware Challenges
7.2.6.2 Software and Algorithmic Challenges
7.2.6.3 Data Encoding and Input/Output Limitations
7.2.6.4 Scalability Challenges
7.2.6.5 Ethical and Security Concerns
7.2.6.6 Summary of Challenges
7.2.7 Future Scope and Developments
7.2.7.1 Future Hardware Advancements
7.2.7.2 Development of New Quantum Algorithms
7.2.7.3 Hybrid Quantum-Classical Systems
7.2.7.4 Industry Trends and Adoption
7.2.7.5 Quantum Cryptography and Secure Communication
7.2.7.6 Ethical and Societal Implications
7.2.7.7 Timeline of Future Developments
7.2.7.8 Summary of Future Prospects
7.3 Conclusion
References
8. Quantum Computing in Healthcare: Revolutionizing Medicine
Yerumbu Nandakishora, S. Prasad Jones Christydass, Avvaru Subramanyam and Ananda Babu Devarapalli
8.1 Introduction
8.2 Quantum Computing Applications in Healthcare
8.2.1 Drug Discovery
8.2.2 Personalized Medicine
8.2.3 Healthcare Management and Disease Prediction
8.2.4 Medical Imaging and Diagnostics
8.2.5 Healthcare Management and Resource Optimization
8.3 Challenges and Limitations
8.4 Conclusion
References
9. Medical Imaging and Diagnostics Using Quantum Computing:
A New Paradigm in Healthcare
Mamta Meena, Priti Rumao, Soniya Khatu and Peter Correia
9.1 Introduction
9.1.1 The Role of Advanced Computing in Medical Imaging
9.1.2 Principles of Quantum Computing
9.1.2.1 Superposition
9.1.2.2 Entanglement
9.1.2.3 Quantum Parallelism
9.2 Quantum Computing in Medical Imaging
9.2.1 Image Reconstruction
9.2.1.1 Quantum Fourier Transform (QFT) in Image Reconstruction
9.2.1.2 More Resolution and Speed
9.2.2 Noise Reduction in Medical Imaging
9.2.2.1 Quantum Error Correction for Noise Suppression
9.2.2.2 Enhanced Signal-to-Noise Ratios
9.2.3 Pattern Recognition and Disease Detection
9.2.3.1 Quantum Machine Learning (QML) in Imaging Analysis
9.2.3.2 Adaptive Learning for Accuracy
9.2.4 Real-Time Image Processing
9.2.4.1 Accelerated Image Processing
9.2.4.2 Integration with Robotic Systems
9.3 Image Reconstruction
9.3.1 Image Reconstruction: Classical vs. Quantum Approaches
9.3.2 Role of Quantum Computing in Image Reconstruction
9.3.3 Quantum Algorithms for Image Reconstruction
9.3.4 Applications of Quantum Computing in Image Reconstruction
9.3.4.1 Magnetic Resonance Imaging (MRI)
9.3.4.2 Computed Tomography (CT)
9.3.4.3 Positron Emission Tomography (PET)
9.3.4.4 Advanced Multimodal Imaging
9.3.5 Benefits of Quantum Computing in Image Reconstruction
9.3.6 Challenges in Quantum Image Reconstruction
9.4 Noise Reduction in Medical Images
9.4.1 Noise in Medical Imaging
9.4.2 Classical Noise Reduction Techniques
9.4.3 Quantum Noise Reduction Mechanisms Using Quantum Computing
9.4.3.1 Quantum Error Correction (QEC)
9.4.3.2 Quantum Filtering
9.4.3.3 Quantum Machine Learning (QML)
9.4.4 Benefits of Quantum Noise Reduction
9.4.5 Challenges and Limitations
9.5 Pattern Recognition and Disease Detection
9.5.1 The Role of Pattern Recognition in Disease Detection
9.5.1.1 Traditional Approaches
9.5.1.2 Limitations of Classical Computing
9.5.1.3 Quantum Computing in Pattern Recognition
9.5.2 Applications of Quantum Computing in Disease Detection
9.5.2.1 Image-Based Diagnostics
9.5.2.2 Genomic Analysis and Precision Medicine
9.5.2.3 Predictive Diagnostics
9.5.3 Quantum Computing in Disease Detection: Benefits
9.5.4 Challenges and Limitations
9.6 Real-Time Image Processing
9.6.1 Applications of Quantum Computing in Real-Time Image Processing
9.6.1.1 Medical Imaging
9.6.1.2 Autonomous Vehicles
9.6.1.3 Video Surveillance and Security
9.6.1.4 Space and Scientific Imaging
9.6.2 Quantum Algorithms for Real-Time Image Processing
9.6.2.1 Quantum Fourier Transform (QFT)
9.6.2.2 Grover’s Algorithm
9.6.2.3 Quantum Neural Networks (QNNs)
9.6.2.4 Quantum Edge Detection
9.7 Future Applications
9.8 Conclusion
References
10. Comprehensive Study and Applications of Quantum Computing for Internet of Things
Priti Subramanium, Gajanan Uttam Patil, Anilkumar Dulichand Vishwakarma and Tushar Hrishikesh Jaware
10.1 Introduction
10.2 Related Theory
10.3 Literature Survey
10.4 Objectives and Proposed Work
10.4.1 Supercomputer AIRAWAT
10.5 Methodology
10.5.1 Increasing the Utility of Quantum Computers
10.5.2 Understanding Quantum Computing
10.5.3 Features of Quantum Computing
10.5.3.1 Superposition
10.5.3.2 Entanglement
10.5.3.3 Decoherence
10.5.4 Components of a Quantum Computer
10.5.5 Quantum Software
10.5.6 Types of Quantum Technology
10.5.6.1 Gate-Based Ion Trap Processors
10.5.6.2 Gate-Based Superconducting Processors
10.5.6.3 Photonic Processors
10.5.7 Neutral Atom Processors
10.5.7.1 Rydberg Atom Processors
10.5.7.2 Quantum Annealers
10.5.8 Companies Use Quantum Computing
10.5.8.1 ML
10.5.8.2 Optimization
10.6 Result and Discussion
10.6.1 Simulation
10.7 Quantum Computing Applications for Internet of Things
10.8 Uses and Benefits of Quantum Computing
10.9 Quantum Computing Applications
10.10 Limitations of Quantum Computing
10.11 Future Scope
10.12 Conclusion
References
11. Diversity of Quantum Paradigms
Mayank Sharma and Anoop Patel
11.1 Introduction
11.2 Main Body/Methods
11.2.1 Model for Quantum Circuits
11.2.1.1 Qubits: Quantum Information’s Basic Building Blocks
11.2.1.2 Gates in Quantum and Unitary Operations
11.2.1.3 Quantum Circuits: Architecture and Functionality
11.2.2 Applications
11.2.3 Challenges and Solutions
11.3 Quantum Turing Machine (TM) Model
11.3.1 Quantum Turing Machine (TM): Basic Concept
11.3.2 Quantum States and Superposition
11.3.3 Quantum Turing Machine: Formal Definition
11.3.4 Quantum Operations: Unitary Transformation
11.3.5 Measurement and Output
11.3.6 Applications
11.3.7 Challenges and Solutions
11.4 Quantum Annealing
11.4.1 Quantum Annealing: Basic Concept
11.4.2 The Quantum Annealing Mechanism/Operations
11.4.3 Quantum States and Superposition in Quantum Annealing
11.4.4 Quantum Annealing: Formal Definition
11.4.5 Measurement and Output
11.4.6 Applications
11.4.7 Challenges and Solutions
11.5 Adiabatic Quantum Computing
11.5.1 Adiabatic Quantum Computing: Basic Concept
11.5.2 Adiabatic Quantum Computation Method/Operations
11.5.3 Quantum States and Superposition in Adiabatic Quantum Computing
11.5.4 Adiabatic Quantum Computing: Formal Definition
11.5.5 Measurement and Output
11.5.6 Applications
11.5.7 Challenges and Solutions
11.6 Topological Quantum Computing
11.6.1 Topological Quantum Computing: Basic Concept
11.6.2 Topological Quantum Computing Process/Operations
11.6.3 Quantum States and Superposition in Topological Quantum Computing
11.6.4 Topological Quantum Computing: Formal Definition
11.6.5 Measurement and Output
11.6.6 Applications
11.6.7 Challenges and Solutions
11.7 Continuous-Variable Quantum Computing
11.7.1 Continuous-Variable Quantum Computing: Basic Concept
11.7.2 Continuous-Variable Quantum Computing Process/Operations
11.7.3 Quantum States and Superposition in CV Quantum Computing
11.7.4 CV Quantum Computing: Formal Definition
11.7.5 Measurement and Output
11.7.6 Applications
11.7.7 Challenges and Solutions
11.8 Quantum Computing in Cluster States
11.8.1 Cluster-State Quantum Computing: Basic Concept
11.8.2 Quantum Computing in Cluster States Process/Operations
11.8.3 Quantum States and Superposition in Cluster-State Quantum Computing
11.8.4 Cluster-State Quantum Computing: Formal Definition
11.8.5 Measurement and Output
11.8.6 Applications
11.8.7 Challenges and Solutions
11.9 Result and Discussion
11.9.1 Results
11.9.2 Discussion
11.10 Conclusion
References
12. Quantum Machine Learning
Moushumi Das, Ravinder Tonk, Vishal Jagota, Rajan Vohra and Shikha Tuteja
12.1 Introduction
12.1.1 Motivation
12.1.2 Contribution
12.1.3 Organization of Chapter
12.2 Foundational Concepts
12.2.1 Quantum Computing Basics
12.2.2 Superposition and Entanglement
12.2.3 Quantum Speedup
12.2.4 Quantum Data Encoding
12.2.5 Variational Quantum Circuits (VQCs)
12.2.6 Quantum Kernels
12.2.7 Hybrid Quantum-Classical Models
12.2.8 Quantum Neural Networks (QNNs)
12.2.9 Quantum Measurement and Readout
12.2.10 Challenges in Quantum Machine Learning
12.3 Key Algorithms in Quantum ML
12.4 Applications
12.5 Challenges
12.6 Latest Trends
12.7 Conclusion and Future Scope
Bibliography
13. Accelerating Security and Efficiency of the BIKE (Bit Flipping Key Encapsulation) through Reinforcement Learning for Industrial IoT
Samir Gherbi, Soumya Banerjee and Yulliwas Ameur
13.1 Introduction
13.1.1 Background to Advances in Post-Quantum Cryptography
13.2 Recent Attempts of Machine Learning
13.3 Background of Reinforcement Learning (RL)
13.3.1 Relevance of Using Reinforcement Learning
13.3.1.1 Simulated Attack Scenarios for RL Training
13.3.1.2 Advantages of Simulated Attack Scenarios
13.3.1.3 RL-Based Tuning Approach
13.4 Proposed Algorithm
13.4.1 Reward Function
13.4.2 RL Algorithm for Optimizing Bit-Flipping
13.4.3 Results and Discussion
13.4.3.1 Simulation Breakdown
13.4.3.2 Observation on Combining Q-Learning (RL)
13.4.3.3 Analysis on Characteristics
13.4.4 Comparison of Performance
13.5 Conclusion and Future Scope of Research
Glossary
References
Index

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