book

Industrial Quantum Computing

Name: Industrial Quantum Computing
ISBN: 9783111355139

by Umesh Kumar Lilhore, Surjeet Dalal, Vishal Dutt, Magdalena Radulescu

December 2024

Intermediate to advanced

281 pages

10h 16m

English

De Gruyter

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List of contributors
Yogesh Kumar Sharma, Manoj Wadhwa, Masira M. S. Kulkarni, Pranita Haridas Mokal Chapter 1 Quantum computing in society: impacts and implications
1.1 Introduction1.1.1 Background and rationale1.1.2 Goals1.1.3 Scope and overview of the methodology1.1.4 Contribution of the work1.1.5 Organization of the chapter1.2 Quantum computing1.2.1 Quantum computing elements1.2.2 Comparison with traditional computer1.3 Quantum computing applications1.3.1 Healthcare1.3.2 Business and finance1.3.3 Climate prediction1.3.4 Travel and transportation1.3.5 Economy1.3.6 Cryptography1.3.7 Pharmaceutical and drug industries1.3.8 Computer programs that use AI and ML1.3.9 Environment and climate models1.3.10 Streamlining logistics and the supply chain1.4 Societal impacts1.5 Ethical considerations1.5.1 Cyber1.5.2 Governance1.5.3 Citizenship1.6 Challenges and limitations1.6.1 Quantum computing challenges1.6.2 Quantum computing limitations1.7 Conclusion
Leena Arya, Yogesh Kumar Sharma, Smitha, Muniraju Naidu Vadlamudi, Umesh Kumar Lilhore Chapter 2 Quantum computing with machine learning: opportunities and challenges
2.1 Introduction2.2 Quantum computing and machine learning2.3 Quantum machine learning2.4 Key applications2.5 Opportunities in QML2.6 Challenges in QML2.7 Conclusion and future direction
Juhi Singh, Aarti Chugh, Shishir Singh Chauhan, Arun Kumar Singh Chapter 3 Quantum machine learning algorithms: a comprehensive review
3.1 Fundamentals of quantum computing3.1.1 Representation of qubits3.1.2 Superposition and entanglement3.1.3 Qubit technology3.1.4 Requirements for handling quantum computations3.1.5 Quantum computing algorithms3.2 Importance of integration of machine learning with quantum computing3.2.1 Machine learning to improve QUBO3.2.2 Unsupervised machine learning with QUBO3.2.3 Supervised machine learning with QUBO3.3 Quantum machine learning3.3.1 Types of quantum machine learning3.4 Challenges and future directions3.5 Conclusion
Neeraj Dahiya, Amit Malik, Surjeet Dalal, Vivek Jaglan, Momina Shaheen Chapter 4 Highlighting major issues with quantum computing in healthcare
4.1 Introduction4.2 Utilizing quantum computing in the healthcare sector4.3 Evolution, context, and key technologies of quantum computing4.4 Crafting quantum algorithms tailored to healthcare applications4.5 Conclusion
Umesh Kumar Lilhore, Leena Arya, Yogesh Kumar Sharma, Ravi Rastogi Chapter 5 Privacy and security for 6G’s IoT-connected future in the age of quantum computing
5.1 Introduction5.1.1 A connected world: overview from 1G to 5G5.1.2 Beyond the numbers5.2 Literature review5.3 Unique cybersecurity threats in 6G IoT5.4 Millimeter-wave Mayhem5.5 AI-powered peril5.6 Supply chain specters5.7 Security and privacy issues of 6G technologies5.7.1 Artificial intelligence5.7.2 Molecular communication5.7.3 Quantum communication5.7.4 Blockchain5.7.5 THz technology (THz)5.7.6 Visible light communication (VLC)5.8 Strategies for securing 6G IoT networks5.8.1 Encryption and authentication mechanisms5.8.2 Secure-by-design principles5.8.3 Intrusion detection and prevention systems (IDPS)5.8.4 Zero trust architecture (ZTA)5.8.5 Security orchestration, automation, and response (SOAR)5.8.6 Collaborative threat intelligence sharing5.8.7 Continuous security monitoring and compliance5.8.8 Mitigating risks through quantum-secure solutions5.9 Conclusion
Sebastian MEZEI Chapter 6 Can quantum computers revolutionize health systems?
6.1 Introduction6.2 Modern health systems6.3 Big data in health6.4 How is health information used?6.5 Can quantum computers revolutionize health systems?6.6 Application areas of quantum technology in health6.6.1 Molecular simulation and drug design6.6.2 Optimization of personalized treatment6.6.3 Assistance in diagnosis and medical imaging6.6.4 Advanced simulations for medical research6.6.5 Health logistics optimization6.6.6 Quantum cloud in health6.6.7 Quantum encryption for health data security6.6.8 Quantum computers and artificial intelligence6.7 Conclusion
Nirdesh Jain, Sakshi Kathuria Chapter 7 Industrial automation and quantum computing
7.1 Introduction7.2 Evolution of industrial automation7.3 Quantum computing fundamentals7.3.1 Quantum bits (qubits)7.3.2 Quantum gates and circuits7.3.3 Quantum algorithms7.3.4 Quantum supremacy and challenges7.3.5 Quantum computing platforms7.4 Integration of quantum computing in industrial automation7.4.1 Quantum computing and mechanical automation7.4.2 Extraordinary calculations7.4.3 Group exertion7.4.4 Security7.4.5 Mimicking complex forms7.4.6 Quantum as a benefit7.4.7 Challenges7.5 Challenges and considerations7.5.1 Quantum equipment7.5.2 Quantum calculations7.5.3 Integration with existing frameworks7.5.4 Moral and administrative contemplations7.5.5 Quantum hardware limitations7.5.6 Scalability issues7.5.7 Algorithmic development7.5.8 Interdisciplinary collaboration7.5.9 Cost and accessibility7.5.10 Quantum security concerns7.6 Case studies and applications7.6.1 Supply chain optimization7.6.2 Production planning and resource planning7.6.3 Material design and process optimization7.7 Future directions and outlook7.7.1 Advances in quantum hardware7.7.2 Algorithm innovation7.7.3 Hybrid quantum classical systems7.7.4 Business development and integration7.7.5 Fairness and social responsibility7.7.6 Global collaboration and information sharing7.8 Conclusion
Rajagopalan S, T Mohana Sundari, Gayathri M Chapter 8 Applications of quantum computing in financial planning and financial control
8.1 Introduction8.1.1 Finance8.1.2 Financial management8.1.3 Financial planning8.1.4 Financial control8.2 Quantum computing8.2.1 Fundamentals of quantum computing8.2.2 Role of quantum computing in finance8.2.3 Quantum computing in financial planning8.2.4 Quantum computing in financial control8.3 Future of quantum computing in financial management8.4 Positive and negative sides of quantum computing in finance8.5 Limitations of quantum computing8.6 Conclusion
Shailja, Neha Chapter 9 Quantum computing in machine learning: an overview
9.1 Introduction9.1.1 Quantum basics9.1.2 Elements of quantum computing9.2 Quantum machine learning (QML)9.2.1 Quantum neural networks (QNN)9.2.2 Architecture of QNN9.2.3 Advantages of QNN9.3 Quantum support vector machine (QSVM)9.3.1 Choosing a quantum framework9.3.2 Data encoding9.3.3 Training the QSVM9.3.4 Testing and evaluation9.4 Application of QML in various fields9.5 Challenges in QML9.6 Conclusion

Muhammad Suhrab, Chen Pinglu, Radulescu Magdalena, Jahangeer Ahmed Soomro, Surjeet Dalal Chapter 10 The impact of AI and automation on income inequality in BRICS countries and the role of structural factors and women’s empowerment
10.1 Introduction10.2 Literature review10.2.1 Theoretical background10.2.2 Automation and AI’s impact on income inequality10.2.3 Impact of women's empowerment on income inequality10.3 Methodology10.4 Results10.4.1 Use of AI in BRICS10.4.2 Use of AI and automation in agriculture10.4.3 Use of AI in education10.4.4 Women’s empowerment and income inequality10.5 Conclusion and recommendations
Priyanka Vashisht, Asif Ali Chapter 11 Quantum computing and machine learning: a symbiotic relationship
11.1 Introduction11.2 Fundamentals of quantum computing11.3 Fundamentals of machine learning11.4 Intersection of machine learning and quantum computing11.5 Quantum machine learning algorithms and techniques11.5.1 Variational or a parametrized quantum circuit (PQC)11.5.2 Quantum optimization problems11.5.3 Quantum approximate optimization algorithm(QAOA)11.5.4 Variational quantum eigensolver11.5.5 Quantum annealing11.5.6 Quantum neural networks11.5.7 Quantum convolutional neural networks11.5.8 Quanvolutional neural networks11.5.9 Quantum recurrent neural networks11.5.10 Quantum generative adversarial networks11.5.11 Quantum autoencoders11.5.12 Quantum kernels11.6 Applications of quantum machine learning11.6.1 Explicit density estimators11.6.2 Implicit density estimators11.7 Challenges in quantum machine learning11.7.1 Hardware restrictions11.7.2 Algorithm making11.7.3 Training and adjustment11.8 Future directions and opportunities in quantum machine learning11.9 Summary11.10 Conclusion11.11 Declaration of Gen AI and AI-assisted technologies in the writing process
Ravi Rastogi, Leena Arya, Yogesh Kumar Sharma Chapter 12 Quantum-secured healthcare data and cybersecurity innovations in the era of Industry 5.0
12.1 Introduction12.2 Related survey12.3 The prospects of healthcare vulnerabilities12.3.1 Legacy systems and outdated infrastructure12.3.2 Fragmented ecosystem12.3.3 Human error12.3.4 Data privacy concerns12.3.5 Evolving attack landscape12.3.6 Connected medical devices12.3.7 Inadequate security awareness12.4 Industry 5.0 technologies in healthcare12.4.1 Internet of things (IoT) in medical services12.4.2 Artificial intelligence (AI) and machine learning (ML) applications12.4.3 Blockchain in healthcare12.5 Cybersecurity challenges in healthcare12.5.1 Threats to patient data privacy12.5.2 Operational disruptions and system vulnerabilities12.5.3 Regulatory compliance and data protection12.5.4 Shortage of cybersecurity talent and training12.6 Safeguarding healthcare data in the quantum age12.7 Innovations in healthcare cybersecurity12.7.1 Adaptive cybersecurity frameworks12.7.2 Encryption and decentralized security models12.7.3 Collaborative cybersecurity initiatives12.8 Case studies and efficacy metrics models12.9 Future directions12.10 Conclusion
Sarita, Riya Sapra, Yogita Yashveer Raghav Chapter 13 Introduction to quantum computing and its revolution in industry and society
13.1 Introduction to quantum computing13.1.1 Algorithms13.2 Working mechanism of quantum computing13.3 Fundamental concepts of quantum computing13.4 A Brief growth of quantum technology13.4.1 Old technology theory’s theoretical foundations (1900–1980)13.4.2 Emergence of quantum computing (1980–1994), quantum mechanics, and computer science13.4.3 Development of quantum algorithms (1994–2000)13.4.4 The 2000–2021 race to build quantum computers experimental milestones13.4.5 Continuous improvements (2021-present): quantum supremacy13.5 Emergence of quantum physics13.6 Demands for quantum computing13.6.1 Benefits and drawbacks13.7 Applications of quantum computing13.7.1 Summary of applications of quantum computing13.7.2 Quantum computing in the AI era and supply chain optimization13.8 Quantum computing revolutionizes industry and society13.8.1 The impact of quantum computing on business, industry, and society13.9 Conclusion
Amena Mahmoud, Surjeet Dalal, Umesh Kumar Lilhore Chapter 14 Advancing healthcare through the opportunities and challenges of quantum computing
14.1 Introduction14.1.1 Basics of quantum mechanics14.2 Applications of quantum computing in healthcare14.2.1 Application of quantum computing in healthcare14.2.2 Drug discovery and development14.3 Challenges and limitations14.3.1 Challenges14.3.2 Hardware constraints14.4 Ethical implications of quantum computing in healthcare14.4.1 Privacy and data security14.5 Future directions and opportunities14.5.1 Integration with AI and machine learning14.6 Conclusion
Bijeta Seth, Surjeet Dalal, Umesh Kumar Lilhore Chapter 15 Quantum computing in drug and chemical
15.1 Fundamentals of quantum computing15.1.1 Navigating the intricacies of quantum gates and circuits15.1.2 Unveiling the key principles of quantum mechanics15.2 Drug discovery using quantum algorithms15.2.1 Quantum algorithms for molecular structure simulation15.3 Quantum chemistry models15.3.1 Quantum algorithms to revolutionize electrical structure computations15.4 Drug design and quantum computing15.4.1 Quantum computing for molecular modelling advances15.5 Quantum computing in rational drug design15.5.1 Effective drug design15.5.2 Quantum processors & atom gatesQuantum processors & atom gates15.6 Challenges15.6.1 Quantum hardware in chemistry: Potential and recent advances15.6.2 A comprehensive analysis of quantum noise issues in chemical models15.6.3 Fixed errors affect drug discovery models15.6.4 Effects of correcting drug discovery models15.7 Consequences for society and ethics15.7.1 Privacy and security in quantum computing for chemical and pharmaceutical research15.7.1 Social and regulatory impacts15.8 Conclusion
Index

Overview

"Industrial quantum computing" (IQC) covers the applications of quantum computing innovations in general industry and industry 4.0. This book presents the application of quantum computations to the financial sector, medical services, the logistics industry, and the manufacturing industry.

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