book

Sustainable Blind Quantum Computing

Name: Sustainable Blind Quantum Computing
ISBN: 9783111265179

by Srinjoy Ganguly, Surbhi Bhatia, Adarsh Kumar

September 2025

Intermediate to advanced

350 pages

13h 8m

English

De Gruyter

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Frontmatter
Contents
List of contributing authors
Apoorv Kumar Masta Chapter 1 Geometric quantum machine learning (GQML): concepts, challenges, and applications
1 Motivations for geometric quantum machine learning (GQML) over traditional QML1.1 Enhanced efficiency1.2 Improved accuracy and generalization1.3 Addressing quantum model limitations1.4 Applications in quantum algorithms1.5 Better alignment with physical systems1.6 Advancing quantum machine learning1.7 Recent advances in GQML2 Overview of group representation theory2.1 Basic concepts2.2 Applications in quantum mechanics2.3 Importance in machine learning and data science2.4 Equivariance and invariance2.5 Constructing representations2.6 Recent advances3 Definition and importance of group representations3.1 Key components3.2 Importance of group representations3.3 Latest progress in quantum machine learning with group representation theory4 Linear representations: matrix groups and actions4.1 Linear representations4.2 Matrix groups4.3 Actions4.4 Value in GQML5 Character theory and irreducible representations5.1 Irreducible representations5.2 Character theory5.3 Use in quantum mechanics6 Symmetry in physics and mathematics6.1 Symmetry groups: point groups, space groups, and Lie groups7 Equivariant and invariant models7.1 Maps and functions with equivariance7.2 Building equivariant models7.3 Equivariant models7.4 Symmetry-invariant neural networks (E-CNNs) and quantum models7.5 Significance in GQML7.6 A new family of symmetry-adapted deep learning architectures for invariant and equivariant Reynolds networks8 Capturing symmetries in GQML8.1 Concept of label invariance under group representation8.2 Label invariance interpretation8.3 Use cases8.4 Advantages of label invariances8.5 Symmetries in GQML: a novel perspective9 How to recognize symmetries in data and problems9.1 Methods for identifying symmetries9.2 Implementing symmetry detection in quantum models9.3 Future direction and hardships10 Constructing equivariant quantum models10.1 Transforming standard gates into equivariant gates10.2 Process of transformation10.3 Constraints due to the symmetries10.4 Equivariant gate examples10.5 The gate symmetrization procedure10.6 Symmetrization methods10.7 Theory for equivariant quantum neural networks10.8 Rotationally equivariant quantum machine learning open for training with guaranteed performance11 Usage and advantages11.1 Efficiency11.2 Better accuracy11.3 Robustness12 Equivariant quantum circuits: examples and implication on quantum machine learning12.1 Quantum convolutional neural networks (QCNNs)12.2 Symmetries under rotation12.3 Equivariant quantum classifiers12.4 Symmetric quantum simulations12.5 Effects on quantum machine learning12.6 Recent advancements in quantum circuits for machine learning and graph data analysis13 Advantages of geometric quantum machine learning (GQML)13.1 Good trainability and generalization13.2 Potential for quantum computational advantage13.3 New developments on quantum computational advantage14 Outlook on the role of GQML in the future of quantum machine learning14.1 Scaling to large or continuous symmetry groups14.2 Alleviating barren plateaus14.3 Combining GQML with other methods15 Conclusion
Abhishek Chakraborty Chapter 2 Quantum simulation of singlet fission for increasing solar cell efficiency
1 Introduction2 Singlet fission: a promising solution to increase solar cell efficiency3 Challenges in simulating singlet fission4 Quantum mechanics and quantum computers5 Quantum simulation: a powerful tool6 Quantum algorithms for singlet fission6.1 Quantum phase estimation6.2 Variational quantum eigensolver 6.3 Quantum approximate optimization algorithm (QAOA)6.4 Quantum walk algorithms6.5 Quantum machine learning7 Method8 Challenges and mitigation9 Commercial importance10 Solar power: additional uses of quantum simulation11 Conclusion
Shaik Khaja Mohiddin, Shaik Sharmila, Vijaya Chandra Jadala, Syed Ziaur Rahman Chapter 3 Blockchain integration in supply chain management
1 Introduction2 Benefits of blockchain integration in SCM2.1 Reduced fraud and counterfeiting in the supply chain2.2 The supply chain has become more efficient2.3 Improved communication and coordination throughout the supply chain3 Challenges of blockchain integration in SCM3.1 Implementing blockchain technology for SCM presents technical obstacles3.2 Regulatory and legal challenges of using blockchain technology in SCM3.3 Integration with existing systems and technologies4 Use cases of blockchain integration in SCM4.1 Examples of companies using blockchain technology to improve their SCM, such as Walmart, Maersk, and Nestle4.2 Case studies on how blockchain technology has been used to address specific supply chain challenges, such as food safety and provenance5 Future directions and opportunities5.1 Possibilities for developing and expanding the field6 Conclusion
Parul Gandhi, Ms. Priyanka Sharma, Ms. Aastha Budhiraja, Ritu Sachdeva Chapter 4 Quantum secrets: safeguarding privacy in computational protocols
1 Introduction1.1 The need for privacy in quantum computation1.2 The quantum leap: opportunities and risks1.3 Privacy in the digital age1.4 Emerging threats and the urgency for solutions1.5 Why privacy matters in quantum computing1.6 How quantum principles such as superposition and entanglement make novel privacy solutions possible1.7 Applications in privacy solutions2 Unique privacy features enabled by quantum principles2.1 No-cloning theorem2.2 Quantum randomness2.3 Tamper evidence2.4 Emerging quantum privacy solutions2.5 Challenges and limitations3 Technical challenges in quantum privacy3.1 Limitations of current quantum hardware3.2 Managing errors and noise in quantum systems3.3 Sources of errors and noise in quantum systems3.4 Balancing privacy, efficiency, and computational resources3.5 Quantum state preparation and measurement3.6 Security vulnerabilities in implementation4 Ethical and policy considerations4.1 Implication of quantum privacy on surveillance and data sovereignty4.2 Ethical challenges of using quantum technology for privacy and security4.3 Policy and regulatory requirements to guide responsible development5 Quantum secrets in action: Case studies and applications5.1 Introduction to quantum secrets5.2 Applications of QKD in finance, healthcare, and defense6 The road ahead: Innovations and opportunities6.1 Emerging technologies in quantum secure computation6.2 PQC integration into the classical systems involves6.3 Quantum-enhanced AI and privacy-preserving machine learning
Ritu Sachdeva, Ms. Aastha Budhiraja, Parul Gandhi, Priyanka Sharma Chapter 5 Invisible inputs: the future of private quantum computing
1 Introduction1.1 Importance of privacy in quantum computing1.2 Invisible inputs1.3 Key techniques for ensuring privacy in quantum computing2 Principles of BQC2.1 Definition and objectives of BQC2.2 Significant features of BQC2.3 Computation via BQC2.4 BQC models3 Ensuring privacy and computation integrity3.1 Techniques for ensuring computation integrity3.2 BQC protocols3.3 Quantum cryptographic techniques in security and privacy3.4 Illustration of invisible inputs preservation during computation4 Applications of BQC5 Use cases of BQC6 Problems/ challenges with BQC7 Future research roadmap8 Advancements and future prospects8.1 New development in the BQC protocol and implementation of experiments8.2 Emerging technologies that enable efficient blind quantum computing9 Practical use and interaction with classical systems10 Private quantum computation10.1 Importance of private quantum computation10.2 Key mechanisms for making inputs invisible10.3 How blind quantum computing can reshape the future of secure computation11 Machine learning in quantum computing: A comprehensive overview11.1 Key advantages of QML over classical ML11.2 Key concepts in QML11.3 Quantum algorithms for machine learning11.4 Quantum generative models11.5 Challenges in QML11.6 Tools and frameworks for QML11.7 Future of QML12 Conclusion of the chapter: A quantum leap for safe computation
Luis Gerardo Ayala Bertel, Ricardo Vivas-Reyes, Srinjoy Ganguly Chapter 6 First Steps Toward Graph-Based and Quantum Tools for Omics Expression Analysis
1 Introduction2 Omics data and perturbation studies2.1 Gene expression analysis2.2 Proteomics in systems biology2.3 Big data challenges and methodological approaches3 Network theory in systems biology3.1 Mechanisms of cAMP signaling: bottom-up approach3.2 Computational tools4 Quantum computing in graph analysis4.1 Integrating quantum and classical approaches5 Applications and case studies6 Discussion7 Conclusion
Sneha Kandacharam, Poonam Chaudhary, Rita Chhikara Chapter 7 Cybersecurity in the quantum era
1 Introduction2 Quantum computing threats to cryptography2.1 Vulnerabilities in classical cryptographic systems2.2 Related work3 Assessing cyber risks in classic cryptographic standards and quantum computingQuantifying quantum threatsProbable likelihood of quantum threat is calculated as:4 Strategic vulnerability assessment4.1 Infrastructure layers at risk4.2 Mitigation strategies5 Recommendations6 Conclusion

Arnav Dua, Vinay Aseri, Khadija Slimani, Samanvay Patel, Abhinav Singh Chapter 8 Quantum computing and blind computing for secure futuristic networks
1 Introduction1.1 Overview of modern network security challenges1.2 Emerging technologies for securing future networks2 Fundamentals of quantum computing2.1 Key concepts of quantum computing: qubits, superposition, and entanglement2.2 Quantum algorithms and their impact on cryptography2.3 Threats to classical cryptographic systems3 Blind computing: enhancing privacy3.1 Definition and key principles of blind computing3.2 Cryptographic techniques of blind computing3.3 Applications of blind computing in securing network4 Quantum-safe cryptography for future networks4.1 Post-quantum cryptography4.2 Quantum-resistant algorithm types4.3 Implementing quantum-safe solutions in networks4.4 Challenges in adoption of quantum-safe cryptography5 Integration of quantum computing and blind computing6 Conclusion
Shalini Devendrababu, Vaishnavi Chandilkar Chapter 9 Materials science for superconducting and photonic qubits
1 Introduction2 Fundamentals of materials science2.1 Structure2.2 Properties2.3 Designing and engineering of materials3 Role of materials science in quantum industry4 Superconducting qubits4.1 London and London’s theory (phenomenological approach)4.2 F. London’s quantum theoretic approach5 Types of superconductors5.1 Type I superconductors5.2 Type II superconductors6 Properties of superconductors6.1 Cooper pairs6.2 Meissner effect6.3 Zero electrical resistance7 High-temperature superconductors8 Materials in superconductors8.1 Two-level system (TLS)8.2 Surface oxides and interfaces8.3 Substrate defects8.4 Josephson junction imperfections8.5 Charge trapping9 Photonic qubits9.1 Introduction to photonic qubits9.2 Material platforms for photonic qubit generation9.3 Characterization of single-photon sources9.4 Photon purity9.5 Generation of photonic qubits by single-photon sources
Sakyojit Banerjee Chapter 10 Quantum cryptography and its advances
1 Introduction1.1 Objectives and contributions of work1.2 Organization of work2 Literature review3 Background3.1 Quantum advantage over classical cryptography4 Analysis of few advanced methods of quantum cryptography5 Practical applications of quantum cryptography5.1 Secure communications5.2 Government and military applications5.3 Financial transactions5.4 IoT devices5.5 Ensuring secure communication in smart devices6 Challenges of quantum cryptography7 Future of quantum cryptography8 Conclusion
Krishna, Param Pathak Chapter 11 Quantum security in 6G networks
1 Introduction1.1 Quantum-grade security in mobile networks1.2 Mobile computing1.3 Wireless networks1.4 6G mobile communication networks2 Quantum computing as a threat2.1 Cryptography2.2 Quantum computing2.3 Shor’s algorithm2.4 Grover’s search algorithm2.5 Impact on wireless mobile networks3 Security approaches3.1 Quantum key distribution3.2 Post-quantum cryptography4 Comparative and hybrid approaches4.1 Challenges in QKD4.2 PQC versus QKD4.3 Hybrid approaches4.4 Blind quantum computation and related protocols5 Conclusion
Vinay Aseri, Arnav Dua, Rajwinder Singh Mankoo, Harish Chowdhary Chapter 12 Quantum blind computing for privacy-preserving medical diagnosis
1 Introduction1.1 Overview of privacy concerns in medical diagnostics1.2 Importance of secure medical data handling1.3 Introduction to quantum computing and blind computing1.4 Purpose and scope of the chapter2 Understanding privacy-preserving techniques2.1 Traditional privacy-preserving methods2.2 Challenges in privacy preservation in medical diagnostics2.3 Limitations of classical privacy-preserving methods3 Basics of quantum computing3.1 Relevance of quantum computing to privacy and security4 Quantum blind computing for medical diagnostics4.1 Integration of quantum blind computing with medical diagnostic tools4.2 Quantum protocols for secure medical data processing5 Case study: privacy-preserving telemedicine platform using quantum key distribution6 Future prospects and research directions7 Conclusion
A. Hemantha Kumar, P. Veera Sanjeeva Kumar, R. Madhan Mohan Chapter 13 Enhancement of cycle boundaries in WEDM on Al7075 with nanoparticles of SiC and TiC
1 Introduction2 Materials and methodology2.1 Mechanism of metal removal rate (MRR)2.2 Design of experiments (DOEs)2.3 Analysis of variance (ANOVA)3 Experimental work4 Results and discussion5 Conclusions
Sai Nandan Morapakula Chapter 14 Design and deployment of a quantum algorithm on cloud
1 Introduction2 Variational quantum algorithm2.1 Architecture2.2 Ansatzes2.3 Optimizers2.4 Applications2.5 Problems related to hardware3 Variational quantum eigensolver4 Summary of VQE4.1 Steps to construct VQE5 Streamlit5.1 Introduction to Streamlit5.2 How to use Streamlit5.3 Building a quantum computing app with Streamlit5.4 Web application of VQE using streamlit5.5 How to use the VQE calculator5.6 Example: calculating the ground-state energy of {H2}5.7 Challenges and solutions6 Deploying the app6.1 Introduction to AWS6.2 Deploying the app on Streamlit Sharing7 Comparison: AWS versus Streamlit Sharing8 Conclusion
Index
Already published in the series
Index

Content preview from Sustainable Blind Quantum Computing

Chapter 1 Geometric quantum machine learning (GQML): concepts, challenges, and applications

Apoorv Kumar Masta

Abstract

To understand the intricacies of quantum geometric machine learning (QGML), it is essential to first examine the foundational concepts of geometric deep learning (GDL). This section addresses key questions such as: “Why is GDL necessary?”, “What advantages does it offer over traditional non-GDL?”, and “How does its complexity compare?”. We also explore the types of data required for GDL models and the preprocessing steps involved, which naturally leads to a discussion on why QGML provides significant advantages over classical GDL.

The data commonly used in deep learning is Euclidean (text, image, and audio), meaning it ...

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Convergence: Artificial Intelligence and Quantum Computing

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ISBN: 9783111265179

Cloud Computing

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Sustainable Blind Quantum Computing

by Srinjoy Ganguly, Surbhi Bhatia, Adarsh Kumar