Sustainable Blind Quantum Computing

Name: Sustainable Blind Quantum Computing
Brand: De Gruyter
Price: 149.95 EUR
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Srinjoy Ganguly Surbhi Bhatia Adarsh Kumar(Editor)

De Gruyter (Publisher)

1st Edition

Published on 9. September 2025

X, 340 pages

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978-3-11-126434-9 (ISBN)

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Content

Intro
Contents
List of contributing authors
Chapter 1 Geometric quantum machine learning (GQML): concepts, challenges, and applications
1 Motivations for geometric quantum machine learning (GQML) over traditional QML
1.1 Enhanced efficiency
1.2 Improved accuracy and generalization
1.3 Addressing quantum model limitations
1.4 Applications in quantum algorithms
1.5 Better alignment with physical systems
1.6 Advancing quantum machine learning
1.7 Recent advances in GQML
2 Overview of group representation theory
2.1 Basic concepts
2.2 Applications in quantum mechanics
2.3 Importance in machine learning and data science
2.4 Equivariance and invariance
2.5 Constructing representations
2.6 Recent advances
3 Definition and importance of group representations
3.1 Key components
3.2 Importance of group representations
3.3 Latest progress in quantum machine learning with group representation theory
4 Linear representations: matrix groups and actions
4.1 Linear representations
4.2 Matrix groups
4.2.1 Matrix Lie groups
4.3 Actions
4.4 Value in GQML
4.4.1 Quantum model construction
4.4.2 Reduction via symmetry
5 Character theory and irreducible representations
5.1 Irreducible representations
5.2 Character theory
5.2.1 Characters
5.2.2 Orthogonality relations
5.3 Use in quantum mechanics
5.3.1 Symmetry analysis
5.3.2 Quantum state decomposition
5.3.3 Efficient implementation of the quantum Schur transform: a generalized coupling method
6 Symmetry in physics and mathematics
6.1 Symmetry groups: point groups, space groups, and Lie groups
6.1.1 Point groups
6.1.2 Space groups
6.1.3 Lie groups
7 Equivariant and invariant models
7.1 Maps and functions with equivariance
7.1.1 Equivariant map
7.2 Building equivariant models
7.2.1 Quantum circuit design
7.3 Equivariant models
7.4 Symmetry-invariant neural networks (E-CNNs) and quantum models
7.4.1 E-CNNs
7.4.2 Quantum analogs
7.5 Significance in GQML
7.5.1 Improvement in model performance
7.5.2 Theoretical grounding
7.6 A new family of symmetry-adapted deep learning architectures for invariant and equivariant Reynolds networks
8 Capturing symmetries in GQML
8.1 Concept of label invariance under group representation
8.2 Label invariance interpretation
8.2.1 Action of groups on data
8.2.2 Mathematical formalism
8.2.3 Significance in quantum models
8.3 Use cases
8.4 Advantages of label invariances
8.4.1 Generalizability
8.4.2 Fewer training examples
8.4.3 Noise robustness
8.5 Symmetries in GQML: a novel perspective
9 How to recognize symmetries in data and problems
9.1 Methods for identifying symmetries
9.1.1 Data analysis
9.1.2 Group theory and algebraic techniques
9.1.3 Feature engineering based on symmetry
9.2 Implementing symmetry detection in quantum models
9.2.1 Invariant and equivariant layers
9.2.2 Twirling and averaging
9.2.3 Group convolution layers
9.3 Future direction and hardships
9.3.1 Harnessing the power of intricate symmetries
9.3.2 Hardships with further quantum methods
9.3.3 Applications in the real world
10 Constructing equivariant quantum models
10.1 Transforming standard gates into equivariant gates
10.2 Process of transformation
10.3 Constraints due to the symmetries
10.4 Equivariant gate examples
10.5 The gate symmetrization procedure
10.6 Symmetrization methods
10.6.1 Twirling
10.6.2 Choi operators
10.6.3 Projection onto invariant subspaces
10.7 Theory for equivariant quantum neural networks
10.7.1 Project on invariant subspaces
10.7.1.1 Theoretical discussion
10.7.2 Invariant subspaces project
10.7.2.1 Theoretical discussion
10.8 Rotationally equivariant quantum machine learning open for training with guaranteed performance
11 Usage and advantages
11.1 Efficiency
11.2 Better accuracy
11.3 Robustness
12 Equivariant quantum circuits: examples and implication on quantum machine learning
12.1 Quantum convolutional neural networks (QCNNs)
12.1.1 QCNNs for group-equivariant neural networks
12.1.2 Rotational symmetries
12.2 Symmetries under rotation
12.3 Equivariant quantum classifiers
12.4 Symmetric quantum simulations
12.5 Effects on quantum machine learning
12.5.1 Advanced learning algorithms
12.5.2 Scalability
12.6 Recent advancements in quantum circuits for machine learning and graph data analysis
13 Advantages of geometric quantum machine learning (GQML)
13.1 Good trainability and generalization
13.1.1 Utilizing symmetry
13.1.2 Incremental generalization
13.1.3 Reduction in training data requirements
13.2 Potential for quantum computational advantage
13.2.1 Quantum parallelism
13.2.2 Compact native quantum representations
13.2.3 Complex quantum systems
13.2.4 Symmetry-adapted ansatzes (SA)
13.2.5 Reduced search space
13.2.6 Gains on the robustness against noise
13.3 New developments on quantum computational advantage
14 Outlook on the role of GQML in the future of quantum machine learning
14.1 Scaling to large or continuous symmetry groups
14.2 Alleviating barren plateaus
14.3 Combining GQML with other methods
15 Conclusion
References
Chapter 2 Quantum simulation of singlet fission for increasing solar cell efficiency
1 Introduction
2 Singlet fission: a promising solution to increase solar cell efficiency
3 Challenges in simulating singlet fission
4 Quantum mechanics and quantum computers
5 Quantum simulation: a powerful tool
6 Quantum algorithms for singlet fission
6.1 Quantum phase estimation
6.2 Variational quantum eigensolver
6.3 Quantum approximate optimization algorithm (QAOA)
6.4 Quantum walk algorithms
6.5 Quantum machine learning
7 Method
8 Challenges and mitigation
9 Commercial importance
10 Solar power: additional uses of quantum simulation
11 Conclusion
References
Chapter 3 Blockchain integration in supply chain management
1 Introduction
2 Benefits of blockchain integration in SCM
2.1 Reduced fraud and counterfeiting in the supply chain
2.2 The supply chain has become more efficient
2.3 Improved communication and coordination throughout the supply chain
3 Challenges of blockchain integration in SCM
3.1 Implementing blockchain technology for SCM presents technical obstacles
3.2 Regulatory and legal challenges of using blockchain technology in SCM
3.3 Integration with existing systems and technologies
4 Use cases of blockchain integration in SCM
4.1 Examples of companies using blockchain technology to improve their SCM, such as Walmart, Maersk, and Nestle
4.2 Case studies on how blockchain technology has been used to address specific supply chain challenges, such as food safety and provenance
5 Future directions and opportunities
5.1 Possibilities for developing and expanding the field
6 Conclusion
References
Chapter 4 Quantum secrets: safeguarding privacy in computational protocols
1 Introduction
1.1 The need for privacy in quantum computation
1.2 The quantum leap: opportunities and risks
1.3 Privacy in the digital age
The concept of privacy in quantum computing
1.4 Emerging threats and the urgency for solutions
1.5 Why privacy matters in quantum computing
1.5.1 Foundations of privacy in quantum computing
1.6 How quantum principles such as superposition and entanglement make novel privacy solutions possible
1.6.1 Superposition: the secure information encoding principle
1.6.2 Quantum entanglement
1.7 Applications in privacy solutions
2 Unique privacy features enabled by quantum principles
2.1 No-cloning theorem
2.2 Quantum randomness
2.3 Tamper evidence
2.4 Emerging quantum privacy solutions
2.5 Challenges and limitations
3 Technical challenges in quantum privacy
3.1 Limitations of current quantum hardware
3.2 Managing errors and noise in quantum systems
3.3 Sources of errors and noise in quantum systems
3.4 Balancing privacy, efficiency, and computational resources
Privacy
Efficiency
3.5 Quantum state preparation and measurement
3.6 Security vulnerabilities in implementation
4 Ethical and policy considerations
4.1 Implication of quantum privacy on surveillance and data sovereignty
4.1.1 Implication of surveillance
4.1.2 Implication of data sovereignty
4.2 Ethical challenges of using quantum technology for privacy and security
4.3 Policy and regulatory requirements to guide responsible development
5 Quantum secrets in action: Case studies and applications
5.1 Introduction to quantum secrets
5.1.1 Key cryptographic applications in the quantum age include:
5.2 Applications of QKD in finance, healthcare, and defense
5.2.1 Applications of QKD in finance
Case studies of QKD in finance
5.2.2 Applications of QKD in healthcare
Case studies of QKD in healthcare
5.2.3 Applications of QKD in defense sector
Case studies of QKD in the defence sector
Conclusion
6 The road ahead: Innovations and opportunities
6.1 Emerging technologies in quantum secure computation
6.2 PQC integration into the classical systems involves
6.2.1 Quantum key distribution and hybrid communication networks
6.3 Quantum-enhanced AI and privacy-preserving machine learning
References
Chapter 5 Invisible inputs: the future of private quantum computing
1 Introduction
1.1 Importance of privacy in quantum computing
1.2 Invisible inputs
1.2.1 Role in secure quantum computing
1.3 Key techniques for ensuring privacy in quantum computing
2 Principles of BQC
2.1 Definition and objectives of BQC
2.1.1 Example scenario
2.1.2 Example of BQC in practice
2.2 Significant features of BQC
2.3 Computation via BQC
2.3.1 Example scenario
2.4 BQC models
Advantages of the circuit-based model
Limitations of the circuit-based model
Applications of the circuit-based model
Advantages of MBQC
Disadvantages of MBQC
Applications of MBQC
Advantages of the ancilla-driven model
Error resilience
Drawbacks of the ancilla-driven model
3 Ensuring privacy and computation integrity
3.1 Techniques for ensuring computation integrity
3.2 BQC protocols
3.3 Quantum cryptographic techniques in security and privacy
3.4 Illustration of invisible inputs preservation during computation
4 Applications of BQC
5 Use cases of BQC
6 Problems/ challenges with BQC
7 Future research roadmap
8 Advancements and future prospects
8.1 New development in the BQC protocol and implementation of experiments
8.2 Emerging technologies that enable efficient blind quantum computing
9 Practical use and interaction with classical systems
10 Private quantum computation
10.1 Importance of private quantum computation
10.2 Key mechanisms for making inputs invisible
10.3 How blind quantum computing can reshape the future of secure computation
11 Machine learning in quantum computing: A comprehensive overview
11.1 Key advantages of QML over classical ML
11.2 Key concepts in QML
11.3 Quantum algorithms for machine learning
11.4 Quantum generative models
11.5 Challenges in QML
11.6 Tools and frameworks for QML
11.7 Future of QML
12 Conclusion of the chapter: A quantum leap for safe computation
References
Chapter 6 First Steps Toward Graph-Based and Quantum Tools for Omics Expression Analysis
1 Introduction
2 Omics data and perturbation studies
2.1 Gene expression analysis
2.2 Proteomics in systems biology
2.2.1 Proteomic tools
2.3 Big data challenges and methodological approaches
3 Network theory in systems biology
3.1 Mechanisms of cAMP signaling: bottom-up approach
3.2 Computational tools
4 Quantum computing in graph analysis
4.1 Integrating quantum and classical approaches
5 Applications and case studies
6 Discussion
7 Conclusion
References
Chapter 7 Cybersecurity in the quantum era
1 Introduction
2 Quantum computing threats to cryptography
2.1 Vulnerabilities in classical cryptographic systems
2.2 Related work
A. Emerging trends and future directions in quantum-resistant security
B. The evolution of quantum computing and cryptography
C. Development and standardization of post-quantum cryptography
D. Impact of quantum computing on infrastructure security
E. Mitigating quantum threats in digital infrastructures
F. Strategies of major cloud providers in addressing quantum threats
Quantum attack scenarios
3 Assessing cyber risks in classic cryptographic standards and quantum computing
Quantifying quantum threats
Probable likelihood of quantum threat is calculated as:
3.1 Post-quantum cryptographic solutions
Quantum impact assessment
Transitioning to a quantum-safe cryptographic environment
Challenges beyond quantum-resistant algorithms
3.2 Risk assessment framework
4 Strategic vulnerability assessment
4.1 Infrastructure layers at risk
4.2 Mitigation strategies
5 Recommendations
6 Conclusion
References
Chapter 8 Quantum computing and blind computing for secure futuristic networks
1 Introduction
1.1 Overview of modern network security challenges
1.2 Emerging technologies for securing future networks
2 Fundamentals of quantum computing
2.1 Key concepts of quantum computing: qubits, superposition, and entanglement
2.2 Quantum algorithms and their impact on cryptography
2.3 Threats to classical cryptographic systems
3 Blind computing: enhancing privacy
3.1 Definition and key principles of blind computing
3.2 Cryptographic techniques of blind computing
3.2.1 Homomorphic encryption (HE)
3.2.2 Secure multiparty computation (SMPC)
3.3 Applications of blind computing in securing network
4 Quantum-safe cryptography for future networks
4.1 Post-quantum cryptography
4.2 Quantum-resistant algorithm types
4.3 Implementing quantum-safe solutions in networks
4.4 Challenges in adoption of quantum-safe cryptography
5 Integration of quantum computing and blind computing
6 Conclusion
References
Chapter 9 Materials science for superconducting and photonic qubits
1 Introduction
2 Fundamentals of materials science
2.1 Structure
2.2 Properties
2.3 Designing and engineering of materials
3 Role of materials science in quantum industry
4 Superconducting qubits
4.1 London and London's theory (phenomenological approach)
4.2 F. London's quantum theoretic approach
5 Types of superconductors
5.1 Type I superconductors
5.1.1 BCS theory
5.2 Type II superconductors
6 Properties of superconductors
6.1 Cooper pairs
6.2 Meissner effect
6.3 Zero electrical resistance
6.3.1 Critical magnetic field, {H_{\rm{c}}}
6.3.1.1 Summary of results
6.3.1.2 List of superconducting materials with critical temperature and critical magnetic field
6.3.1.2.2 Type II superconductors (compounds)
6.3.2 Current density
7 High-temperature superconductors
8 Materials in superconductors
8.1 Two-level system (TLS)
8.2 Surface oxides and interfaces
8.3 Substrate defects
8.4 Josephson junction imperfections
8.5 Charge trapping
9 Photonic qubits
9.1 Introduction to photonic qubits
9.2 Material platforms for photonic qubit generation
9.2.1 Silicon-based platforms
9.2.2 III-V semiconductors
9.3 Characterization of single-photon sources
9.4 Photon purity
9.4.1 Indistinguishability
9.4.2 Source efficiency
9.5 Generation of photonic qubits by single-photon sources
References
Chapter 10 Quantum cryptography and its advances
1 Introduction
1.1 Objectives and contributions of work
1.2 Organization of work
2 Literature review
3 Background
3.1 Quantum advantage over classical cryptography
4 Analysis of few advanced methods of quantum cryptography
5 Practical applications of quantum cryptography
5.1 Secure communications
5.2 Government and military applications
5.3 Financial transactions
5.4 IoT devices
5.5 Ensuring secure communication in smart devices
6 Challenges of quantum cryptography
7 Future of quantum cryptography
8 Conclusion
References
Chapter 11 Quantum security in 6G networks
1 Introduction
1.1 Quantum-grade security in mobile networks
1.2 Mobile computing
1.3 Wireless networks
1.4 6G mobile communication networks
2 Quantum computing as a threat
2.1 Cryptography
2.2 Quantum computing
2.2.1 Quantum gates and their matrices
2.3 Shor's algorithm
2.4 Grover's search algorithm
2.5 Impact on wireless mobile networks
3 Security approaches
3.1 Quantum key distribution
3.1.1 The principles of QKD
3.1.2 The BB84 protocol
3.1.3 The B92 protocol
3.1.4 The E91 protocol
3.1.5 Advanced QKD protocols
3.1.6 Comparative analysis
3.2 Post-quantum cryptography
3.2.1 Lattice-based cryptography
3.2.2 Code-based cryptography
3.2.3 Hash-based cryptography
3.2.4 Multivariate polynomial cryptography
3.2.5 Isogeny-based cryptography
3.2.6 NIST standards of PQC
4 Comparative and hybrid approaches
4.1 Challenges in QKD
4.2 PQC versus QKD
4.3 Hybrid approaches
4.4 Blind quantum computation and related protocols
5 Conclusion
References
Chapter 12 Quantum blind computing for privacy-preserving medical diagnosis
1 Introduction
1.1 Overview of privacy concerns in medical diagnostics
1.2 Importance of secure medical data handling
1.3 Introduction to quantum computing and blind computing
1.4 Purpose and scope of the chapter
2 Understanding privacy-preserving techniques
2.1 Traditional privacy-preserving methods
2.2 Challenges in privacy preservation in medical diagnostics
2.3 Limitations of classical privacy-preserving methods
3 Basics of quantum computing
3.1 Relevance of quantum computing to privacy and security
4 Quantum blind computing for medical diagnostics
4.1 Integration of quantum blind computing with medical diagnostic tools
4.2 Quantum protocols for secure medical data processing
5 Case study: privacy-preserving telemedicine platform using quantum key distribution
6 Future prospects and research directions
7 Conclusion
References
Chapter 13 Enhancement of cycle boundaries in WEDM on Al7075 with nanoparticles of SiC and TiC
1 Introduction
2 Materials and methodology
2.1 Mechanism of metal removal rate (MRR)
2.2 Design of experiments (DOEs)
2.3 Analysis of variance (ANOVA)
3 Experimental work
4 Results and discussion
5 Conclusions
References
Chapter 14 Design and deployment of a quantum algorithm on cloud
1 Introduction
2 Variational quantum algorithm
2.1 Architecture
2.2 Ansatzes
2.3 Optimizers
2.4 Applications
2.5 Problems related to hardware
2.5.1 Noise
2.5.2 Error mitigation
3 Variational quantum eigensolver
4 Summary of VQE
4.1 Steps to construct VQE
4.1.1 Constructing the Hamiltonian
4.1.2 Operator encoding
4.1.3 Selecting the ansatz and setting the state
4.1.4 Optimizing the parameter
5 Streamlit
5.1 Introduction to Streamlit
5.2 How to use Streamlit
5.2.1 Installing Streamlit
5.2.2 Creating your first Streamlit app
5.2.3 Core components of Streamlit
5.3 Building a quantum computing app with Streamlit
5.4 Web application of VQE using streamlit
5.4.1 Features of the VQE calculator
5.5 How to use the VQE calculator
5.6 Example: calculating the ground-state energy of {H2}
5.7 Challenges and solutions
6 Deploying the app
6.1 Introduction to AWS
6.1.1 Deploying the app on AWS
6.2 Deploying the app on Streamlit Sharing
7 Comparison: AWS versus Streamlit Sharing
8 Conclusion
References
Index
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Sustainable Blind Quantum Computing

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