book

Quantum Machine Learning

Name: Quantum Machine Learning
ISBN: 9783111342597

by Pethuru Raj, Houbing Herbert Song, Dac-Nhuong Le, Narayan Vyas

August 2024

Intermediate to advanced

327 pages

11h 59m

English

De Gruyter

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Kavita Arora, Neha Gupta, Rashmi Agrawal, Nguyen Ha Huy Cuong 1 Quantum computing: a paradigm shift from conventional computing
1.1 Introduction to quantum computing1.1.1 New kind of computing1.1.2 Quantum computing1.2 Need for quantum computers1.3 Fundamentals of quantum computing1.4 Origin point of bits1.5 Properties of quantum computing1.6 Superposition, entanglement, and interference1.7 Topography of quantum mechanics1.7.1 Classical versus quantum computing1.8 The framework of quantum machines1.9 Hardware and software requirements of quantum computers1.9.1 Hardware requirements1.9.2 Software requirements1.10 An insight into quantum algorithm1.11 Impediments of quantum computers1.12 Advents of quantum computing1.13 Pros and cons of quantum computing1.13.1 Pros of quantum computing1.13.2 Cons of quantum computing1.14 Application areas of quantum computing1.15 Limitations of quantum computing1.16 Contribution of quantum computing1.17 Conclusion1.18 Future scope of quantum computing
Neera Batra, Sonali Goyal, Amandeep Kaur, Rakhi Chauhan 2 An exploration of quantum computing: concept, architecture, and innovative applications
2.1 Introduction2.1.1 Conceptual understanding of quantum computing2.1.2 Quantum computing approaches2.1.3 Motivation2.1.4 Objectives2.2 Literature survey2.3 Concept and architecture2.3.1 Quantum computing: its rich and promising past2.3.2 Quantum algorithms2.3.3 Quantum computing models2.3.4 Key issues of quantum computing2.4 Architecture2.4.1 Physical layer or hardware2.4.2 Approaches for physically representing and managing qubits2.5 Quantum logical layer2.5.1 Application and programming layer2.6 Research gaps2.7 Challenges2.7.1 Quantum hardware challenges2.7.2 Quantum decoherence and error correction2.7.3 Quantum communication and networking2.7.4 Resource overhead2.7.5 Scalability2.7.6 Standardization and interoperability2.7.7 Innovative applications of quantum computing2.8 Challenges and future directions2.9 Conclusion
Dankan Gowda V, Avinash Kumar, Belsam Jeba Ananth M, Vasanthakumar G U, Mandeep Singh 3 Quantum machine learning in healthcare: diagnostics and drug discovery
3.1 Introduction3.2 Rise of QML3.2.1 Quantum computing: a glimpse into the quantum realm3.2.2 Machine learning and healthcare: a synergistic potential3.2.3 Quantum supremacy to quantum utility3.2.4 Computational advantage in healthcare3.3 Diagnostics: the frontier for QML3.3.1 Quantum computing meets medical imaging3.3.2 Genomic sequencing and analysis3.3.3 Proteomics and complex biomarkers3.3.4 Predictive diagnostics3.3.5 Personalized medicine3.3.6 Challenges in QML diagnostics3.4 Drug discovery through quantum algorithms3.4.1 Quantum-assisted molecular modeling3.4.2 Enhancing the screening process3.4.3 Simulation of biochemical reactions3.4.4 Optimizing drug properties3.4.5 Personalized medicine and quantum chemistry3.4.6 Challenges and considerations3.5 Overcoming the hurdles of implementation3.5.1 Technical limitations and quantum hardware3.5.2 Scalability and integration3.5.3 Algorithm development3.5.4 Data privacy and security3.5.5 Regulatory and ethical considerations3.5.6 Collaboration between disciplines3.6 Ethical and practical considerations3.6.1 Ethical concerns3.6.2 Practical considerations3.7 Conclusion
Kanu Priya Baheti, Purushender Dhiman 4 Quantum machine learning in finance
4.1 Introduction4.2 Quantum computing principles4.2.1 Quantum bits (qubits)4.2.2 Superposition4.2.3 Entanglement4.2.4 Quantum gates4.2.5 Quantum interference4.2.6 Measurement4.2.7 No-cloning theorem4.2.8 Quantum error correction4.3 Applications of quantum machine learning in finance4.3.1 Option pricing4.3.2 Fraud detection4.3.3 Swap netting4.3.4 Time series analysis and forecasting4.3.5 Risk management4.4 Challenges and ethical considerations4.4.1 Challenges4.4.2 Ethical considerations4.5 Conclusion
Ronak Duggar, Nesma E. ElSayed 5 Crucial role of blockchain in quantum computing: enhancing security and trust
5.1 Introduction5.1.1 Brief overview of quantum computing’s potential impact on cryptography5.1.2 The need for enhanced security mechanisms in the quantum era5.1.3 The role of blockchain technology in addressing quantum threats5.2 Quantum computing fundamentals5.2.1 Concise explanation of quantum bits (qubits and their properties)5.2.2 Quantum algorithms and their implications for cryptography5.2.3 Implications for cryptography5.2.4 Shor’s algorithm and Grover’s algorithm: threats to classical encryption5.2.5 Algorithm5.3 Grover’s algorithm5.4 Algorithm5.4.1 The timeline for practical realization of quantum computers5.5 Blockchain technology primer5.5.1 Key components and features of blockchain technology5.5.2 Proof of work versus proof of stake (consensus mechanisms)5.5.3 The challenges of current blockchain systems5.6 Quantum threats to cryptography5.6.1 Elaboration on the vulnerabilities of classical cryptographic schemes5.6.2 The timeline for the emergence of quantum-safe cryptographic algorithms5.7 Blockchain as a quantum-resistant solution5.8 Quantum-secure smart contracts5.9 Quantum key distribution (QKD) and blockchain5.9.1 Quantum-secure blockchains5.9.2 Secure key distribution for blockchain nodes5.9.3 Decentralization with quantum security5.9.4 Future applications
Neha Bhati, Aradhya Pokhriyal, Abeer Saber 6 Algorithmic exploration of unveiling fault tolerance in quantum machine learning
6.1 Introduction6.1.1 Quantum principles: the pillars of quantum reality6.1.2 Tracing the quantum legacy: a historical perspective6.1.3 Quantum innovations: the nexus of theory and application6.2 Transitioning from classical bits to the quantum world of qubits6.2.1 Contrasting binary and superpositional thinking6.2.2 Quantum gates and the quantum mechanism6.2.3 Measurement in quantum systems6.3 Synergy between quantum computers and machine learning6.3.1 The quantum benefit: acceleration due to quantum effects6.3.2 Quantum-enhanced optimization for navigating solutions6.3.3 Hybrid models, the best of both worlds6.4 Exploring the quantum world of algorithms6.4.1 The quantum Fourier transform6.4.2 Quantum sleuth Grover’s search algorithm6.4.3 Quantum neural networks (QNNs): a deep dive6.5 Fault tolerance6.5.1 Fault tolerance in quantum computing and machine learning6.5.2 The role of fault tolerance6.5.3 Quantum error correction methods6.5.4 Fault tolerance in machine learning algorithms6.5.5 Comparative analysis of fault tolerance techniques6.6 Bagging (bootstrap aggregating algorithm)6.6.1 Methods of algorithms6.7 Random forest algorithm6.7.1 Methods of algorithms6.8 k-Fold cross-validation algorithm6.8.1 Methods of algorithms6.8.2 Difficulties and possible future paths6.9 The new age of quantum computing and data processing6.9.1 Quantum encoding6.9.2 Multiverses of computations6.9.3 The quantum guardian: quantum error correction6.10 Quantum computing: giving machine learning a fresh start6.10.1 Quantum datasets: weaving a quantum web of data6.10.2 The role of QML in business6.10.3 Real-world case histories of quantum ML6.11 QML and the maze of uncertainty6.11.1 The problem with quantum hardware6.11.2 The symbiotic nature of scalability6.11.3 Connecting the quantum and classical worlds6.12 QML and ethics6.12.1 Information security6.12.2 Relevance to society6.12.3 Difficulties with regulation6.13 Future directions6.13.1 Future of machine learning beyond the quantum horizon6.13.2 Evolution of quantum computing hardware6.13.3 The acceptance of quantum technology by industry6.13.4 Researchers on the cutting edge of the quantum frontiers6.13.5 Projected timeline of quantum developments6.14 Conclusion and prospects for the future6.14.1 An overview and major contributions6.14.2 Limitations and recommendations6.14.3 Future research directions
Yash Mahajan, Muskan Sharma, Abdullah Alzahrani 7 Quantum machine learning in renewable energy systems
7.1 Introduction7.1.1 Background7.1.2 Objectives7.2 Literature review7.3 Quantum computing and renewable energy7.3.1 Simulating quantum systems7.3.2 Simulating renewable energy systems7.3.3 Quantum computers: precision pioneers7.3.4 Quantum superposition in renewable energy optimization7.3.5 Harnessing entanglement for renewable energy modeling7.3.6 Quantum entanglement in materials discovery7.3.7 Quantum superpowers being used7.3.8 Useful application: quantum-enhanced optimization of solar farms7.4 Conclusion
Malik Muzamil Ishaq, Inam Ul Haq, Aya Gamal 8 Decentralized quantum machine learning: distributed quantum computing for enhanced learning
8.1 Introduction to decentralized quantum machine learning (DQML)8.1.1 Evolution of quantum computing and machine learning8.1.2 The concept of decentralized systems8.1.3 Motivation for DQML: bridging the gap8.2 Fundamentals of quantum computing and machine learning8.2.1 Quantum mechanics premier8.2.2 Quantum circuits and quantum gates8.2.3 Quantum algorithms for machine learning8.2.4 Classical machine learning techniques8.3 Decentralized systems and distributed ledger technologies introduction to decentralization and distributed systems8.3.1 Blockchain and distributed ledger technologies8.3.2 Smart contracts and data sharing in decentralized networks8.4 The convergence of quantum computing and decentralized systems8.4.1 Challenges in combining quantum and decentralized technologies8.4.2 Quantum key distribution and secure communication8.4.3 Quantum consensus mechanisms for decentralized networks8.5 Privacy-preserving data analysis with DQML8.5.1 Quantum SMPC8.5.2 Quantum homomorphic encryption8.5.3 Encrypted inference and data sharing in DQML8.6 Conclusion
Ashutosh Pagrotra, Vedant Dhiman 9 Quantum reinforcement learning: decision-making in quantum environments
9.1 Introduction9.2 Understanding reinforcement learning9.2.1 Reinforcement learning basics9.2.2 Challenges in traditional reinforcement learning9.3 Fundamental concepts of quantum mechanics9.3.1 Some key concepts of quantum mechanics9.3.2 New archetype: QRL9.4 QRL overview9.4.1 Key foundations of QRL9.4.2 Why QRL is a big deal?9.5 Quantum agents and environments: what are quantum agents?9.5.1 Quantum agents: utilizing quantum properties for learning9.5.2 What are quantum environments?9.5.3 Beyond classical simulation: quantum environments9.6 Quantum Q-learning9.6.1 The Q-learning algorithm9.6.2 Quantum Q-learning using Bellman equations: combining quantum dynamics and decision-making9.6.3 Classical Bellman equation in reinforcement learning9.6.4 Quantum state spaces9.6.5 Quantum actions9.6.6 Quantum rewards9.6.7 Quantum Bellman equation in quantum Q-learning9.7 Quantum deep reinforcement learning (QDRL)9.7.1 Bridging quantum and deep learning9.7.2 Quantum neural networks in QRL9.7.3 Quantum deep reinforcement learning architectures9.8 Quantum noise and error correction9.8.1 What’s quantum noise and errors?9.8.2 The impact of quantum noise and errors9.8.3 Quantum error correction9.8.4 Some key components of quantum error correction9.9 Hybrid quantum-classical approaches9.9.1 What is hybrid quantum-classical computing?9.9.2 Key elements of hybrid quantum-classical approach9.9.3 Classical processing and postprocessing9.9.4 Quantum computation9.9.5 Quantum subroutines9.10 Quantum computing and quantum chemistry9.10.1 Quantum computing9.11 Finance and portfolio optimization: revolutionizing investment strategies QRL in finance market9.11.1 Key applications of QRL in finance9.11.2 QRL in portfolio optimization9.11.3 Application of QRL in portfolio optimization9.12 Quantum gaming and entertainment9.12.1 Quantum game development9.12.2 Challenges in quantum game development9.13 Limits of quantum hardware9.13.1 Quantum powered virtual reality9.14 Challenges in quantum-powered virtual reality: computational demands9.14.1 Quantum networking9.14.2 Quantum sensors9.15 Conclusion: navigating the quantum frontier the promise of QRL9.15.1 Quantum environments: unlocking undiscovered domains9.15.2 Impact on society: changing sectors and decision-making9.15.3 Democratizing quantum solutions with accessible quantum resources9.16 Key point of the chapter
Umesh Kumar Lilhore, Sarita Simaiya 10 Quantum machine learning in natural language processing: opportunities and challenges
10.1 Introduction10.2 Quantum computing fundamentals10.2.1 Fundamentals of quantum computing10.2.2 Essential notations10.2.3 Quantum computation and algorithms10.3 Quantum natural language processing: state-of-the-art quantum natural language processing10.3.1 Quantum algorithms for NLP10.4 Opportunities in QNLP10.4.1 Key opportunities in QNLP10.4.2 NLP real-world applications10.5 Challenges in QNLP10.5.1 Quantum hardware constraints10.5.2 Development of quantum algorithms10.5.3 Data prerequisites10.5.4 NLP models prepared for quantum computing10.5.5 Quantum access and resources10.6 QNLP use cases10.6.1 Analysis of healthcare and medical texts10.6.2 Linguistic translation for languages with limited resources10.6.3 Ensuring legal and regulatory compliance10.7 Conclusion and future direction

Hardik Dhiman, Maheshwar Dhiman 11 Unveiling intelligence: exploring variational quantum circuits as machine learning models
11.1 Introduction11.2 The principles of quantum computing11.2.1 Quantum bits or qubits11.2.2 Quasi-quantum gates11.2.3 The quantum state11.2.4 Foundation for VQC11.3 VQC: building blocks11.3.1 Quantum gates in VQCs11.3.2 Adjusting VQCs via gate parameters11.4 VQCs as optimization tools11.4.1 Cost functions for quantum ML11.4.2 Utilizing VQCs to reduce the cost functions11.4.3 The function of optimization methods11.4.4 Quantum–classical hybrid approach to using VQC11.4.5 The VQCs’ hybrid nature11.4.6 Collaboration of VQCs and classical optimization11.4.7 Partnership of classical and quantum elements11.4.8 Case study: simulation of quantum chemistry11.4.9 The synergy at work11.4.10 Practical implications11.5 Applications of VQCs in ML11.5.1 Quantum ML11.5.2 ML’s quantum advantage11.5.3 VQCs as quantum components11.5.4 Quantum feature mapping and kernel methods11.5.5 Mapping quantum features11.5.6 Function of VQCs11.5.7 Advantages of quantum feature mapping with VQCs11.6 Kernel method applications11.6.1 Quantum-enhanced optimization11.6.2 Quantum-assisted optimization and VQCs11.6.3 VQCs’ benefits for quantum-enhanced optimization11.6.4 VQCs in quantum-enhanced optimization: applications11.6.5 Quantum chemistry and molecular modeling11.6.6 VQCs’ function in quantum chemistry11.6.7 VQCs’ benefits for quantum chemistry11.6.8 Molecular applications for modeling11.6.9 Quantum data generation and generative models11.6.10 The role of VQCs in generative models11.6.11 VQCs in generative models: benefits11.6.12 Generative model applications11.7 Challenges and limitations of VQCs in ML11.7.1 Using and implementing VQCs in ML: challenges11.7.2 Noise and error correction in quantum systems: a problem11.7.3 Current limitations of quantum hardware for VQCs11.8 Case study: quantum chemistry simulation11.8.1 Background11.8.2 Objective11.8.3 Methodology11.8.4 Results11.8.5 Discussion11.8.6 Conclusion11.9 Future directions and trends11.10 Conclusion
Mahsa Radnejad, Khushdeep Kaur, Houbing Song, Lei Zhang 12 Methods and tools to improve quantum software quality: a survey
12.1 Introduction12.2 Quantum bugs12.2.1 Bug patterns12.2.2 Bug pattern detection techniques12.2.3 Quantum bug benchmark12.3 Quantum software testing12.3.1 Noise in quantum computing12.4 Quantum software debugging12.4.1 Debugging tactics in classical SE12.4.2 Debugging tactics in QSE12.5 Challenges and opportunities12.5.1 QSE research challenges12.5.2 Future research directions in QSE12.6 Conclusions
Manisa Manoswini, Debasish Swapnesh Kumar Nayak, Tejaswini Das, Tripti Swarnkar 13 Quantum-enhanced neural networks: bridging the quantum algorithm and machine learning
13.1 Introduction13.1.1 Motive and background13.2 Quantum principles13.2.1 Superposition13.3 Entanglement: a fascinating phenomenon of quantum physics13.4 Physical reversibility13.4.1 Interaction and measure of qubits13.5 Techniques for QML13.5.1 Grover’s search: quantum space search revolutionized13.5.2 Application and drawbacks13.5.3 Cryptography13.6 Oracle13.6.1 Algorithm13.7 Cryptographic implications of Shor’s factoring13.7.1 Quantum cryptography application areas13.8 Real-world applications13.8.1 Healthcare applications of quantum computing13.9 Conclusion
V. Dankan Gowda, Saptarshi Mukherjee, M. Belsam Jeba Ananth, L. Sri Ramachandra, Shafiqul Abidin 14 Future trends and research horizons in quantum machine learning
14.1 Quantum computing: a brief overview14.1.1 The quantum difference14.1.2 The mechanics of quantum computing14.1.3 Quantum supremacy and beyond14.1.4 Challenges on the horizon14.1.5 Quantum computing today14.2 Quantum leap in machine learning14.2.1 Enhanced computational speed and capacity14.2.2 Quantum machine learning algorithms (QMLAs)14.2.3 Quantum optimization14.2.4 Data encoding and quantum feature spaces14.2.5 Kernel methods in quantum domain14.2.6 Implications for deep learning14.2.7 Addressing quantum challenges through machine learning14.2.8 Quantum computing’s impact on AI research14.2.9 Caution and consideration14.3 Synergy of quantum algorithms and machine learning14.3.1 Quantum algorithms as a catalyst14.3.2 Enhancing machine learning models14.3.3 Quantum algorithmic thinking in ML14.3.4 Challenges and potential14.4 Redefining neural networks with quantum principles14.4.1 The quantum neuron14.4.2 Quantum convolutional neural networks (QCNNs)14.4.3 Quantum recurrent neural networks (QRNNs)14.4.4 Quantum gates as neuronal functions14.4.5 Training quantum neural networks14.4.6 Hybrid quantum–classical networks14.4.7 Challenges in realizing QNNs14.5 Pursuit of quantum advantage14.5.1 Defining quantum advantage in QML14.5.2 Benchmarking against classical algorithms14.5.3 Quantum supremacy in machine learning14.5.4 Use cases poised for quantum advantage14.5.5 Challenges to overcome14.6 Current challenges in QML14.6.1 Hardware limitations14.6.2 Algorithmic challenges14.6.3 Theoretical challenges14.6.4 Practical and ethical considerations14.6.5 Collaboration and education14.7 Symbiosis between quantum and classical systems14.7.1 Complementary strengths14.7.2 Hybrid quantum–classical algorithms14.7.3 Codevelopment of technologies14.7.4 Interoperability concerns14.7.5 Quantum-inspired algorithms14.7.6 Education and workforce development14.7.7 The transition era14.7.8 Nurturing the symbiosis14.8 Envisioning the future landscape of QML14.8.1 Accelerated discovery and innovation14.8.2 Transformation of industries14.8.3 Advancements in AI14.8.4 Ethical and secure computing14.8.5 Integration and accessibility14.8.6 Continuous learning and adaptation14.9 ConclusionBiographiesSeries editorsVolume editors
Index

Overview

Quantum computing has shown a potential to tackle specific types of problems, especially those involving a daunting number of variables, at an exponentially faster rate compared to classical computers. This volume focuses on quantum variants of machine learning algorithms, such as quantum neural networks, quantum reinforcement learning, quantum principal component analysis, quantum support vectors, quantum Boltzmann machines, and many more.

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A Practical Guide to Quantum Machine Learning and Quantum Optimization

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