8 Decentralized quantum machine learning: distributed quantum computing for enhanced learning
-
Malik Muzamil Ishaq
Abstract
Decentralized quantum machine learning (DQML) represents an innovative fusion of two cutting-edge technologies: quantum computing and decentralized/distributed systems. This chapter explores the emerging field of DQML and its potential to revolutionize traditional machine-learning paradigms by harnessing the power of quantum computing while leveraging the benefits of decentralized networks. This chapter delves into the underlying principles, challenges, and opportunities associated with DQML, offering insights into how this interdisciplinary approach can accelerate advancements in artificial intelligence and data processing. Moreover, this chapter discusses various use cases where DQML demonstrates its superiority over classical machine learning approaches including privacy-preserving data analysis, federated learning, and distributed optimization. Finally, this chapter discusses the future research directions and key considerations for the successful integration and deployment of DQML in real-world applications.
Abstract
Decentralized quantum machine learning (DQML) represents an innovative fusion of two cutting-edge technologies: quantum computing and decentralized/distributed systems. This chapter explores the emerging field of DQML and its potential to revolutionize traditional machine-learning paradigms by harnessing the power of quantum computing while leveraging the benefits of decentralized networks. This chapter delves into the underlying principles, challenges, and opportunities associated with DQML, offering insights into how this interdisciplinary approach can accelerate advancements in artificial intelligence and data processing. Moreover, this chapter discusses various use cases where DQML demonstrates its superiority over classical machine learning approaches including privacy-preserving data analysis, federated learning, and distributed optimization. Finally, this chapter discusses the future research directions and key considerations for the successful integration and deployment of DQML in real-world applications.
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- 1 Quantum computing: a paradigm shift from conventional computing 1
- 2 An exploration of quantum computing: concept, architecture, and innovative applications 21
- 3 Quantum machine learning in healthcare: diagnostics and drug discovery 39
- 4 Quantum machine learning in finance 65
- 5 Crucial role of blockchain in quantum computing: enhancing security and trust 79
- 6 Algorithmic exploration of unveiling fault tolerance in quantum machine learning 103
- 7 Quantum machine learning in renewable energy systems 131
- 8 Decentralized quantum machine learning: distributed quantum computing for enhanced learning 149
- 9 Quantum reinforcement learning: decision-making in quantum environments 171
- 10 Quantum machine learning in natural language processing: opportunities and challenges 199
- 11 Unveiling intelligence: exploring variational quantum circuits as machine learning models 217
- 12 Methods and tools to improve quantum software quality: a survey 245
- 13 Quantum-enhanced neural networks: bridging the quantum algorithm and machine learning 273
- 14 Future trends and research horizons in quantum machine learning 293
- Biographies 321
- Index 323
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- 1 Quantum computing: a paradigm shift from conventional computing 1
- 2 An exploration of quantum computing: concept, architecture, and innovative applications 21
- 3 Quantum machine learning in healthcare: diagnostics and drug discovery 39
- 4 Quantum machine learning in finance 65
- 5 Crucial role of blockchain in quantum computing: enhancing security and trust 79
- 6 Algorithmic exploration of unveiling fault tolerance in quantum machine learning 103
- 7 Quantum machine learning in renewable energy systems 131
- 8 Decentralized quantum machine learning: distributed quantum computing for enhanced learning 149
- 9 Quantum reinforcement learning: decision-making in quantum environments 171
- 10 Quantum machine learning in natural language processing: opportunities and challenges 199
- 11 Unveiling intelligence: exploring variational quantum circuits as machine learning models 217
- 12 Methods and tools to improve quantum software quality: a survey 245
- 13 Quantum-enhanced neural networks: bridging the quantum algorithm and machine learning 273
- 14 Future trends and research horizons in quantum machine learning 293
- Biographies 321
- Index 323
