10 Quantum machine learning in natural language processing: opportunities and challenges
-
Umesh Kumar Lilhore
Abstract
Quantum natural language processing (QNLP) refers to the many approaches developed within the discipline as a result of the convergence of natural language processing (NLP) and quantum computing in recent years. This multidisciplinary field involves applying quantum mechanical insights to fundamental aspects of language processing across a wide range of NLP activities. Existing methods span a broad spectrum from theoretical demonstrations of the quantum advantage to actual implementations of algorithms using quantum hardware. This work aims to do just that by bringing together, in one place, the many different methods that have been applied to the problem. Theoretical studies and practices that have been implemented on classical or quantum hardware are separated here. The categorization also takes into account the goal of each strategy. Syntax-semantic representation is an example of a general-purpose task. In this chapter, the benefits of QNLP in terms of efficiency and methodology are dissected. In addition, it explores how QNLP techniques might be utilized in place of stateof- the-art deep learning-based methodologies.
Abstract
Quantum natural language processing (QNLP) refers to the many approaches developed within the discipline as a result of the convergence of natural language processing (NLP) and quantum computing in recent years. This multidisciplinary field involves applying quantum mechanical insights to fundamental aspects of language processing across a wide range of NLP activities. Existing methods span a broad spectrum from theoretical demonstrations of the quantum advantage to actual implementations of algorithms using quantum hardware. This work aims to do just that by bringing together, in one place, the many different methods that have been applied to the problem. Theoretical studies and practices that have been implemented on classical or quantum hardware are separated here. The categorization also takes into account the goal of each strategy. Syntax-semantic representation is an example of a general-purpose task. In this chapter, the benefits of QNLP in terms of efficiency and methodology are dissected. In addition, it explores how QNLP techniques might be utilized in place of stateof- the-art deep learning-based methodologies.
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
