6 Algorithmic exploration of unveiling fault tolerance in quantum machine learning
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Neha Bhati
Abstract
Rapid progress in quantum computing and machine learning has unlocked new possibilities for addressing complex issues. However, fault tolerance difficulties frequently impede the practical implementation of quantum ML (QML) algorithms. Algorithmic exploration of unveiling fault tolerance in QML aims to find and create fault-tolerant algorithms specifically suited to QML use cases. The techniques are designed to reduce the impact of quantum errors, making QML models more stable and trustworthy. An increase in fault tolerance has been observed in preliminary tests, with no adverse effects on QML’s computing performance or accuracy. This work not only adds to the understanding of fault-tolerant quantum algorithms but also lays the way for the widespread use of QML in areas as varied as finance, medicine, and security.
Abstract
Rapid progress in quantum computing and machine learning has unlocked new possibilities for addressing complex issues. However, fault tolerance difficulties frequently impede the practical implementation of quantum ML (QML) algorithms. Algorithmic exploration of unveiling fault tolerance in QML aims to find and create fault-tolerant algorithms specifically suited to QML use cases. The techniques are designed to reduce the impact of quantum errors, making QML models more stable and trustworthy. An increase in fault tolerance has been observed in preliminary tests, with no adverse effects on QML’s computing performance or accuracy. This work not only adds to the understanding of fault-tolerant quantum algorithms but also lays the way for the widespread use of QML in areas as varied as finance, medicine, and security.
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
