4 Quantum machine learning in finance
-
Kanu Priya Baheti
Abstract
Quantum computing has the potential to revolutionize many industries, including banking. It is expected that by the end of this decade, quantum computers will have surpassed regular computers in processing capabilities, which will have far-reaching consequences for many sectors, including the financial sector. In particular, it is predicted that the financial industry will gain the most in the short, medium, and long term from using quantum computing. The authors of this chapter discuss the potential benefits of combining machine learning with quantum computing for use in financial modeling. Combining quantum computing and machine learning, or quantum machine learning (QML), provides novel approaches to solving difficult economic problems that classical computers are ill-equipped to tackle. The benefits of employing QML for risk management, credit rating, portfolio optimization, option pricing, and uncovering arbitrage opportunities are addressed, as well as an introduction to the principles of quantum computing. It also describes the quantum hardware and software and the potential challenges and ethical problems associated with QML’s use in the financial sector.
Abstract
Quantum computing has the potential to revolutionize many industries, including banking. It is expected that by the end of this decade, quantum computers will have surpassed regular computers in processing capabilities, which will have far-reaching consequences for many sectors, including the financial sector. In particular, it is predicted that the financial industry will gain the most in the short, medium, and long term from using quantum computing. The authors of this chapter discuss the potential benefits of combining machine learning with quantum computing for use in financial modeling. Combining quantum computing and machine learning, or quantum machine learning (QML), provides novel approaches to solving difficult economic problems that classical computers are ill-equipped to tackle. The benefits of employing QML for risk management, credit rating, portfolio optimization, option pricing, and uncovering arbitrage opportunities are addressed, as well as an introduction to the principles of quantum computing. It also describes the quantum hardware and software and the potential challenges and ethical problems associated with QML’s use in the financial sector.
Kapitel in diesem Buch
- 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
Kapitel in diesem Buch
- 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
