11 Unveiling intelligence: exploring variational quantum circuits as machine learning models
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Hardik Dhiman
Abstract
Quantum computing and machine learning (ML) are merging in novel ways, thanks to variational quantum circuits (VQCs). This chapter explores VQCs, fundamentals, various applications, and notable outcomes. VQCs show how quantum computing and ML will address complicated problems in the future. Encoding difficult issues into quantum states and optimizing them to find solutions rapidly is the foundation of VQCs. This parallelism-enhanced quantum technique could improve ML applications including natural language processing, drug discovery, and molecular simulations. VQCs easily integrate classical optimization methods with quantum circuits and use a hybrid classical-quantum methodology. Combining the best classical and quantum computing yields more precise and efficient computations. The chapter examines VQC’s properties and prospective uses in several domains. VQCs can change several industries by modeling molecular structures, predicting chemical reactions, improving recommendation systems, and optimizing financial portfolios. VQCs give more individuals access to quantum computing resources through cloud-based platforms, fostering global creativity and cooperation. VQCs lead quantum ML (QML) as quantum technology advances, enabling previously unthinkable breakthroughs. VQCs unlock the quantum edge in ML and solve real-world challenges in novel ways. VQCs’ potential to change technology and research as we embark on this quantum-infused voyage is infinite, offering faster scientific discoveries, better processing capability, and unequalled problem-solving.
Abstract
Quantum computing and machine learning (ML) are merging in novel ways, thanks to variational quantum circuits (VQCs). This chapter explores VQCs, fundamentals, various applications, and notable outcomes. VQCs show how quantum computing and ML will address complicated problems in the future. Encoding difficult issues into quantum states and optimizing them to find solutions rapidly is the foundation of VQCs. This parallelism-enhanced quantum technique could improve ML applications including natural language processing, drug discovery, and molecular simulations. VQCs easily integrate classical optimization methods with quantum circuits and use a hybrid classical-quantum methodology. Combining the best classical and quantum computing yields more precise and efficient computations. The chapter examines VQC’s properties and prospective uses in several domains. VQCs can change several industries by modeling molecular structures, predicting chemical reactions, improving recommendation systems, and optimizing financial portfolios. VQCs give more individuals access to quantum computing resources through cloud-based platforms, fostering global creativity and cooperation. VQCs lead quantum ML (QML) as quantum technology advances, enabling previously unthinkable breakthroughs. VQCs unlock the quantum edge in ML and solve real-world challenges in novel ways. VQCs’ potential to change technology and research as we embark on this quantum-infused voyage is infinite, offering faster scientific discoveries, better processing capability, and unequalled problem-solving.
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
