9 Quantum reinforcement learning: decision-making in quantum environments
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Ashutosh Pagrotra
Abstract
This chapter unites quantum mechanics with decision-making through an exciting journey through quantum reinforcement learning (QRL). It addresses fundamental RL principles and issues by severing the links between QRL and conventional reinforcement learning. The groundwork for quantum robotics and lights, where quantum organisms explore quantum environments and discover new frontiers, is laid via a quantum mechanics primer. By combining quantum annealing, search methods, and parallelism, QRL provides a novel remedy for the drawbacks of RL. It goes beyond traditional simulations and explores uncharted areas such as quantum chemistry and innovative financial techniques. Through education and remote quantum resources made possible by cloud services, QRL democratizes quantum solutions, increasing access and motivating future generations. It ignites curiosity by providing an amazing environment for learning and multidisciplinary study. Beyond computation, QRL inspires the next generation, advances science, and catalyzes fields.
Abstract
This chapter unites quantum mechanics with decision-making through an exciting journey through quantum reinforcement learning (QRL). It addresses fundamental RL principles and issues by severing the links between QRL and conventional reinforcement learning. The groundwork for quantum robotics and lights, where quantum organisms explore quantum environments and discover new frontiers, is laid via a quantum mechanics primer. By combining quantum annealing, search methods, and parallelism, QRL provides a novel remedy for the drawbacks of RL. It goes beyond traditional simulations and explores uncharted areas such as quantum chemistry and innovative financial techniques. Through education and remote quantum resources made possible by cloud services, QRL democratizes quantum solutions, increasing access and motivating future generations. It ignites curiosity by providing an amazing environment for learning and multidisciplinary study. Beyond computation, QRL inspires the next generation, advances science, and catalyzes fields.
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
