14 Future trends and research horizons in quantum machine learning
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Gowda V. Dankan
Abstract
The convergence of quantum mechanics and advanced analytics gives rise to the burgeoning discipline of quantum machine learning (QML). This intersection heralds a transformative leap in computational capabilities, potentially revolutionizing data analysis and enhancing the sophistication of predictive models. The evolution of QML is analyzed, highlighting its potential to overhaul the entrenched paradigms of data mining and evaluation. On the brink of quantum technological innovations, machine learning integration sets the stage for unparalleled computational speed and the development of novel algorithmic strategies. This chapter focuses on the pivotal characteristics of QML, accentuating the role of recent quantum technological advances in propelling the field. It covers the range of quantum-based algorithms in development, tailored for machine learning applications. These algorithms are posited as solutions to bypass the limitations of classical computing methods. The narrative provides an indepth examination of the amalgamation of quantum principles with neural networks, evaluating the pursuit of the “quantum advantage” for tangible applications. The complexities and challenges that QML faces are also tackled, such as the constraints of noisy intermediate-scale quantum (NISQ) technologies and the imperative need for advanced quantum error correction methods.
Abstract
The convergence of quantum mechanics and advanced analytics gives rise to the burgeoning discipline of quantum machine learning (QML). This intersection heralds a transformative leap in computational capabilities, potentially revolutionizing data analysis and enhancing the sophistication of predictive models. The evolution of QML is analyzed, highlighting its potential to overhaul the entrenched paradigms of data mining and evaluation. On the brink of quantum technological innovations, machine learning integration sets the stage for unparalleled computational speed and the development of novel algorithmic strategies. This chapter focuses on the pivotal characteristics of QML, accentuating the role of recent quantum technological advances in propelling the field. It covers the range of quantum-based algorithms in development, tailored for machine learning applications. These algorithms are posited as solutions to bypass the limitations of classical computing methods. The narrative provides an indepth examination of the amalgamation of quantum principles with neural networks, evaluating the pursuit of the “quantum advantage” for tangible applications. The complexities and challenges that QML faces are also tackled, such as the constraints of noisy intermediate-scale quantum (NISQ) technologies and the imperative need for advanced quantum error correction methods.
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
