3 Quantum machine learning in healthcare: diagnostics and drug discovery
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Dankan Gowda V
Abstract
The awareness of quantum machine learning (QML) as a possible catalyst for redefining the healthcare industry is fast increasing. This chapter provides an analysis of the transformational potential of QML in the context of healthcare diagnostics and drug development. The confluence of quantum computing with machine learning in the field of diagnostics offers significant prospects for the analysis of extensive and intricate biological datasets at unparalleled velocities. This has the potential to improve the precision and promptness of illness diagnosis and monitoring. This chapter explores the ways in which quantum algorithms might augment imaging methodologies, hence facilitating more accurate and noninvasive diagnostic procedures. QML presents a transformative change within the domain of drug discovery. The considerable computing capabilities of quantum systems have the potential to greatly expedite the process of drug creation and screening. These systems can efficiently navigate extensive chemical databases in a substantially shorter timeframe compared to conventional systems. Through the examination of intricate molecular structures and their interactions on a quantum scale, novel therapeutic interventions might be discerned for illnesses that now lack efficacious remedies. This chapter examines the promising prospects of QML in the medical field while also highlighting the current challenges and limitations it faces upon integration. Key points of consideration include the vulnerability of quantum computing devices to environmental interference and the nascent stage of quantum algorithms tailored for healthcare data. The merging of quantum computing with the healthcare industry is poised to have profound effects on patient treatment, medical investigations, and the drug development industry. The content herein provides a comprehensive review of the current state, potential benefits, and challenges of employing QML within healthcare diagnostics and pharmaceutical innovation. Moreover, it charts an optimistic trajectory for the evolution of medical research going forward.
Abstract
The awareness of quantum machine learning (QML) as a possible catalyst for redefining the healthcare industry is fast increasing. This chapter provides an analysis of the transformational potential of QML in the context of healthcare diagnostics and drug development. The confluence of quantum computing with machine learning in the field of diagnostics offers significant prospects for the analysis of extensive and intricate biological datasets at unparalleled velocities. This has the potential to improve the precision and promptness of illness diagnosis and monitoring. This chapter explores the ways in which quantum algorithms might augment imaging methodologies, hence facilitating more accurate and noninvasive diagnostic procedures. QML presents a transformative change within the domain of drug discovery. The considerable computing capabilities of quantum systems have the potential to greatly expedite the process of drug creation and screening. These systems can efficiently navigate extensive chemical databases in a substantially shorter timeframe compared to conventional systems. Through the examination of intricate molecular structures and their interactions on a quantum scale, novel therapeutic interventions might be discerned for illnesses that now lack efficacious remedies. This chapter examines the promising prospects of QML in the medical field while also highlighting the current challenges and limitations it faces upon integration. Key points of consideration include the vulnerability of quantum computing devices to environmental interference and the nascent stage of quantum algorithms tailored for healthcare data. The merging of quantum computing with the healthcare industry is poised to have profound effects on patient treatment, medical investigations, and the drug development industry. The content herein provides a comprehensive review of the current state, potential benefits, and challenges of employing QML within healthcare diagnostics and pharmaceutical innovation. Moreover, it charts an optimistic trajectory for the evolution of medical research going forward.
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
