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Synopsis
Reactive Publishing
Quantum Machine Learning for Drug Discovery with Python and Qiskit/PennyLane
Quantum machine learning is emerging as one of the most promising frontiers at the intersection of quantum computing, artificial intelligence, and pharmaceutical research. This book provides a practical, hands-on guide to applying quantum machine learning techniques to drug discovery using Python and industry-standard quantum frameworks.
Readers will learn how to build and implement quantum-enhanced models for molecular property prediction, molecular generation, and virtual screening. The book bridges classical machine learning concepts with quantum algorithms, demonstrating how tools like Qiskit and PennyLane can be used to encode molecular data into quantum circuits, train quantum kernels, and explore variational quantum algorithms for chemistry-related tasks.
What’s Inside:- Fundamentals of quantum computing tailored for drug discovery applications
- Integration of quantum machine learning with classical cheminformatics pipelines
- Practical implementation of quantum support vector machines, quantum neural networks, and hybrid quantum-classical models
- Real-world examples using Qiskit and PennyLane for molecular datasets
- Code walkthroughs for simulating quantum circuits and running experiments on both simulators and quantum hardware
- Best practices for data encoding, feature mapping, and model evaluation in a quantum context
Written for Python-proficient researchers, data scientists, and computational chemists, this book assumes basic familiarity with machine learning and Python but requires no prior quantum computing experience. All concepts are introduced progressively with clear explanations and working code examples.
Whether you are exploring quantum computing for the first time or seeking to apply it specifically to pharmaceutical research, this book offers a focused, implementation-oriented approach to one of the most exciting application areas in quantum technology.
Ideal for: Computational biologists, quantum computing enthusiasts, AI researchers in life sciences, and pharmaceutical data scientists interested in next-generation methods for accelerating drug discovery.
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Quantum Machine Learning for Drug Discovery with Python and Qiskit
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Paperback. Condition: new. Paperback. Reactive PublishingQuantum Machine Learning for Drug Discovery with Python and Qiskit/PennyLaneQuantum machine learning is emerging as one of the most promising frontiers at the intersection of quantum computing, artificial intelligence, and pharmaceutical research. This book provides a practical, hands-on guide to applying quantum machine learning techniques to drug discovery using Python and industry-standard quantum frameworks.Readers will learn how to build and implement quantum-enhanced models for molecular property prediction, molecular generation, and virtual screening. The book bridges classical machine learning concepts with quantum algorithms, demonstrating how tools like Qiskit and PennyLane can be used to encode molecular data into quantum circuits, train quantum kernels, and explore variational quantum algorithms for chemistry-related tasks.What's Inside: Fundamentals of quantum computing tailored for drug discovery applicationsIntegration of quantum machine learning with classical cheminformatics pipelinesPractical implementation of quantum support vector machines, quantum neural networks, and hybrid quantum-classical modelsReal-world examples using Qiskit and PennyLane for molecular datasetsCode walkthroughs for simulating quantum circuits and running experiments on both simulators and quantum hardwareBest practices for data encoding, feature mapping, and model evaluation in a quantum contextWritten for Python-proficient researchers, data scientists, and computational chemists, this book assumes basic familiarity with machine learning and Python but requires no prior quantum computing experience. All concepts are introduced progressively with clear explanations and working code examples.Whether you are exploring quantum computing for the first time or seeking to apply it specifically to pharmaceutical research, this book offers a focused, implementation-oriented approach to one of the most exciting application areas in quantum technology.Ideal for: Computational biologists, quantum computing enthusiasts, AI researchers in life sciences, and pharmaceutical data scientists interested in next-generation methods for accelerating drug discovery. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798199496131
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Quantum Machine Learning for Drug Discovery with Python and Qiskit
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Taschenbuch. Condition: Neu. Neuware - Reactive PublishingQuantum Machine Learning for Drug Discovery with Python and Qiskit/PennyLaneQuantum machine learning is emerging as one of the most promising frontiers at the intersection of quantum computing, artificial intelligence, and pharmaceutical research. This book provides a practical, hands-on guide to applying quantum machine learning techniques to drug discovery using Python and industry-standard quantum frameworks.Readers will learn how to build and implement quantum-enhanced models for molecular property prediction, molecular generation, and virtual screening. The book bridges classical machine learning concepts with quantum algorithms, demonstrating how tools like Qiskit and PennyLane can be used to encode molecular data into quantum circuits, train quantum kernels, and explore variational quantum algorithms for chemistry-related tasks.What's Inside: - Fundamentals of quantum computing tailored for drug discovery applications- Integration of quantum machine learning with classical cheminformatics pipelines- Practical implementation of quantum support vector machines, quantum neural networks, and hybrid quantum-classical models- Real-world examples using Qiskit and PennyLane for molecular datasets- Code walkthroughs for simulating quantum circuits and running experiments on both simulators and quantum hardware- Best practices for data encoding, feature mapping, and model evaluation in a quantum contextWritten for Python-proficient researchers, data scientists, and computational chemists, this book assumes basic familiarity with machine learning and Python but requires no prior quantum computing experience. All concepts are introduced progressively with clear explanations and working code examples.Whether you are exploring quantum computing for the first time or seeking to apply it specifically to pharmaceutical research, this book offers a focused, implementation-oriented approach to one of the most exciting application areas in quantum technology.Ideal for: Computational biologists, quantum computing enthusiasts, AI researchers in life sciences, and pharmaceutical data scientists interested in next-generation methods for accelerating drug discovery. Seller Inventory # 9798199496131
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