Quantum Machine Learning for Drug Discovery with Python and Qiskit
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Free Shipping in India on orders above Rs. 500
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: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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