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Item #:
243033136
Machine Learning with Quantum Computers (Quantum Science and Technology)
Item #:
243033136
HTG 17665
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This book offers an introduction into quantum machine learning research, covering approaches that range from near-term to fault-tolerant quantum machine learning algorithms.
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What Stands Out
Quantum Advantage
Leverages quantum computing's power for enhanced machine learning capabilities, outperforming classical algorithms in speed and efficiency.
Interdisciplinary Approach
Combines principles of quantum science and traditional machine learning, fostering a deeper understanding for researchers and practitioners in both fields.
Real-World Applications
Addresses complex problem-solving in diverse sectors such as finance and healthcare, showcasing practical uses of quantum-enhanced machine learning.
Product Details
| Publisher | Springer |
| Publication date | October 18, 2021 |
| Edition | Second Edition 2021 |
| Language | English |
| Print length | 326 pages |
| ISBN-10 | 3030830977 |
| ISBN-13 | 978-3030830977 |
| Item Weight | 1.4 pounds (640 grams) |
| Dimensions | 6.5 x 1 x 9.5 inches (16.5 x 2.5 x 24.1 cm) |
| Part of series | Quantum Science and Technology |
Product Description
Machine Learning with Quantum Computers (Quantum Science and Technology)
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Who is the target audience for this book?
Answer: The book is aimed at graduate-level computer scientists and physicists. -
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What topics are covered in the book?
Answer: Topics include quantum circuits, hybrid optimization, data encoding, and quantum neural networks. -
Question:
How does the second edition differ from the first?
Answer: The second edition extends the material beyond supervised learning and focuses on recent developments in near-term quantum machine learning.
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Features & Benefits
- Introductory resource for quantum machine learning research.
- Explores both near-term and fault-tolerant quantum algorithms.
- Covers theoretical and practical techniques for understanding quantum data learning.
- Includes topics like parameterized quantum circuits and quantum neural networks.
- Updated second edition expands beyond supervised learning.
- Targeted towards graduate-level computer scientists and physicists.
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