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Machine Learning with Quantum Computers - (Quantum Science and Technology) 2nd Edition by Maria Schuld & Francesco Petruccione (Hardcover)

Machine Learning with Quantum Computers - (Quantum Science and Technology) 2nd Edition by  Maria Schuld & Francesco Petruccione (Hardcover) - 1 of 1
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About this item

Highlights

  • This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data.
  • About the Author: Maria Schuld works as a researcher for the Toronto-based quantum computing start-up Xanadu.
  • 312 Pages
  • Science, Physics
  • Series Name: Quantum Science and Technology

Description



Book Synopsis



This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards.

The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.



From the Back Cover



This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards.

The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.



About the Author



Maria Schuld works as a researcher for the Toronto-based quantum computing start-up Xanadu. She received her Ph.D. from the University of KwaZulu-Natal in 2017, where she began working on the intersection between quantum computing and machine learning in 2013. Besides her numerous contributions to the field, she is a co-developer for the open-source quantum machine learning software framework PennyLane.

Francesco Petruccione received his Ph.D. (1988) and "Habilitation" (1994) from the University of Freiburg, Germany. Since 2004, he has been a professor of Theoretical Physics at the University of KwaZulu-Natal in Durban, South Africa, where in 2007, he was granted a South African Research Chair for Quantum Information Processing and Communication. He is the co-author of "The Theory of Open Quantum Systems" (Oxford University Press, 2002) and has published more than 250 papers in refereed journals. Francesco Petruccione's research focuses on open quantum systems and quantum information processing and communication.


Dimensions (Overall): 9.21 Inches (H) x 6.14 Inches (W) x .75 Inches (D)
Weight: 1.4 Pounds
Suggested Age: 22 Years and Up
Number of Pages: 312
Genre: Science
Sub-Genre: Physics
Series Title: Quantum Science and Technology
Publisher: Springer
Format: Hardcover
Author: Maria Schuld & Francesco Petruccione
Language: English
Street Date: October 18, 2021
TCIN: 84911260
UPC: 9783030830977
Item Number (DPCI): 247-33-6399
Origin: Made in the USA or Imported
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Shipping details

Estimated ship dimensions: 0.75 inches length x 6.14 inches width x 9.21 inches height
Estimated ship weight: 1.4 pounds
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