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QML Unlocked: From Curiosity to Capability in Quantum Machine Learning

By:

Montero Javier Mancilia

Material type: Text

TextPublication details: USA Montero Javier Mancilia 2025Description: 228pISBN:

9798312536867

Other title:

Quantum Machine Learning Unlocked

Subject(s):

Quantum Machine Learning

DDC classification:

006.31 MAC/Q

Contents:

Chapter 1: Quantum Computing and Machine Learning Chapter 2: Do Quantum Computers Really Exist? Chapter 3: It’s Not Trivial to Pick a QPU Chapter 4: Implementing Quantum Machine Learning Models with Python Chapter 5: The Relevance of the Preprocessing Phase Chapter 6: From Classical Data to Quantum States Chapter 7: Support Vector Classifiers Chapter 8: Variational Quantum Classifiers Chapter 9: Promising Approaches Chapter 10: A Journey to Implement Yourself

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Holdings
Item type	Current library	Call number	Copy number	Status	Barcode
Reference	IIIT Kottayam Central Library Reference	006.31 MON/Q (Browse shelf(Opens below))		Not For Loan	2823
Books	IIIT Kottayam Central Library General Stacks	006.31 MON/Q (Browse shelf(Opens below))	1	Available	2824
Books	IIIT Kottayam Central Library General Stacks	006.31 MON/Q (Browse shelf(Opens below))	2	Available	2825

Total holds: 0

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Previous				No cover image available				Next
Previous	006.31 LAZ/M Machine Learning for Time Series Forecasting With Python	006.31 MAN/F Foundations of statistical natural language processing	006.31 MIT/M Machine Learning	006.31 MON/Q QML Unlocked: From Curiosity to Capability in Quantum Machine Learning	006.31 MUN/A Accelerators for Convolutional Neural Networks	006.31 MUR/P Probabilistic Machine Learning : An Introduction	006.31 POW/R Reinforcement Learning and Stochastic Optimization : A Unified Framework for Sequential Decisions	Next

Chapter 1: Quantum Computing and Machine Learning
Chapter 2: Do Quantum Computers Really Exist?
Chapter 3: It’s Not Trivial to Pick a QPU
Chapter 4: Implementing Quantum Machine Learning Models with Python
Chapter 5: The Relevance of the Preprocessing Phase
Chapter 6: From Classical Data to Quantum States
Chapter 7: Support Vector Classifiers
Chapter 8: Variational Quantum Classifiers
Chapter 9: Promising Approaches
Chapter 10: A Journey to Implement Yourself

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