Overview
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Dive into a friendly 52-minute introduction to quantum computing and machine learning, requiring no prior knowledge. Explore the intersection of quantum and classical machine learning, probability concepts, and the fundamentals of qubits. Discover quantum measurement techniques, learn about qubits as generative models, and understand the importance of measuring with different bases. Delve into quantum gates, quantum entanglement, and entanglement gates. Examine the principles of quantum machine learning, including training models and loss functions like KL divergence. Conclude with insights into practical applications through labs and code examples. This comprehensive video, part of a collaboration with Zapata Computing, provides a solid foundation for understanding the emerging field of quantum machine learning.
Syllabus
Introduction:
Probability:
The qubit:
Quantum measurement:
Qubits as generative models:
Measuring with different bases:
Quantum gates:
Quantum entanglement:
Entanglement gates:
Quantum machine learning
Training models:
Loss functions and KL divergence:
Labs, code, etc:
Taught by
Serrano.Academy