Quantum Machine Learning 101: An Introduction for Beginners with...
summary
Demos build and convert quantum circuits into tensors using TensorFlow Quantum and Cirq, create a parametrized quantum circuit as a trainable layer, and run model.compile/model.fit with standard Keras workflow (requires Python 3.7–3.9 and TFQ 0.7.2). Claim of exponential speedup is qualified — only when data is natively quantum or duality is exploited — and the shown GPU/simulator demos run slower than classical due to the costly classical→quantum encoding step. Hybrid models (quantum ops for select layers, classical nets for the rest) are presented as the practical path.
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chapters (10)
- 0:00Introduction to Quantum Machine Learning
- 2:27Quantum computing basics
- 7:02Why quantum computing can be exponentially faster
- 13:29QML core concepts
- 16:40Hybrid QML models
- 23:02Cirq and TF Quantum
- 27:45Circuit to tensor demo
- 33:10Quantum data classification
- 39:30QML research status
- 41:38Q&A
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