Quantum Machine Learning With TensorFlow Quantum
summary
Overview of practical setup and claims: current devices sit around 50–72 qubits (Google 53 cited) with a roadmap claiming 1000 qubits by 2023, and frameworks stack TensorFlow + Cirq + Pennylane to run variational models. Code creates parameterized circuits in a few lines, supports TensorFlow Quantum layers, differentiability, circuit batching and backend‑agnostic execution, and trains hybrid quantum-classical models where circuit measurements feed a classical NN. Targets include chemistry simulation, quantum communication and error‑mitigation pipelines.
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chapters (7)
- 0:00Introduction to quantum
- 4:10Quantum computing basics
- 7:07Qubits and quantum gates
- 12:05Hardware and algorithms
- 15:10TensorFlow Quantum framework
- 20:16Workflow and circuit design
- 24:37Hybrid models and conclusion
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