Beyond the Hype: Hybrid Quantum-Classical AI Agents for Scientific Discovery
summary
Hybrid quantum-classical pipelines are benchmarked on concrete PoCs: image defect detection with a synthetically expanded dataset of 8,500 images (4,700 originals) where classical CNNs currently outperform QCNNs, QGANs stabilize earlier and produce smoother indentation defects, and a pulmonary set of 805 chest X‑rays is used for classification experiments. Variational quantum circuits (VQC) beat classical baselines on one engine-fault dataset but lose on others; a KUKA robot RL demo runs but quantum RL shows 46.5% slower training on CPU/GPU emulation while offering better stability.
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chapters (12)
- 0:00Hybrid quantum-classical AI intro
- 2:56Quantum layers enhance classical AI
- 5:43Quantum ML pipeline overview
- 7:30Quantum CNNs for defect detection
- 9:37Quantum GANs for generative tasks
- 11:24Variational quantum circuits
- 13:31Quantum neural networks for tabular data
- 15:41Quantum autoencoders
- 16:50Student presentations on Q GANs & Q RL
- 19:04Quantum GANs for manufacturing defects
- 20:47Quantum RL for robotic path planning
- 24:02Future of hybrid quantum-classical AI
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