Noise-Robust Quantum Machine Learning - Lecture 23

Explore noise-robust quantum machine learning in this comprehensive lecture from MIT's TinyML and Efficient Deep Learning Computing course. Delve into advanced techniques for deploying neural networks on resource-constrained devices like mobile phones, IoT devices, and quantum machines. Learn about model compression, pruning, quantization, neural architecture search, and distillation for efficient inference. Discover efficient training methods, including gradient compression and on-device transfer learning. Gain insights into application-specific model optimization for videos, point cloud, and NLP. Get hands-on experience implementing deep learning applications on microcontrollers and quantum machines through an open-ended design project focused on mobile AI. Access lecture slides and additional course information at efficientml.ai.

Lecture 23 - Noise-Robust Quantum Machine Learning | MIT 6.S965