Course Summary and Quantum Machine Learning: Pruning, NAS, and On-Device Training on Quantum Circuits | MIT 6.5940 Lectures 22–23
The last two lectures of MIT 6.5940 Fall 2024 come in two parts. The opening of Lecture 22 is a 13-page Course-Summary.pdf (only a short stretch at the start of the recording) that redraws the course as three blocks (inference, training, application-specific) on System and Algorithm axes, then lays out the 7-item final project rubric. Most of the rest of Lecture 22 is Quantum ML Part I, which has a recording but no slides. Lecture 23 (Hanrui Wang, 99 slides) covers parameterized quantum circuits (PQCs): data encoding, parameter-shift gradients, probabilistic gradient pruning under noise (QOC), the TorchQuantum library, and QuantumNAS, which searches with a SuperCircuit and then prunes gates. It reads like a replay of the course's supernet and magnitude pruning on quantum circuits. Fall 2026 has replaced both lectures with a guest lecture.