MIT 6.5940 L22–L23 Course Summary and Quantum Machine Learning: Pruning, NAS, and On-Device Training on Quantum Circuits
The last two lectures of MIT 6.5940 Fall 2024 come in two halves. The first half of Lecture 22 is a 13-page Course-Summary.pdf 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. The second half, Quantum ML Part I, 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.