Harvard Physics 272 / CS 2233
This course covers quantum learning theory, a contemporary subject at the intersection of quantum mechanics, quantum computing, statistical learning theory, and machine learning. The core question of the subject is: how can we use quantum computation to efficiently learn properties of quantum mechanical systems? Answering this question helps us understand the power of quantum computers in assisting experimental physicists with studying quantum materials and quantum chemical systems, while also providing valuable tools for quantum machine learning to develop algorithms based on quantum data. Quantum learning theory has become a core subject in quantum information and computation, and this course is one of the first of its kind to present quantum learning theory in its entirety. We will explore the theory of learning quantum states and quantum dynamics, the theory of quantum memory and quantum replica-learning, random and pseudo-random quantum circuits, and many applications to quantum many-body physics.
Time/Location: MW 3-4:15, Pierce 209
Instructors: Sitan Chen, Jordan Cotler Office hours: Th 10:30-11:30, SEC 3.325; Tu 11-12, Goel 418
Teaching Fellows: Weiyuan Gong, Quynh Nguyen Office hours: M 5-6 pm, Pierce G7A; W 2-3, 52 Oxford St B150 Recitation: Th 4-5pm, Maxwell Dworkin G115
Canvas (for announcements) Ed discussion forum
Course Policies: See syllabus for detailed overview.
| Topic | Link | Readings | |
|---|---|---|---|
| Lecture 1 (9/3) | Vignette: Learning an Unknown Rotation | - HKOT23: bootstrapping for learning arbitrary unitaries down to Heisenberg limit - GLM11: review paper on quantum metrology |
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| Lecture 2 (9/8) | Classical probability and tensors | ||
| Lecture 3 (9/10) | Quantum mechanics basics I | ||
| Lecture 4 (9/15) | Quantum mechanics basics II |