47-779 / 47-785 course syllabus - Fall 2021

Lectures: Slides

• Lecture 0: Course Overview.
  Slides

• Lecture 1: Review of Linear Algebra I.
  Slides

• Lecture 2: Review of Linear Algebra II.
  Slides

• Lecture 3: Mathematical Programming.
  Slides

• Lecture 4: Introduction to Machine Learning.
  Slides

• Lecture 5: Ising and QUBO.
  Slides

• Lecture 6: Deep Learning.
  Slides

• Lecture 7: Convolutional Neural Networks.
  Slides

• Lecture 8: Graver Augmented Multiseed Algorithm (GAMA).
  Slides

• Lecture 9: Axioms of Quantum Mechanics.
  Slides

• Lecture 10: Introduction to Quantum Gates and Circuits.
  Slides

• Lecture 11: First Look at Quantum Algorithms Deutsch’s Problem.
  Slides

• Lecture 12: Quantum Approximate Optimization Algorithm.
  Slides

• Lecture 13: Midterm Presentations

• Lecture 14: Quantum Annealing, Quantum-inspired Heuristics, Benchmarking, and Parameter setting.
  Slides

• Lecture 15: Quantum Fourier Transform.
  Slides

• Lecture 16: Quantum Phase Estimation and Linear Algebraic Systems.
  Slides

• Lecture 17: Novel Approaches to Solving Ising Problems.
  Slides

• Lecture 18: Guest Speaker: Prof. Prabha Mandayam. Quantum Error Correction. Part 1, Part 2, Part 3.

• Lecture X: Subscribing to AWS Braket, DWave, IBM Qiskit and USRA RIACS.
  Slides