Course by the same professor at St. Petersburg University who taught this course (extremely math heavy, not a great experience for me). This course claims to focus “on how the mathematical model of quantum computing grows out from physics and experiment, while omitting most of the formulas (when possible) and rigorous proofs.”
Begins with math of quantum computing, then math of quantum physics, then a bit of physics
Quantum-enhanced machine learning, focusing on algorithms challenging to classical computers. Implement protocols using open-source tools in Python.
Describe and implement classical-quantum hybrid learning algorithms. Encode classical information in quantum systems. Perform discrete optimization in ensembles and unsupervised machine learning with different quantum computing paradigms. Sample quantum states for probabilistic models. Experiment with unusual kernel functions on quantum computers
Demonstrate coherent quantum machine learning protocols and estimate their resources requirements. Summarize quantum Fourier transformation, quantum phase estimation and quantum matrix, and implement these algorithms. General linear algebra subroutines by quantum algorithms. Gaussian processes on a quantum computer.
First in 3 courses, but this one seems most relevant
de Broglie waves, the wavefunction, and its probability interpretation. We then introduce the Schrodinger equation, inner products, and Hermitian operators. We also study the time-evolution of wave-packets, Ehrenfest’s theorem, and uncertainty relations.
MIT: Mastering Quantum Mechanics EDX
“Completing the 3-part Quantum Mechanics series will give you the necessary foundation to pursue advanced study or research at the graduate level in areas related to quantum mechanics.” Follows MIT’s 8.05.