Quantum Computing Math

Five hours of background, every idea checked in NumPy.

Three sessions covering the mathematics a first quantum computing course takes for granted: complex numbers, kets and bras, tensor products, unitary and Hermitian matrices, eigenvalues, matrix exponentials and the Fourier transform.

You will not be asked to recite formulas, so this course trains the other skill: knowing what each idea does, why quantum computing needs it, and what it looks like in NumPy. Recognize, don't recite — for every formula, know what it does and when it applies, and let the code carry the details.

Two habits make the difference. Before each code cell, write down what you expect it to print; the surprises are where the learning is. And at the end of each part, answer the prompts in a sentence or two before you open the answers at the foot of the page.

The third lesson is the short one, an hour rather than two. It is where phase and gates finally meet, and the demonstration at the end is the smallest thing on the site that could not happen in classical computing.

Who it's for

Anyone starting a quantum computing course who wants the background in place first. You need Python, enough NumPy to index an array, and no physics. Written alongside CSCI 5730, whose lectures are shared with CSCI 4140.