Motivation IBM recently published a paper that carried out the Trotterized time evolution of a transverse-field Ising model Hamiltonian \(H = -J \sum_{<i,j>}Z_{i}Z_{j} + h \sum_{i}X_{i}\) on 127 qubits. The quantum circuit used for time evolution consisted of "up to 60 layers of two-qubit gates, a total of 2880 CNOT gates" [1]. This is a significant experiment due to the relatively low quality of CNOT gates across all currently available quantum computing platforms.

Motivation In my ongoing project on ZX Calculus, I found myself needing an elegant and efficient way to handle objects of specific types and perform operations on their values. Enter Algebraic Data Types (ADTs) - a powerful concept from functional programming. ADTs, coupled with pattern matching, provide a solution that is not only efficient but also expressive, allowing for cleaner, more readable code when dealing with complex data structures.

Purpose In this blog, I have collected some tutorials that I found useful while learning Julia. I will also provide a tool-chain that I like to use while coding in Julia. I hope this will speed up your process of learning Julia. Operating System This is not intended to be a cynical view or criticism against Windows users. However, it’s worth considering the benefits of using Linux or macOS for your programming needs, especially when working with Julia.