We, the quantum computing folks, have opened up our formerly-internal weekly fun meeting where we "peer-teach" each other the stuff that everybody learned recently for their research. As we use the Julia programming language for numerical simulations and quantum device experiments, we have to learn that thoroughly, too, so the meeting series is be called:
𝑱𝒖𝒍𝒊𝒂 𝒊𝒏 𝑸𝒖𝒂𝒏𝒕𝒖𝒎𝒍𝒂𝒏𝒅
𝐽𝑢𝑙𝑖𝑎 𝑖𝑛 𝑄𝑢𝑎𝑛𝑡𝑢𝑚𝑙𝑎𝑛𝑑 is an informal meeting of students and faculty where we discuss quantum technology, quantum-tech relevant physics and mathematics, and the Julia programming language, preferably all at the same time.
We meet, eat pizza (or some other don't-worry¹–be-quantum food), and listen to the person(s) presenting. As mentioned above, the idea remains that participants explain to each other what they had to learn for their research anyway — so preparing a presentation should be little effort and everybody comes out a little smarter.
¹) About your BMI.
Prerequisites
The idea is to keep requirements to a minimum: FunQ! No further knowledge about either math or quantum physics is required.
This means that not every participant will understand everything in every meeting. In fact, there are meetings where few people understand much, so that those with more math and/or physics background get something to chew other than anchovies.
Credit points
Students can get ECTS points by
- Registering to LTAT.04.004 Quantum Seminar
- Giving at least one presentation, and
- Being present for the other sessions.
Position or momentum? Time or energy
We meet, currently, Fridays, ≈ 17:45-19:00. Contact Handy Kurniawan (firstname . lastname @ut.ee) to be invited and notified.
Why 𝐽𝑢𝑙𝑖𝑎?
Short answer
... because it's the language we use for prototyping our quantum ideas.
Ok, but, why 𝐽𝑢𝑙𝑖𝑎?
Long answer.
In teaching courses, we have found Julia uniquely suited to elegantly and succinctly express math and quantum stuff. The Julia syntax allows to formulate mathematics in a way that actually resembles math, very much unlike Python. Many math concepts — such as defining a new function or obtaining the derivative of a function — are expressed in the way you would expect from math (e.g., ψ(x) = exp(2π𝒊·√x), and ψ').
At the same time, Julia is fast, modern, and open source — unlike MATLAB.
Finally, as the language design has a focus on large-scale scientific computation, Julia is well suited for starting new computation projects where you need to rapidly prototype, but also require fast-running code: No need to prototype in MATLAB/Python and then implement in Fortran or C++; multi-threading and GPU-offloading are piece-of-cake tasks.
Have a look
Take a look at the Julia notebooks in the "History" 📜 tab. (You don't need to install Julia.)