This paper presents an overview of the two posters accepted for the EduPar’25 poster session. The poster session serves as a valuable opportunity for community interaction, encouraging discussions on innovative approaches and emerging ideas.High-Performance Computing (HPC) and Quantum Computing (QC) are both critical for advancing computational capabilities, each complementing the other in solving complex problems. HPC provides the classical infrastructure necessary for simulating and optimizing quantum algorithms, enabling researchers to test and refine quantum models before deploying them on quantum hardware. The integration of HPC and QC paves the way for breakthroughs in scientific discovery, artificial intelligence, and next-generation computing technologies.In the first poster, the authors propose a workshop aimed at introducing secondary school students to the fundamentals of Quantum Computing (QC) and High-Performance Computing (HPC) combining a theoretical presentation with an escape room hands-on experience. The second poster explores how postgraduate students learn HPC skills through a qualitative approach, aligning key themes with learning theories and proposing interventions into undergraduate modules.
In the last two chapters, we talked about computational complexity up till the early 1970s. Here, we'll add a new ingredient to our already simmering stew – something that was thrown in around the mid-1970s, and that now pervades complexity to such an extent that it's hard to imagine doing anything without it. This new ingredient is randomness . Certainly, if you want to study quantum computing, then you first have to understand randomized computing. I mean, quantum amplitudes only become interesting when they exhibit some behavior that classical probabilities don't : contextuality, interference, entanglement (as opposed to correlation), etc. So we can't even begin to discuss quantum mechanics without first knowing what it is that we're comparing against. Alright, so what is randomness? Well, that’s a profound philosophical question, but I’m a simpleminded person. So, you’ve got some probability p , which is a real number in the unit interval [0, 1]. That’s randomness. But wasn’t it a big achievement when Kolmogorov put probability on an axiomatic basis in the 1930s? Yes, it was! But in this chapter, we’ll only care about probability distributions over finitely many events, so all the subtle questions of integrability, measurability, and so on won’t arise. In my view, probability theory is yet another example where mathematicians immediately go to infinite-dimensional spaces, in order to solve the problem of having a nontrivial problem to solve! And that’s fine – whatever floats your boat. I’m not criticizing that. But in theoretical computer science, we’ve already got our hands full with 2n choices. We need 2ℵ0 choices like we need a hole in the head.