Artem Konotpchyk
I am a Data Scientist and AI Engineer at IBM, where I work on machine learning systems and applied AI in production environments. My day-to-day work focuses on building, deploying, and evaluating ML models, while also exploring emerging computational approaches that challenge classical assumptions.
Alongside classical machine learning, I work with quantum computing and Quantum Machine Learning, with an emphasis on practical experimentation rather than theory alone. I have delivered a hands-on quantum computing workshop at IBM, introducing quantum concepts through runnable code and real examples. Using Qiskit, I design and test hybrid quantum–classical workflows, compare them with classical baselines, and analyze the effects of noise, limited qubit counts, and current hardware constraints.
Session
Quantum Machine Learning (QML) combines quantum computing and classical machine learning, but its practical value is often misunderstood. In this hands-on workshop, we will explore QML using Qiskit, build and run real quantum machine learning models, and compare them with classical approaches. The session focuses on practical intuition, runnable Python code, and a clear discussion of current advantages, limitations, and realistic use cases of QML.