Quantum Machine Learning with Qiskit
Speaker
Jonas Adomaitis
Jonas Adomaitis is a Senior Data Scientist and People Manager at IBM Lithuania. He holds a Bachelor of Science in Computer Science and Mathematics from The University of Edinburgh. Jonas is a highly active member of the global quantum community, serving as a member of the Qiskit Advocate program and the Co-lead of IBM’s Quantum Club.
His technical expertise encompasses Agentic AI, NLP, and Explainable AI (XAI), with a focus on delivering advanced analytics for the financial and government sectors. Jonas’s contributions have been recognized with several distinctions, including the 2025 Quantum Excellence designation from the Qiskit Global Summer School and the IBM Quantum Challenge 2024 Achievement.
Abstract
Unlock the power of Quantum Machine Learning (QML) with Python and Qiskit. You will explore the distinctions between classical and quantum machine learning and gain an understanding of data encoding, quantum kernels, and the training process of a Variational Quantum Classifier. You’ll also discover how Qiskit Functions integrate quantum into application workflows to solve complex challenges. Finally, you’ll explore IBM case studies showing the transition to practical "quantum utility."
Description
The presentation covers four critical pillars:
Classical vs. Quantum Paradigms: Attendees will explore the fundamental distinctions between classical and quantum machine learning. This includes understanding how quantum states allow for information processing that differs significantly from traditional classical methods.
Qiskit and Technical Foundations of QML: The session provides a technical overview of the quantum pipeline. Participants will learn how to perform quantum data encoding to map classical information into quantum states, define the role of quantum kernels, and understand the methodology behind training a Variational Quantum Classifier (VQC).
Qiskit Functions: The session explains what are Qiskit Functions and explains how to integrate quantum capabilities directly into existing application workflows, managing problem-specific classical inputs and outputs to address complex computational challenges.
IBM Quantum Case Studies: The session concludes by discussing several high-impact case studies from IBM Quantum. These collaborative examples showcase how organizations are currently transitioning from experimental research toward practical "quantum utility" to solve industry-specific problems.