Quantum Machine Learning with Qiskit
Speakers
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.
Manta Ribkauskyte
Abstract
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.
Description
This workshop is a practical introduction to Quantum Machine Learning with Qiskit, aimed at Python developers, data scientists, and machine learning practitioners who want to understand quantum ML beyond the hype.
Participants will learn the core ideas behind quantum computing and how they are applied to machine learning, including qubits, quantum circuits, data encoding, and hybrid quantum–classical models. The workshop emphasizes what can be done today using simulators and real quantum hardware, and where classical machine learning still performs better.
During the session, attendees will work with Qiskit to:
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Build quantum circuits in Python
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Encode classical data into quantum states
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Implement a simple Quantum Machine Learning model (e.g. a variational classifier)
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Run experiments on simulators
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Compare results with a classical baseline
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Analyze the impact of noise, limited qubit counts, and scalability constraints
No prior experience with quantum computing is required. Basic Python knowledge and familiarity with classical machine learning concepts (such as classification and optimization) are sufficient.
By the end of the workshop, participants will:
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Understand the fundamental principles of Quantum Machine Learning
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Be able to write and run basic Qiskit-based QML code
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Have a realistic view of the strengths and limitations of current quantum ML approaches
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Leave with practical examples and resources to continue exploring the field