Event Series: SENSS Specialist Training Series – Machine Learning and Data Science

Quantum Machine Learning

Name: Quantum Machine Learning
Start: 2025-11-07T09:30:00+00:00
End: 2025-11-07T12:30:00+00:00
Location: Zoom

November 7 @ 9:30 am – 12:30 pm

This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS).

SENSS Specialist Training: Quantum Machine Learning
Instructor: Dr Jan Novotny (Centre for Econometric Analysis, Bayes Business School and Nomura International)
Term: Autumn 2025

Module Outline and Aims

Recent breakthroughs in quantum computing have ushered in a transformative era in computational science, redefining the boundaries of what is algorithmically possible. This course explores the intersection of quantum mechanics and machine learning, offering participants a gateway into a radically new paradigm where quantum principles enable novel approaches to data processing, optimization, and pattern recognition. As quantum hardware continues to evolve, understanding its theoretical foundations becomes essential for researchers poised to shape the future of intelligent systems.

This course introduces the principles of quantum computing, including qubits, entanglement, superposition, and quantum gates. Through a blend of theoretical lectures and hands-on exercises, participants will gain fluency in quantum algorithms and their applications to machine learning tasks. The curriculum bridges classical and quantum machine learning frameworks, highlighting both the conceptual shifts and practical implications of transitioning to quantum-enhanced models.

A distinctive feature of the course is its emphasis on experiential learning. Students will engage directly with quantum programming environments and, where possible, run experiments on real quantum hardware. By the end of the course, participants will not only be quantum-computing literate but also equipped to apply the cutting-edge research in quantum machine learning in their respective fields. This course is ideal for those seeking to expand their computational toolkit and explore the frontier of intelligent systems in the quantum age.

Prerequisites

Students are expected to have a knowledge of statistics and basic working knowledge with Python and jupyter notebooks.

Software

Participants will use Python within jupyter notebooks along the standard machine learning libraries (sci-kit) and Qiskit.

Content Outline

Fundamental principles of the quantum mechanics and quantum computing
The NISQ computers and the basic operations using quantum gates
Key quantum algorithms
Hands-on calculations using quantum computer simulators as well as real hardware
Using quantum computing as an extension to the traditional machine learning toolkit

Learning Objectives

By the end of the session, participants will be able to:

Understand the principles of quantum computing.
Run the quantum machine learning algorithms as a part of their machine learning workflow
Be aware of the strengths of the quantum computing and assess the applicability for the real world problems.

Main References

Jacquier, Antoine, et al. “Quantum Machine Learning and Optimisation in Finance.” Birmingham: Packt Publishing Ltd (2022).
Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. “The elements of statistical learning.” (2009). (online edition)

Please ensure you meet the prerequisites before registering.

Please direct enquiries to: trainingmanager@senss-dtp.ac.uk