Quantum computing is not a distant concept anymore. Research labs and
technology companies are actively integrating quantum approaches
into machine learning pipelines, optimization problems, and data
processing tasks. This course gives practitioners a grounded,
technical understanding of how quantum systems work and where
they realistically fit alongside classical AI infrastructure.
What the course covers
Participants start with the physics behind superposition and
entanglement, then move into quantum circuit design using
Qiskit. The second half of the program focuses on hybrid
quantum-classical models, including variational quantum
eigensolvers and quantum approximate optimization. Students also
examine how large language models like Claude AI handle
reasoning tasks that quantum systems currently cannot replicate,
which clarifies where each technology has genuine advantages.
Who this is for
The program suits software engineers and data scientists who already
work with machine learning and want to understand quantum
computing without a physics degree. Prior experience with Python
and basic linear algebra is expected. The course does not assume
familiarity with quantum mechanics.
Lectures are recorded and available asynchronously. Live Q&A
sessions run twice per week with the lead instructor, Tobias
Wrenfield, a researcher who has published on quantum error
correction at two national laboratories.
- 12 weeks, self-paced with structured milestones
- Access to IBM Quantum hardware for lab exercises
- Certificate of completion issued upon passing final
assessment
