Build quantum ML models, then measure them honestly
Quantum machine learning attracts more speculative claims than any other corner of quantum computing. This course takes the opposite approach: you will build working models on real quantum frameworks, run them on simulators and cloud hardware, and benchmark them against strong classical baselines. You leave knowing what these methods can do today, and with the judgment to recognize what they cannot.
Who this course is for
Data scientists, machine learning engineers, and developers who are comfortable with Python and basic ML workflows and want practical, code-level experience with quantum approaches. No physics background is required; the quantum computing concepts you need are built up in the first module.
Curriculum
- Quantum computing for ML practitioners: qubits, gates, and circuits in one focused module
- Data encoding strategies and their consequences
- Variational quantum classifiers: architecture, training loops, and gradients
- Quantum kernel methods and where they genuinely differ
- Trainability problems: barren plateaus, noise, and expressibility trade-offs
- Running on hardware: cloud platforms, shot budgets, and error awareness
- The benchmarking lab: quantum versus tuned classical models on identical tasks
- Reading QML research and vendor claims critically
Format and delivery
Hands-on labs in Python throughout, with prepared notebooks you keep. Available online on-demand, live online, in person, and as private team training. Approximately 8 hours including labs.
Prerequisites
Working Python and familiarity with basic machine learning concepts (training, validation, overfitting). No prior quantum experience needed.
Who this course is for
Machine learning engineers and data scientists moving into quantum, quantum software developers extending into ML, and researchers who need to build and benchmark rather than read about it. Working Python and classical ML knowledge is assumed.
If you need to evaluate other people’s claims rather than build your own models, Evaluating Quantum AI and Machine Learning does that without the code.
What you’ll be able to do afterward
- Build and train a variational quantum classifier
- Implement quantum kernel methods and read their results
- Benchmark a quantum model against a tuned classical baseline on the same task
- Judge published QML claims against the benchmarking standard they should meet
What you leave with
You leave with the course handbook, a PDF of the full material with the instructor notes written out in place of the slides’ bullet points, and a PDF copy of Quantum Systems Integration, included at no extra cost. The handbook is yours to keep. For most organizations, that shared reference is the clearest return on a training budget.
Enrollment includes 180 days of access to the online on-demand course. Where that course is not yet published, the 180 days start on the day it is.
Where this course fits
Working Python and basic machine learning concepts are required, as the labs assume both. No prior quantum experience is needed, and Quantum Computing Fundamentals fills that side in if you want it. AI for Quantum Engineering for machine learning applied to the hardware, and Quantum-Safe AI Systems for the security of AI systems in production.
Why we teach this
The material comes from Quantum Systems Integration, the reference text on quantum computing, networking, and sensing written by the course author, and from faculty who work with quantum engineering and integration teams. We teach what we have had to get right in the field. The course teaches the judgment calls, and leaves vendor roadmaps to the vendors.
Certificate of Completion
Upon completion, you will receive a Quantum Academy certificate of completion. This is not a professional certification.
About this program
Quantum Academy credentials are private professional credentials issued by Quantum Academy, a trade name of Post-Quantum Institute. They are not government-issued licenses, accredited degrees, or academic credit, and earning one does not guarantee employment, promotion, regulatory approval, or any other specific outcome.
Quantum Academy programs are educational and informational only, and are not legal, compliance, or engineering advice.