Build quantum ML models, then measure them honestly
Quantum machine learning has more speculative claims than any other area of quantum computing. In this course, you 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 where they fall short.
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 first module covers the quantum computing concepts you need.
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.
To evaluate other people’s claims without building models yourself, take Evaluating Quantum AI and Machine Learning. It requires no coding.
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
When you finish, we email you the course handbook, a PDF of every slide with notes that explain it in full. We also include a PDF copy of Quantum Systems Integration. The handbook is yours to keep.
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. Quantum Computing Fundamentals covers the quantum side if you want a grounding first. AI for Quantum Engineering covers machine learning applied to the hardware. Quantum-Safe AI Systems covers the security of AI systems in production.
Why we teach this
The material is based on Quantum Systems Integration, the reference text on quantum computing, networking, and sensing written by the course author. It also draws on faculty who work with quantum engineering and integration teams. We teach what we have had to get right in the field. We focus on the judgment calls you will face in practice, and vendor roadmaps are not part of the course.
Certificate of Completion
Upon completion, you receive a Quantum Academy certificate of completion. It is separate from our professional certifications and states your CPE credits, one for each 60 minutes of teaching. ISC2 counts them as professional development, and other certifying bodies set their own rules. The credits also count toward renewing a Quantum Academy certification. Our CPE credits page explains how to claim them.
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.