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.
What you will learn
- Implement variational quantum classifiers and understand what the circuit is actually computing
- Build quantum kernel methods and compare them against classical kernels on the same data
- Encode classical data into quantum states, and recognize why encoding dominates performance
- Diagnose barren plateaus and other trainability failures in variational models
- Run experiments on cloud quantum hardware and interpret noisy results correctly
- Benchmark quantum models against tuned classical baselines without stacking the deck
- Assess published QML claims: what was measured, what was compared, and what was left out
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, 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.