Resources:
- “Introduction | IBM Quantum Learning,” IBM Quantum Learning, 2021. https://quantum.cloud.ibm.com/learning/en/courses/quantum-machine-learning/introduction (accessed Feb. 20, 2026).
Introduction to Quantum Machine Learning
A Comprehensive Course Overview
The Classical-Quantum Intersection
Quantum Machine Learning (QML) is a symbiotic cycle where quantum and classical systems push each other’s limits. The current focus remains on applying quantum algorithms to classical datasets to complement existing workflows where classical systems already excel.
Feature Mapping & Kernels
A primary goal in QML is mapping data into higher-dimensional spaces. If data points are not separable by a hyperplane in their original dimensions, they can be projected into a higher-dimensional space where they become separable.
Critical Realities of QML
- Beyond Superposition: High dimensionality and entanglement are not sufficient conditions for increased power.
- Dequantization: Several algorithms initially thought to provide speedups have been replaced by classical alternatives.
- Feature Engineering: Success requires investigating circuits that behave differently from classical counterparts on complex data structures.
The Qiskit Pattern Workflow
1. Map
Inputs to Circuit
Inputs to Circuit
2. Optimize
Execution readiness
Execution readiness
3. Execute
Runtime Primitives
Runtime Primitives
4. Analyze
Post-processing
Post-processing