Read the research.
Check the assumptions.
Foundational reviews, landmark papers, and maintained software documentation. External links require internet access; the rest of this site works offline.
Biamonte et al., Quantum machine learning (Nature, 2017)Open original source ↗
Schuld & Petruccione, Supervised Learning with Quantum ComputersOpen original source ↗
Cerezo et al., Variational quantum algorithms (Nature Reviews Physics, 2021)Open original source ↗
Havlíček et al., Supervised learning with quantum-enhanced feature spaces (Nature, 2019)Open original source ↗
Schuld & Killoran, Quantum machine learning in feature Hilbert spaces (PRL, 2019)Open original source ↗
McClean et al., Barren plateaus in quantum neural network training landscapes (Nature Communications, 2018)Open original source ↗
Huang et al., Power of data in quantum machine learning (Nature Communications, 2021)Open original source ↗
Qiskit Machine Learning documentationOpen original source ↗
PennyLane demonstrations and documentationOpen original source ↗
scikit-learn user guideOpen original source ↗
Quantum Algorithm ZooOpen original source ↗
Research note: citations support further study, not a blanket claim that quantum learning outperforms classical methods. Verify each paper’s data access, computational assumptions and benchmarks.