Editing Quantum Ml (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Getting started with QML research:
# Learn PennyLane (most ML-friendly QML library) or Qiskit (IBM ecosystem).
# Start with VQE on small molecules (H₂, LiH) — the most mature QML application.
# Implement a quantum kernel SVM on a small dataset; compare against RBF SVM.
# Use quantum hardware simulators (statevector simulation) before accessing real quantum hardware.
# For real hardware access: IBM Quantum (free tier), Amazon Braket, Google Quantum AI, Azure Quantum.
# Track the field through arXiv quant-ph and Nature/Science QML papers — the field is evolving rapidly, and the current consensus on what's tractable changes frequently.

[[Category:Artificial Intelligence]]
[[Category:Quantum Computing]]
[[Category:Machine Learning]]
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