Research note
Quantum Machine Learning / State Preparation
Notes and experiments around quantum feature maps, state preparation, and QML model behavior.
Research question
How do state preparation choices influence quantum machine learning experiments, and where do simple baselines reveal the real difficulty?
Motivation
QML experiments are most useful when the encoding, baseline, and measurement assumptions are made explicit.
Methods
Quantum circuits State preparation Variational experiments Baseline comparisons
Approach
- Separate data encoding from trainable circuit design.
- Compare QML results against classical baselines before interpreting gains.
- Track circuit depth, parameter count, and simulation limits.
Next steps
- Create a compact state-preparation reference note.
- Add reproducible toy experiments with clear classical controls.
Current results
Placeholder for organized experiment outcomes and failure-mode notes.
Tools
Python Qiskit PennyLane NumPy
Status: Experiment planning and note consolidation