Quantum Periodicity & Quantum Machine Learning

Purpose

Make quantum periodicity and QML concrete via executable circuits and a concise research report.

Targets

• Implement periodicity detection with explicit oracle design and inverse QFT.
• Summarize QML optimization methods and outline a hybrid classical‑quantum scheme.

Design

Oracle f(x) with period r ─► QFT-based circuit ─► measurement ─► post‑processing to infer r

Notebook contents

• Oracle construction, circuit diagrams, and statevector visualizations.
• Experiments with noise models and small‑device constraints.

Report highlights

• Survey of gradient‑based, annealing, and quantum gradient descent approaches; complexity and practicality.
• Proposal for a hybrid loop with classical preconditioning and quantum fine‑tuning.

Takeaways

• Clear didactic artifacts and a roadmap for small‑scale QML experiments.