Privacy-Preserving Active Learning for circular manufacturing supply chains in hybrid quantum-classical pipelines

Описание и применение

Архитектура сочетает локальные квантовые эмбеддинги, активное обучение, дифференциальную приватность и глобальную классическую модель для эффективного и безопасного обмена данными в цепочках поставок циркулярного производства.

Задача

Circular manufacturing requires cross‑party data sharing while protecting proprietary material composition and reducing labeling costs.

Результат

The architecture reduces labeling effort by focusing on high‑entropy samples while maintaining differential privacy, and leverages quantum embeddings to compensate for limited feature richness.

Рабочий процесс

• 1. Each participant runs a local variational quantum circuit to embed material spectra into a compact feature vector.
• 2. The classical active learning selector queries only the most informative unlabeled samples based on predictive entropy.
• 3. Selected samples are labeled by the participant and gradients are clipped and noised via DP‑SGD before aggregation.
• 4. Aggregated, privacy‑protected gradients update a global classical model that predicts material quality.
• 5. The process repeats until desired performance or privacy budget is met.

Доказательства

Active learning selects the most informative unlabeled samples for annotation.

Differential privacy (DP) bounds the influence any single record can have on model outputs.

Quantum circuits can encode classical data into high‑dimensional Hilbert spaces using angle or amplitude embedding.

The architecture I converged on has four layers: Local quantum feature extractor, Classical active learning selector, DP‑protected aggregation, Global classical head.