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

说明与用途

该架构结合本地量子嵌入、主动学习、差分隐私和全局经典模型，实现循环制造供应链中的高效安全数据共享。

问题

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

结果

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.

工作流

• 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.

证据

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.