SAFE Quantum Machine Learning with Variational Quantum Classifiers

Vasily Kolesnikov, Paolo Recchia, Ying Chen, Paolo Giudici

Keywords: Security, Accuracy, Explainability, Quantum AI

Abstract: We propose a variational quantum classifier operating on high-dimensional deep representations via amplitude encoding, stabilized by a learnable classical pre-encoding layer. By combining normalized amplitude embeddings with bounded quantum observables, the resulting model induces a structured and smooth hypothesis class with controlled sensitivity to input variations. Model reliability is assessed using SAFE-AI metrics derived from the Cramér–von Mises divergence, enabling consistent evaluation across accuracy, robustness, and explainability dimensions. Empirical results show that the proposed quantum model provides comparable accuracy to strong classical baselines while exhibiting a more balanced SAFE reliability profile, with improved robustness to noise and stability under structured feature removal. These findings suggest that variational quantum circuits offer a principled mechanism for stability-oriented SAFE learning in safety-critical settings.

Presentation Format: We prefer to present our paper in a 15-20 minute talk

Data Release: We authorize the release of our submission and author names to the public in the event of acceptance.

LLM Policy: LLMs may be used to understand the paper and related work but may not be used to assess quality, identify strengths/weakesses, draft the review structure or write the review itself

Submission Number: 15