# Q-SafeML: Safety Assessment of Quantum Machine Learning via Quantum Distance Metrics

arXiv: https://arxiv.org/abs/2509.04536 · HTML: https://mlsanity.com/paper/2509.04536/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 4 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities](https://mlsanity.com/paper/2008.08221/) (2020)
- [SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure](https://mlsanity.com/paper/2005.13166/) (2020)
- [SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents](https://mlsanity.com/paper/2308.02594/) (2023)
- [Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations](https://mlsanity.com/paper/2311.07286/) (2023)
- [Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring](https://mlsanity.com/paper/2207.05078/) (2022)
- [SafeDrones: Real-Time Reliability Evaluation of UAVs using Executable Digital Dependable Identities](https://mlsanity.com/paper/2207.05643/) (2022)
- [StaDRe and StaDRo: Reliability and Robustness Estimation of ML-based Forecasting using Statistical Distance Measures](https://mlsanity.com/paper/2206.11116/) (2022)
- [Scope Compliance Uncertainty Estimate](https://mlsanity.com/paper/2312.10801/) (2023)

## Built on by (most cited first, 1 of 1)

- [Formal Verification of Noisy Quantum Reinforcement Learning Policies](https://mlsanity.com/paper/2512.01502/) (2025)

## Abstract (arXiv)

The rise of machine learning in safety-critical systems has paralleled advancements in quantum computing, leading to the emerging field of Quantum Machine Learning (QML). While safety monitoring has progressed in classical ML, existing methods are not directly applicable to QML due to fundamental differences in quantum computation. Given the novelty of QML, dedicated safety mechanisms remain underdeveloped. This paper introduces Q-SafeML, a safety monitoring approach for QML. The method builds on SafeML, a recent method that utilizes statistical distance measures to assess model accuracy and provide confidence in the reasoning of an algorithm. An adapted version of Q-SafeML incorporates quantum-centric distance measures, aligning with the probabilistic nature of QML outputs. This shift to a model-dependent, post-classification evaluation represents a key departure from classical SafeML, which is dataset-driven and classifier-agnostic. The distinction is motivated by the unique representational constraints of quantum systems, requiring distance metrics defined over quantum state spaces. Q-SafeML detects distances between operational and training data addressing the concept drifts in the context of QML. Experiments on QCNN and VQC Models show that this enables informed human oversight, enhancing system transparency and safety.