Mitigating the Effects of Concept Drift from Data Streams in Quantum Machine Learning

Quantum computing offers potential advancements in machine learning. However, the impact of concept drift remains unexplored when introducing data streams into quantum machine learning models. Hence, this study is the first to examine the impact of concept drift on quantum support vector machines and quantum neural networks, contrasting them with their classical counterparts. Our study is based on two data streams provided by the RiverML Library, namely, the real KDDCUP99 dataset and the synthetic RandomRBFDrift dataset. We demonstrate that the quantum models under study are more susceptible to concept drift compared with their classical counterparts in the KDDCUP99 dataset. This is seen in the results as more severe drops with longer recovery duration exist in the prequential area under the curve (AUC) of the quantum models' receiver operating characteristics curve (ROC). On the other hand, comparable results are observed in the RandomRBFDrift dataset. As a mitigation strategy for concept drift in quantum models, we propose an optimal encoding strategy based on a genetic optimization that maximizes the AUC despite concept drift. The proposed strategy determines, based on the dataset, an optimal encoding of the classical data into quantum states using a combination of angle, amplitude, and basis encoding, optimal repetitions, circuit depth, etc. Our results demonstrate that the proposed strategy enhances the performance of the quantum models in the presence of concept drift, showcasing up to 5% improvement in the model accuracy. This study provides valuable insights into the effects and mitigation of concept drift in quantum machine learning and opens the door for future research.