We study non-linear bandit optimization where the learner maximizes a black-box function with zeroth order function oracle, which has been successfully applied in many critical applications such as drug discovery and materials design. Existing works have showed that with the aid of quantum computing, it is possible to break the classical Ω(√T) regret lower bound and achieve the new O(poly log T) upper bound. However, they usually assume that the objective function sits within the reproducing kernel Hilbert space and their algorithms suffer from the curse of dimensionality. In this paper, we propose the new Q-NLB-UCB algorithm which enjoys an input dimension-free O(poly log T) upper bound, making it applicable for high-dimensional tasks. At the heart of our algorithm design are quantum Monte Carlo mean estimator, parametric function approximation technique, and a new quantum non-linear regression oracle, which can be of independent interests in more quantum machine learning problems. Our algorithm is also validated for its efficiency compared with other quantum algorithms on both high-dimensional synthetic and real-world tasks.

Spatial flood susceptibility mapping (FSM) is one of the key components of flood risk assessment. Recent studies showed the efficacy of hybridized machine learning (ML)-based models in predicting flood susceptibility. The quality of this prediction depends on the presence or absence of label noise in the training data collected from real world flood inundation maps. However, no study has been conducted to explore the effects of label noise in ML based spatial FSM. In the present study, we have explored the effects of four different random class label noises on the regression performances and model complexities of both standalone and hybridized ML based FSM. We also explored the optimal hyperparameter values of different hybridized ML and reported the robustness of each model in the presence and absence of label noise. The hybridized ML models showed greater efficacy in modeling spatial flood susceptibility in both cases.