Quantum bandit with amplitude amplification exploration in an adversarial environment

Abstract

The rapid proliferation of learning systems in an arbitrarily changing environment mandates the need for managing tensions between exploration and exploitation. This work proposes a quantum-inspired bandit learning approach for the learning-and-adapting-based offloading problem where a client observes and learns the costs of each task offloaded to the candidate resource providers, e.g., fog nodes. In this approach, a new action update strategy and novel probabilistic action selection are adopted, provoked by the amplitude amplification and collapse postulate in quantum computation theory, respectively. We devise a locally linear mapping between a quantum-mechanical phase in a quantum domain, e.g., Grover-type search algorithm, and a distilled probability-magnitude in a value-based decision-making domain, e.g., adversarial multi-armed bandit algorithm. The proposed algorithm is generalized, via the devised mapping, for better learning weight adjustments on favourable/unfavourable actions and its effectiveness is verified via simulation.

Quantum contextual bandits and recommender systems for quantum data

“…Quantum algorithms for the classical multi-armed bandit problem have been studied for the settings of best-arm identification [10,11], exploration-exploitation with stochastic environments [12] (uncorrelated and linear correlated actions) and adversarial environments [13]. Also, a quantum neural network approach was considered in [14] for a simple best-arm identification problem.…”