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Linear bandits with polylogarithmic minimax regret
We study a noise model for linear stochastic bandits for which the subgaussian noise parameter vanishes linearly as we select actions on the unit sphere closer and closer to the unknown vector.
Learning finitely correlated states: stability of the spectral reconstruction
A refined error bound can be proven for $C^*$-finitely correlated states, which have an operational interpretation in terms of sequential quantum channels applied to the memory system.
Quantum contextual bandits and recommender systems for quantum data
Using this model we formulate the low energy quantum state recommendation problem where the context is a Hamiltonian and the goal is to recommend the state with the lowest energy.
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