Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Lumbreras, Josep"'
We initiate the study of quantum state tomography with minimal regret. A learner has sequential oracle access to an unknown pure quantum state, and in each round selects a pure probe state. Regret is incurred if the unknown state is measured orthogon
Externí odkaz:
http://arxiv.org/abs/2406.18370
Autor:
Lumbreras, Josep, Tomamichel, Marco
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. We introduce an algorithm for this problem that exhibits
Externí odkaz:
http://arxiv.org/abs/2402.12042
We show that marginals of blocks of $t$ systems of any finitely correlated translation invariant state on a chain can be learned, in trace distance, with $O(t^2)$ copies -- with an explicit dependence on local dimension, memory dimension and spectral
Externí odkaz:
http://arxiv.org/abs/2312.07516
We study a recommender system for quantum data using the linear contextual bandit framework. In each round, a learner receives an observable (the context) and has to recommend from a finite set of unknown quantum states (the actions) which one to mea
Externí odkaz:
http://arxiv.org/abs/2301.13524
Publikováno v:
Commun. Math. Phys. 405(2):50, 2024
Arguably, the largest class of stochastic processes generated by means of a finite memory consists of those that are sequences of observations produced by sequential measurements in a suitable generalized probabilistic theory (GPT). These are constru
Externí odkaz:
http://arxiv.org/abs/2209.11225
Publikováno v:
Quantum 6, 749 (2022)
We initiate the study of tradeoffs between exploration and exploitation in online learning of properties of quantum states. Given sequential oracle access to an unknown quantum state, in each round, we are tasked to choose an observable from a set of
Externí odkaz:
http://arxiv.org/abs/2108.13050
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Akademický článek
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Publikováno v:
Quantum Machine Intelligence; December 2024, Vol. 6 Issue: 2