Exact Learning with Tunable Quantum Neural Networks and a Quantum Example Oracle
VP Ngoc, H Wiklicky - arXiv preprint arXiv:2309.00561, 2023 - arxiv.org
arXiv preprint arXiv:2309.00561, 2023•arxiv.org
In this paper, we study the tunable quantum neural network architecture in the quantum
exact learning framework with access to a uniform quantum example oracle. We present an
approach that uses amplitude amplification to correctly tune the network to the target
concept. We applied our approach to the class of positive $ k $-juntas and found that $ O (n^
22^ k) $ quantum examples are sufficient with experimental results seemingly showing that
a tighter upper bound is possible.
exact learning framework with access to a uniform quantum example oracle. We present an
approach that uses amplitude amplification to correctly tune the network to the target
concept. We applied our approach to the class of positive $ k $-juntas and found that $ O (n^
22^ k) $ quantum examples are sufficient with experimental results seemingly showing that
a tighter upper bound is possible.
In this paper, we study the tunable quantum neural network architecture in the quantum exact learning framework with access to a uniform quantum example oracle. We present an approach that uses amplitude amplification to correctly tune the network to the target concept. We applied our approach to the class of positive -juntas and found that quantum examples are sufficient with experimental results seemingly showing that a tighter upper bound is possible.
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