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Thesis

Algorithmic models in quantum mechanics

Abstract:

We study classical and quantum learning algorithms with access to data produced by a quantum process. First, we consider the problem of learning quantum states and, in the framework of the probably approximately correct (PAC) model, prove that stabiliser states are efficiently learnable. Second, we introduce a generative model based on artificial neural networks capable of finding efficient representations of quantum states and assess its performance on states with varying levels of complexit...

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Division:
MPLS
Department:
Computer Science
Role:
Author

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Supervisor
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Name:
Engineering and Physical Sciences Research Council
Grant:
1653586
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
Language:
English
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UUID:
uuid:63537c54-f080-49f7-b0c5-7417a2ef53af
Deposit date:
2019-09-22

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