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arXiv · 1712.00127

Experimental learning of quantum states

Andrea Rocchetto·Scott Aaronson·Simone Severini·Gonzalo Carvacho·Davide Poderini·Iris Agresti·Marco Bentivegna·Fabio Sciarrino
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Abstract

The number of parameters describing a quantum state is well known to grow exponentially with the number of particles. This scaling clearly limits our ability to do tomography to systems with no more than a few qubits and has been used to argue against the universal validity of quantum mechanics itself. However, from a computational learning theory perspective, it can be shown that, in a probabilistic setting, quantum states can be approximately learned using only a linear number of measurements. Here we experimentally demonstrate this linear scaling in optical systems with up to 6 qubits. Our results highlight the power of computational learning theory to investigate quantum information, provide the first experimental demonstration that quantum states can be "probably approximately learned" with access to a number of copies of the state that scales linearly with the number of qubits, and pave the way to probing quantum states at new, larger scales.

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Andrea Rocchetto, Scott Aaronson, Simone Severini, Gonzalo Carvacho, Davide Poderini, Iris Agresti, Marco Bentivegna, Fabio Sciarrino. 2017-11-30. Experimental learning of quantum states. https://arxiv.org/abs/1712.00127

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