Entanglement entropy production in Quantum Neural Networks
نویسندگان
چکیده
Quantum Neural Networks (QNN) are considered a candidate for achieving quantum advantage in the Noisy Intermediate Scale computer (NISQ) era. Several QNN architectures have been proposed and successfully tested on benchmark datasets machine learning. However, quantitative studies of QNN-generated entanglement investigated only up to few qubits. Tensor network methods allow emulate circuits with large number qubits wide variety scenarios. Here, we employ matrix product states characterize recently studied random parameters fifty showing that their entanglement, measured terms entropy between qubits, tends Haar distributed as depth is increased. We certify randomness also by measuring expressibility circuits, well using tools from theory. show universal behavior rate at which created any given architecture, consequently introduce new measure production QNNs: entangling speed. Our results characterise properties neural networks, provides evidence these approximate unitaries.
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ژورنال
عنوان ژورنال: Quantum
سال: 2023
ISSN: ['2521-327X']
DOI: https://doi.org/10.22331/q-2023-05-31-1023