Related papers: Quantifying scrambling in quantum neural networks

Quantifying scrambling in quantum neural networks

URL: http://arxiv.org/abs/2112.01440v2
Date: Fri, 7 Jan 2022 20:44:30 GMT
Title: Quantifying scrambling in quantum neural networks
Authors: Roy J. Garcia, Kaifeng Bu, Arthur Jaffe
Abstract summary: We characterize a quantum neural network's error in terms of the network's scrambling properties via the out-of-time-ordered correlator. Our results pave the way for the exploration of quantum chaos in quantum neural networks.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We characterize a quantum neural network's error in terms of the network's scrambling properties via the out-of-time-ordered correlator. A network can be trained by optimizing either a loss function or a cost function. We show that, with some probability, both functions can be bounded by out-of-time-ordered correlators. The gradients of these functions can be bounded by the gradient of the out-of-time-ordered correlator, demonstrating that the network's scrambling ability governs its trainability. Our results pave the way for the exploration of quantum chaos in quantum neural networks.

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