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arXiv · 2112.01440

Quantifying scrambling in quantum neural networks

Roy J. Garcia·Kaifeng Bu·Arthur Jaffe
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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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Roy J. Garcia, Kaifeng Bu, Arthur Jaffe. 2021-12-02. Quantifying scrambling in quantum neural networks. https://doi.org/10.1007/jhep03(2022)027

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