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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