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Quantum Machine Learning
Quantifying scrambling in quantum neural networks
arXiv
Authors: Roy J. Garcia, Kaifeng Bu, Arthur Jaffe
Year
2021
Paper ID
41030
Status
Preprint
Abstract Read
~2 min
Abstract Words
86
Citations
N/A
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2021 reference point for readers tracking recent quantum research.
- We characterize a quantum neural network's error in terms of the network's scrambling properties via the out-of-time-ordered correlator.
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