Quantifying scrambling in quantum neural networks

DOI10.1007/JHEP03(2022)027OpenAlexW3216059184MaRDI QIDQ2090812

Roy J. Garcia, Arthur Jaffe, Kaifeng Bu

Publication date: 31 October 2022

Published in: Journal of High Energy Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2112.01440

zbMATH Keywords

stochastic processesrandom systems

Mathematics Subject Classification ID

Quantum theory (81-XX)

Related Items (1)

Barren plateaus from learning scramblers with local cost functions

Uses Software

• ImageNet
• AdaGrad
• AlexNet

Cites Work

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• Black holes and the butterfly effect
• Multiple shocks
• Local random quantum circuits are approximate polynomial-designs
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• Random quantum circuits are approximate 2-designs
• Chaos in quantum channels
• A bound on chaos
• Chaos and complexity by design
• Localized shocks
• Chaos and complexity from quantum neural network. A study with diffusion metric in machine learning
• Entanglement, quantum randomness, and complexity beyond scrambling
• Robust chaos in neural networks
• Oscillations and chaos in neural networks: an exactly solvable model.

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