Chaos and complexity from quantum neural network. A study with diffusion metric in machine learning
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Publication:2032539
DOI10.1007/JHEP04(2021)138zbMath1462.81100arXiv2011.07145OpenAlexW3154367330MaRDI QIDQ2032539
Debisree Ray, Sayantan Choudhury, Ankan Dutta
Publication date: 11 June 2021
Published in: Journal of High Energy Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2011.07145
Learning and adaptive systems in artificial intelligence (68T05) Quantum chaos (81Q50) Stochastic approximation (62L20) Neural nets and related approaches to inference from stochastic processes (62M45)
Related Items (4)
Spatial regions, chaos bound and its violation ⋮ Thermalization in quenched open quantum cosmology ⋮ Barren plateaus from learning scramblers with local cost functions ⋮ Quantifying scrambling in quantum neural networks
Uses Software
Cites Work
- Unnamed Item
- Black holes and the butterfly effect
- Towards the fast scrambling conjecture
- Quantum cognition: the possibility of processing with nuclear spins in the brain
- Circuit complexity for coherent states
- Circuit complexity in interacting QFTs and RG flows
- A bound on chaos
- Comments on holographic complexity
- Circuit complexity in quantum field theory
- Subsystem complexity and holography
- Holographic complexity equals which action?
- Three lectures on complexity and black holes
- Path-integral complexity for perturbed CFTs
- Time evolution of complexity: a critique of three methods
- Robust chaos in neural networks
- Information flow in entangled quantum systems
- Predictability, Complexity, and Learning
- Oscillations and chaos in neural networks: an exactly solvable model.
- Entropy-SGD: biasing gradient descent into wide valleys
- Aspects of the first law of complexity
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