Quantum Darwinism
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QCNNs and QNNs with tree tensor network architectures do not exhibit barren plateaus, potentially making them a generically trainable architecture for large-scale implementations.
“Moreover, it has been shown that QCNNs and QNNs with tree tensor network architectures do not exhibit barren plateaus [179, 180] (which will be discussed later), potentially making them a generically trainable architecture for large-scale implementations.”
From Variational quantum algorithms (2021) · cites this paper as [179]
Section III, “From Copying to Quantum Jumps,” discusses copying quantum-state information, while Section V discusses quantum Darwinism. Searches for barren plateaus, QCNNs, tree tensor networks, and neural-network trainability found no passage addressing the attributed claim. (LLM, conf=0.78)
Experimental — accuracy not yet measuredCertain quantum neural network architectures are trainable even for large problems.
“An exciting prospect for using quantum neural networks is that certain architectures are immune to barren plateaus, and hence are trainable even for large problems [179, 180].”
From Variational quantum algorithms (2021) · cites this paper as [179]
The opening passage describes quantum Darwinism, and Section V discusses decoherence and the measurement problem. Searches for quantum neural networks, barren plateaus, and trainability returned no passage addressing whether any architecture is trainable for large problems. (LLM, conf=0.78)
Experimental — accuracy not yet measuredCertain quantum neural network architectures are immune to barren plateaus.
“An exciting prospect for using quantum neural networks is that certain architectures are immune to barren plateaus, and hence are trainable even for large problems [179, 180].”
From Variational quantum algorithms (2021) · cites this paper as [179]
The opening passage and Section V (Discussion) address quantum Darwinism and the quantum measurement problem, not neural-network training. Searches for barren plateaus and for vanishing gradients or trainability in large quantum circuits found no passage addressing the claim. (LLM, conf=0.78)
Experimental — accuracy not yet measured
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Generic Emergence of Objectivity of Observables in Infinite Dimensions
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Self-citationCritical Bias in Critical Care Devices
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Quantum Darwinism Requires an Extra-Theoretical Assumption of Encoding Redundancy
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- Authors
- Zurek, W. H.
- Affiliations
- Los Alamos National Laboratory (US)
- Corresponding author
- Zurek, Wojciech Hubert
- Venue
- Nature Physics
- Published
- 2009-03
- Volume
- 5
- Issue
- 3
- Pages
- 181-188
- ISSN
- 1745-2473 (ISSN-L)
- 1745-2481
- OpenAlex
- W3037859133
- Cited by
- 564
- References
- 33
- Citations by year
- 2026: 77 · 2025: 44 · 2024: 40 · 2023: 37 · 2022: 41 · 2021: 36 · 2020: 31 · 2019: 37 · 2018: 48 · 2017: 24 · 2016: 28 · 2015: 25 · 2014: 18 · 2013: 24 · 2012: 22
- FWCI
- 10.04
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Physics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (public-domain)
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Mechanics and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Advanced Thermodynamics and Statistical Mechanics
- Keywords
- quantum Darwinism, measurement problem, wave packet collapse, Born rule, environmental records, decoherence, quantum-to-classical transition
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