Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T13:14:10.668452Z
Paper Citation Record · LEDGER
As of 9 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 0 inbound Pith citation observations for arXiv:2509.00854.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T13:14:10.668452Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
Source: cited_works
77 of 77 outbound references displayed
External citation measurements
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Observation a3176cc7-ff09-4d6c-921d-11f383db67cf · outbound
Assessing the Advantages and Limitations of Quantum Neural Networks in Regression Tasks Schalkoff, Artificial Neural Networks , McGraw-Hill, 1997
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Assessing the Advantages and Limitations of Quantum Neural Networks in Regression Tasks Pedrycz e S.M
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Assessing the Advantages and Limitations of Quantum Neural Networks in Regression Tasks Leonard e M.A
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Assessing the Advantages and Limitations of Quantum Neural Networks in Regression Tasks A Survey on Deep Neural Network Compression: Challenges, Overview, and Solutions
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Assessing the Advantages and Limitations of Quantum Neural Networks in Regression Tasks A review of Quantum Neural Networks: Methods, Models, Dilemma
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Assessing the Advantages and Limitations of Quantum Neural Networks in Regression Tasks Altaisky et al., Quantum neural networks: Current status and prospects for development , Physics of Particles and Nuclei , vol
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Assessing the Advantages and Limitations of Quantum Neural Networks in Regression Tasks 1447–1457
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Assessing the Advantages and Limitations of Quantum Neural Networks in Regression Tasks West et al., Benchmarking adversarially robust quantum machine learning at scale , Phys
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Assessing the Advantages and Limitations of Quantum Neural Networks in Regression Tasks classical: A comprehensive bench- mark study for predicting time series with variational quantum machine learning, arXiv preprint arXiv:2504.12416, 2025
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Assessing the Advantages and Limitations of Quantum Neural Networks in Regression Tasks Adam et al., No Free Lunch Theorem: A Review , in Ap- proximation and Optimization : Algorithms, Complexity and Appli- cations, Springer International Publishing, 2019, pp
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Assessing the Advantages and Limitations of Quantum Neural Networks in Regression Tasks Adam: A Method for Stochastic Optimization
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