Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T16:38:29.860112Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 0 inbound Pith citation observations for arXiv:2508.18161.
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 2026-08-05T16:38:29.860112Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
68 of 68 outbound references displayed
External citation measurements
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Observation 7e29dc29-c3d7-48a4-bd38-faebe434c218 · outbound
Hybrid Quantum-Classical Learning for Multiclass Image Classification The first pair of convolu- tions acts on the angle-encoded image and detects 5 FIG
Reference 1
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Hybrid Quantum-Classical Learning for Multiclass Image Classification The reduced register is processed by the next two convolutional layers, which integrate information across a larger effec- tive receptive field
Reference 2
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Hybrid Quantum-Classical Learning for Multiclass Image Classification The final convolutional pair operates on the remaining qubits to encode global patterns in the quantum state
Reference 3
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Reference 4
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Outputs are centred at the origin, aligning with the most sensitive region of tanh and similar non-linearities
Reference 5
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Positive and negative excursions are treated equally, preventing biased weight updates during back-propagation
Reference 6
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Hybrid Quantum-Classical Learning for Multiclass Image Classification The four-fold expan- sion of the raw range widens the gradient window, mitigating both vanishing and exploding tendencies while preserving the relative ordering of probabili- ties
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Hybrid Quantum-Classical Learning for Multiclass Image Classification The deriva- tive of tanh is largest for |x| ≲ 2
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Hybrid Quantum-Classical Learning for Multiclass Image Classification If p is approximately uniform, then Var[p] = 1/12
Reference 9
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Reference 10
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Hybrid Quantum-Classical Learning for Multiclass Image Classification We pass this vector through a single fully connected layer of width m matching the number of target classes followed by a tanh nonlinearity
Reference 11
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Hybrid Quantum-Classical Learning for Multiclass Image Classification In the four-class experiments reported here (m = 4) this is only 42 +4 = 20 trainable parameters
Reference 12
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Hybrid Quantum-Classical Learning for Multiclass Image Classification V B 2), allowing direct dot-product fusion without further projection
Reference 13
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Hybrid Quantum-Classical Learning for Multiclass Image Classification 8 We measure the discarded register, rescale the outcomes to [−2, 2], and obtain xdisc ∈ [−2, 2]m
Reference 14
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Unresolved cited work
Reference 16
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Unresolved cited work
Reference 17
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Hybrid Quantum-Classical Learning for Multiclass Image Classification V B 1– V B 2), the two quantum-derived feature streams are merged without adding any further trainable parameters
Reference 18
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Quanvolutional Neural Networks: Powering Image Recognition with Quantum Circuits
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Ciliberto, M
Reference 21
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Reference 22
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Li, R.-G
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Vatan and C
Reference 24
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Chalumuri, R
Reference 25
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Unresolved cited work
Reference 26
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Observation e11829a8-90fc-40da-924f-db6e417d14dd · outbound
Hybrid Quantum-Classical Learning for Multiclass Image Classification Gianani, I
Reference 27
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Observation a05787a2-16c7-4970-84f3-b78b8e4bc043 · outbound
Hybrid Quantum-Classical Learning for Multiclass Image Classification A hybrid quantum-classical neural network with deep residual learning
Reference 28
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Observation 29e2d20d-0963-48eb-b10a-ecc083886faf · outbound
Hybrid Quantum-Classical Learning for Multiclass Image Classification Preskill, Quantum computing in the nisq era and be- yond, Quantum 2, 79 (2018)
Reference 29
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Observation 2d792e04-ead7-4886-a9a5-f7ab3352d8a2 · outbound
Hybrid Quantum-Classical Learning for Multiclass Image Classification Bharti, A
Reference 30
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Easom-McCaldin, A
Reference 31
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Reference 32
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Chandra, S
Reference 33
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Reference 34
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Abbas, D
Reference 35
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Bokhan, A
Reference 36
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Chalumuri, R
Reference 37
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Quantum embeddings for machine learning
Reference 38
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Reference 39
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Reference 40
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Reference 41
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Rebentrost, M
Reference 42
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Hybrid Quantum-Classical Learning for Multiclass Image Classification Lloyd, M
Reference 43
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Reference 46
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Reference 48
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Reference 49
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Reference 50
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Reference 51
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Reference 52
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Reference 54
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Reference 55
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Reference 57
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Reference 58
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Reference 59
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Reference 60
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Reference 61
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Reference 62
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Reference 67
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Reference 68
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No inbound Pith citation observations are available.