Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection | Paper Citation Record

Observation 5068ba4d-78c7-4deb-b6e3-a14d9f1a8267 · outbound

This paper cites Sachdev,Quantum Phase Transitions, 2nd ed.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Sachdev,Quantum Phase Transitions, 2nd ed

Reference 1

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Observation cfb5fb36-9c30-4aea-bd67-6ad7c61b841b · outbound

This paper cites Sachdev,Quantum Phases of Matter(Cambridge Uni- versity Press, 2023).

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Sachdev,Quantum Phases of Matter(Cambridge Uni- versity Press, 2023)

Reference 2

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Observation 4ddc5cb3-981d-4c07-8240-3965d9250af1 · outbound

This paper cites Preskill, Quantum computing in the nisq era and be- yond, Quantum2, 79 (2018).

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Preskill, Quantum computing in the nisq era and be- yond, Quantum2, 79 (2018)

Reference 3

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This paper cites Islam, E.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Islam, E

Reference 4

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This paper cites Bloch, J.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Bloch, J

Reference 5

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Observation 585e2d78-a00d-4f0c-a742-c89a76c2e4d9 · outbound

This paper cites Peruzzo, J.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Peruzzo, J

Reference 6

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Observation 30ed29ef-e813-4b27-a68d-2c32cd9f0b5b · outbound

This paper cites Kandala, A.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Kandala, A

Reference 7

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Observation e41dfc58-be49-4253-8b8a-cb13d68baef2 · outbound

This paper cites The least squares method: application to analysis of the flavor dependence of the QCD relation between pole and $\rm{\overline{MS}}$-scheme running heavy quark masses.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection The least squares method: application to analysis of the flavor dependence of the QCD relation between pole and $\rm{\overline{MS}}$-scheme running heavy quark masses

Reference 8

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This paper cites an unresolved cited work.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 9

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 10

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Observation 31e53449-9fdc-40ca-9f57-2499bf35a93d · outbound

This paper cites Tilly, H.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Tilly, H

Reference 11

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Observation ad194676-75e4-4a27-8c00-85527c7b4bc4 · outbound

This paper cites Cerezo, A.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Cerezo, A

Reference 12

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This paper cites Li and F.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Li and F

Reference 13

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Observation 97504133-74ba-402c-878d-f0afc20f51cb · outbound

This paper cites Pollmann and A.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Pollmann and A

Reference 14

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This paper cites Haegeman, D.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Haegeman, D

Reference 15

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Observation 6e6c291e-2fd6-4e87-a50e-7068d96b740c · outbound

This paper cites Pollmann, A.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Pollmann, A

Reference 16

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Observation 27d91b81-fe53-465e-9a89-6cd12c61d711 · outbound

This paper cites Lidiak and Z.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Lidiak and Z

Reference 19

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 20

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 21

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This paper cites an unresolved cited work.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 22

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This paper cites Herrmann, S.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Herrmann, S

Reference 23

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Observation bba5f30f-b0aa-4f1a-a8c3-8727d9636d1e · outbound

This paper cites Monaco, O.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Monaco, O

Reference 24

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Observation 6e33c424-c2f4-42e2-b8c8-eac955c30b56 · outbound

This paper cites Quantum ensemble learning with a programmable superconducting processor.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Quantum ensemble learning with a programmable superconducting processor

Reference 25

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 26

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Observation 1dd4bb75-8e49-4e61-aa8d-77c4f7021843 · outbound

This paper cites Huang, R.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Huang, R

Reference 27

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Observation 78659faf-812e-4980-9301-ad66584ea23e · outbound

This paper cites Huang, R.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Huang, R

Reference 28

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This paper cites Lewis, H.-Y.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Lewis, H.-Y

Reference 29

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correction dated 2024-02-26. Source: crossref record 10.1038/s41467-024-46164-4->10.1038/s41467-024-45014-7:correction, observed 2026-07-11T03:00:38.151056+00:00. This notice travels one citation hop only.

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This paper cites Broecker, J.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Broecker, J

Reference 30

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This paper cites Canabarro, F.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Canabarro, F

Reference 31

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 32

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Observation e5e21e00-ee11-4fb3-9b31-036cc69f0410 · outbound

This paper cites ClassiFIM: An Unsupervised Method To Detect Phase Transitions.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection ClassiFIM: An Unsupervised Method To Detect Phase Transitions

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:58:35.365409Z

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 34

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 35

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Observation ed26e820-7079-4ffb-92a3-cd2e55bfe7c7 · outbound

This paper cites Autoencoder-based learning of Quantum phase transitions in the two-component Bose-Hubbard model.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Autoencoder-based learning of Quantum phase transitions in the two-component Bose-Hubbard model

Reference 36

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 38

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This paper cites Angelides, P.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Angelides, P

Reference 39

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 40

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Observation 171c2fcf-ce0b-4273-a8f1-3ef3ac481711 · outbound

This paper cites Grossi, O.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Grossi, O

Reference 41

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Observation 106730b4-2cc1-4805-b74b-0b0037442fb0 · outbound

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 42

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This paper cites Holmes, K.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Holmes, K

Reference 43

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This paper cites Ortiz Marrero, M.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Ortiz Marrero, M

Reference 44

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 45

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 46

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This paper cites an unresolved cited work.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 47

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Observation b9b1d26a-cc70-4402-ab96-121b1ba3c70a · outbound

This paper cites Non-Convex Optimization by Hamiltonian Alternation.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Non-Convex Optimization by Hamiltonian Alternation

Reference 48

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Observation af477ff5-44fd-4fcf-b5a7-b5a6b43a7027 · outbound

This paper cites Li, Quantum computers quickly find local minima, Nature Physics21, 512 (2025).

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Li, Quantum computers quickly find local minima, Nature Physics21, 512 (2025)

Reference 49

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This paper cites Unveiling quantum phase transitions from traps in variational quantum algorithms.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unveiling quantum phase transitions from traps in variational quantum algorithms

Reference 50

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Observation 826993d9-f78c-4aee-b6ec-1200c4c811dd · outbound

This paper cites Crognaletti, G.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Crognaletti, G

Reference 51

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Observation 683b1118-1608-4295-8606-9da4e405026d · outbound

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 52

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This paper cites Pearson, Liii.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Pearson, Liii

Reference 53

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Observation 23e32c40-10be-43d4-a28c-e87320ec9ddd · outbound

This paper cites Permuter, J.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Permuter, J

Reference 54

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Observation 15402fb5-6fe4-41d7-a42a-edb596ac2e18 · outbound

This paper cites Sch¨ olkopf, A.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Sch¨ olkopf, A

Reference 55

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This paper cites Unsupervised Deep Embedding for Clustering Analysis.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unsupervised Deep Embedding for Clustering Analysis

Reference 56

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This paper cites Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering

Reference 57

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This paper cites Correia, J.-C.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Correia, J.-C

Reference 58

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Observation dcfb7510-e9c1-45ea-a2ca-19980cc751c1 · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Neural Machine Translation by Jointly Learning to Align and Translate

Reference 59

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This paper cites Attention Is All You Need.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Attention Is All You Need

Reference 60

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 61

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This paper cites Scalable Diffusion Models with Transformers.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Scalable Diffusion Models with Transformers

Reference 62

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Observation 4c2bbef3-0ed2-44f3-8649-3c76a73d452b · outbound

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:58:36.909178Z

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This paper cites Disentangling by Factorising.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Disentangling by Factorising

Reference 64

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Isolating Sources of Disentanglement in Variational Autoencoders

Reference 65

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Denoising Diffusion Probabilistic Models

Reference 66

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Observation 2b8ec1f3-57e5-41d0-9a07-7a4d948a7d25 · outbound

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection High-Resolution Image Synthesis with Latent Diffusion Models

Reference 67

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Observation e90f0af9-190d-4e2e-8ab5-de44bc71af23 · outbound

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Learning Transferable Visual Models From Natural Language Supervision

Reference 68

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This paper cites Chenet al., Quantum mixed-state self-attention net- work, Information Sciences668, 1 (2025).

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Chenet al., Quantum mixed-state self-attention net- work, Information Sciences668, 1 (2025)

Reference 69

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Observation 9d1e3475-4e54-4331-9eb2-f39d3fcf8d6d · outbound

This paper cites Rocchiaet al., Unveiling the physical meaning of transformer attention in molecular wave functions, Chi- nese Physics B (2024), in press.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Rocchiaet al., Unveiling the physical meaning of transformer attention in molecular wave functions, Chi- nese Physics B (2024), in press

Reference 70

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Observation ec6274e1-fa1f-42ec-939b-e740646392a4 · outbound

This paper cites Campostrini, A.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Campostrini, A

Reference 71

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This paper cites Schuld, I.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Schuld, I

Reference 72

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Observation d4e916d0-a399-48c0-9248-1b168cb4e887 · outbound

This paper cites Luo, J.-G.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Luo, J.-G

Reference 73

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Observation 2a1ca1ff-a25b-4c65-ad25-0af12173ad71 · outbound

This paper cites Smacchia, L.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection Smacchia, L

Reference 74

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Observation 0bb10f88-f329-4dd4-bd7a-e9def56d95c6 · outbound

This paper cites MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:32.905224Z

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Observation 9a116d0b-ac8a-4c1d-b1a6-f6a1f68cf799 · outbound

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Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 76

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Observation 81ac9476-fb44-4a6b-9410-9b9c78e589d4 · outbound

This paper cites The distribution of 2001 samples in the latent space is shown in Fig.

Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection The distribution of 2001 samples in the latent space is shown in Fig

Reference 1008

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