Paper Citation Record · LEDGER

Distinguishing Ordered Phases using Machine Learning and Classical Shadows

As of 10 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 1 inbound Pith citation observation for arXiv:2501.17837.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.17837 v2

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-23T04:23:06.386395Z

measured 68 of 68 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:54:36.370979Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-07T04:54:37.464695Z

Reference resolution

67 of 67 outbound references displayed

verified exact1
verified fuzzy57
unresolved9
parse uncertain0
malformed identifier0
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External citation measurements

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Outbound references

Observation 455f6637-e8a9-4138-a32c-87c1b112d559 · outbound

This paper cites Both models are relevant for describing the magnetic properties of real materials and display rich phase diagrams with multiple ordered and disordered phases.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Both models are relevant for describing the magnetic properties of real materials and display rich phase diagrams with multiple ordered and disordered phases

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.945140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 09c4b44f-1fd5-4691-a6bd-9a30186bcdd0 · outbound

This paper cites In this phase, the system spontaneously breaks the Z2 symmetry σz j 7→ −σz j.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows In this phase, the system spontaneously breaks the Z2 symmetry σz j 7→ −σz j

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.922282Z

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Observation 20c2abca-e4c3-42c2-831f-1e6690f5e142 · outbound

This paper cites an unresolved cited work.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-05-23T04:27:33.081824Z

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Observation 1126672b-c28b-46bb-ada5-62cdeb038ebf · outbound

This paper cites antiphase.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows antiphase

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.930040Z

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Observation e3868080-a813-4287-bf87-1b2a77efabcf · outbound

This paper cites floating phase.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows floating phase

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.918115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 143c9fd2-57e5-4fee-97a8-fd74cce7f19b · outbound

This paper cites an unresolved cited work.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-05-23T04:27:32.926239Z

Source-reported events for the cited work

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Observation 2ba08d3a-b5b1-4f0e-b2b0-ae657d5dbb6b · outbound

This paper cites This corresponds to a narrow region around the pure Kitaev limit at ϕ = π 2 , where J = 0 and K = 1.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows This corresponds to a narrow region around the pure Kitaev limit at ϕ = π 2 , where J = 0 and K = 1

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.017054Z

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Observation 2a2ec996-cc5d-4f0d-9c31-4a17310fcd63 · outbound

This paper cites an unresolved cited work.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-05-23T04:27:32.980927Z

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Observation a809e776-6394-43d3-b8f2-19a486fce3a5 · outbound

This paper cites an unresolved cited work.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-05-23T04:27:32.959424Z

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Observation b1e9d6e7-5944-45ed-bccb-e1058fc89dd4 · outbound

This paper cites Interestingly, this spin liquid phase is significantly wider than its antiferromagnetic counterpart.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Interestingly, this spin liquid phase is significantly wider than its antiferromagnetic counterpart

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.941100Z

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Observation 746c9991-fd24-4d6d-a08e-67b5007d3df5 · outbound

This paper cites snapshot.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows snapshot

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.904282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2da8de82-613a-45ed-ad9f-3b358c5f94a7 · outbound

This paper cites Solving the quantum many-body problem with artificial neural networks.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Solving the quantum many-body problem with artificial neural networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.970436Z

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Observation 41d19fbc-2dcc-452d-b145-ce05022d1109 · outbound

This paper cites Neural-network quantum state tomography.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Neural-network quantum state tomography

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.893483Z

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Observation 8aed945f-98c4-420e-ae81-58efed83d104 · outbound

This paper cites Machine learning and the physical sciences.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Machine learning and the physical sciences

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.937383Z

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Observation 6dcab316-f08f-4845-87e7-6eaf34483288 · outbound

This paper cites Machine learning for quantum matter.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Machine learning for quantum matter

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.013282Z

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Observation c832f2ff-d4f4-4131-b399-8126d22349ed · outbound

This paper cites Predicting many properties of a quantum system from very few measurements.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Predicting many properties of a quantum system from very few measurements

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.875492Z

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Observation 7c521617-ddec-4260-999b-3dfebb086bbe · outbound

This paper cites Machine learning phases of matter.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Machine learning phases of matter

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.860655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c1a7336-e457-4243-a1e0-95ed4748bbe4 · outbound

This paper cites Machine learning of quantum phase transitions.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Machine learning of quantum phase transitions

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.864208Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f47b7a34-5c43-4613-bbfb-fbed460f228d · outbound

This paper cites Unsupervised machine learning of quantum phase transitions using diffusion maps.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Unsupervised machine learning of quantum phase transitions using diffusion maps

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.857079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b66944c7-00bf-4018-9228-c614ce9dd87e · outbound

This paper cites Machine learning quantum phases of matter beyond the fermion sign problem.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Machine learning quantum phases of matter beyond the fermion sign problem

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.002499Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 13f52531-0b63-41f1-989d-2948d9de999f · outbound

This paper cites Identifying topological order through unsupervised machine learning.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Identifying topological order through unsupervised machine learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.871772Z

Source-reported events for the cited work

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Observation e9b61b6b-fa8a-4593-8485-20bb9cc3b1cd · outbound

This paper cites Machine learning of quantum phase transitions.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Machine learning of quantum phase transitions

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.910882Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c799085f-1b5c-4c50-86c2-a0ae021d9bba · outbound

This paper cites Machine learning phase transitions with a quantum processor.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Machine learning phase transitions with a quantum processor

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.984310Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9478664c-fd04-477a-8cab-f55155459228 · outbound

This paper cites Predicting topological invariants and unconventional superconducting pairing from density of states and machine learning.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Predicting topological invariants and unconventional superconducting pairing from density of states and machine learning

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:25:23.568159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c8a52a8e-deee-4a49-bb4a-9d4e646bc413 · outbound

This paper cites Topological quantum phase transitions retrieved through unsupervised machine learning.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Topological quantum phase transitions retrieved through unsupervised machine learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.075253Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 22e71465-932c-4e22-88bf-1d3fd251ec12 · outbound

This paper cites Quantum monte carlo simulations of solids.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Quantum monte carlo simulations of solids

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.991788Z

Source-reported events for the cited work

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Observation f86dc73b-6e4a-4a4e-b925-8fbbfe6bc87b · outbound

This paper cites Efficient quantum state tomography.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Efficient quantum state tomography

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.065159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 03febfdd-2dcb-4ef0-9723-06b08c15c3d8 · outbound

This paper cites Challenges and opportunities in quantum machine learning.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Challenges and opportunities in quantum machine learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.973917Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 70a0456f-be34-44c3-99bc-b24c5954ef3f · outbound

This paper cites Training variational quantum algorithms is np-hard.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Training variational quantum algorithms is np-hard

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.963421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e6e38cf2-780c-4c0a-a2b9-c80e6e1e9419 · outbound

This paper cites Power of data in quantum machine learning.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Power of data in quantum machine learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.061475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6d10776d-2526-41e8-b801-8582bf20f1b7 · outbound

This paper cites Shadow tomography of quantum states.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Shadow tomography of quantum states

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.068698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 88b438c3-4d7a-442b-84ab-be6a64d76fdf · outbound

This paper cites The annni model—theoretical analysis and experimental application.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows The annni model—theoretical analysis and experimental application

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.071907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 458d26af-9f94-4e1a-9d90-8081ee4520d4 · outbound

This paper cites Sørensen, and Hae-Young Kee.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Sørensen, and Hae-Young Kee

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.084940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 70b03f75-7531-442b-8b4e-81b4c4bbee1e · outbound

This paper cites Topological characterization of quantum phase transitions in a spin-1/2 model.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Topological characterization of quantum phase transitions in a spin-1/2 model

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.051339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0b5a3dbd-d0af-4b74-95b5-3829f95ebfe8 · outbound

This paper cites an unresolved cited work.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-05-23T04:27:33.054495Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aa5b71a7-ac44-4908-935e-73891ab2bf93 · outbound

This paper cites Least squares quantization in pcm.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Least squares quantization in pcm

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.043878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f3b28bda-5dc5-4208-8c30-95865af52569 · outbound

This paper cites an unresolved cited work.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-05-23T04:27:33.023147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7dc650a3-06c9-43df-987c-3f57e200d1e8 · outbound

This paper cites Villain and P.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Villain and P

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.020136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e9f2297f-701c-4a01-b78d-fa1f6ebafa6d · outbound

This paper cites A two-leg quantum ising ladder: a bosonization study of the ANNNI model.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows A two-leg quantum ising ladder: a bosonization study of the ANNNI model

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.036894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9305fb5e-d68f-45dc-8e39-42598f960d5c · outbound

This paper cites The one-dimensional ANNNI model in a transverse field: analytic and numerical study of effective hamiltonians.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows The one-dimensional ANNNI model in a transverse field: analytic and numerical study of effective hamiltonians

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.040217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 51b5c553-9ee7-4206-937e-c32a191ceb0f · outbound

This paper cites Colares Guimarães, João A.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Colares Guimarães, João A

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.047135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 583f6ecf-b72f-4fd5-9b42-8a5130d585c6 · outbound

This paper cites Evidence for a floating phase of the transverse annni model at high frustration.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Evidence for a floating phase of the transverse annni model at high frustration

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.057898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 256e4d5d-1b25-47a2-98cb-91978811a9d9 · outbound

This paper cites Exploring phase transitions by finite- entanglement scaling of MPS in the 1d ANNNI model.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Exploring phase transitions by finite- entanglement scaling of MPS in the 1d ANNNI model

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.088414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 11e8140e-0a86-43d5-ba77-60aa83930253 · outbound

This paper cites Anyons in an exactly solved model and beyond.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Anyons in an exactly solved model and beyond

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.033766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4cab1ad3-c915-40d7-9439-da3cef9b751d · outbound

This paper cites Pereira and Reinhold Egger.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Pereira and Reinhold Egger

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.914325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a492784c-2dda-49e4-922a-09df7f51daf2 · outbound

This paper cites an unresolved cited work.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-05-23T04:27:33.026601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0653001d-7d95-4c2f-bfe4-d018b493e5cc · outbound

This paper cites Kitaev-Heisenberg Model on a Honeycomb Lattice: Possible Exotic Phases in Iridium Oxides A2IrO3.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Kitaev-Heisenberg Model on a Honeycomb Lattice: Possible Exotic Phases in Iridium Oxides A2IrO3

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.006198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 545d8f56-a935-439d-8419-1f85b9a7a49e · outbound

This paper cites Manni, J.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Manni, J

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.009872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 92e1e7cc-5a9f-4669-8b34-b09bf3e38961 · outbound

This paper cites an unresolved cited work.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-05-23T04:27:32.998857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 435f0729-2670-4cb2-a124-fc009b7fcf2c · outbound

This paper cites The annni model — theoretical analysis and experimental application.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows The annni model — theoretical analysis and experimental application

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.030060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8156c428-15a2-4439-8f8a-5e9770a3865d · outbound

This paper cites Unveiling phase transitions with machine learning.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Unveiling phase transitions with machine learning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.897101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc4cdf75-9798-4d85-97fb-fc929292e6e7 · outbound

This paper cites an unresolved cited work.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-05-23T04:27:32.977326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 424543d2-656a-47de-9a22-4239e956a42b · outbound

This paper cites Karrasch and D.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Karrasch and D

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.948597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 568a7879-078a-4fbe-a4bc-6cc51ad55cef · outbound

This paper cites Strongly interacting majorana modes in an array of josephson junctions.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Strongly interacting majorana modes in an array of josephson junctions

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.956048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6de7db16-c6ff-4a66-80da-efe470be3979 · outbound

This paper cites Milsted, L.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Milsted, L

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.933494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d3b4c8f1-5bd1-408d-b141-de0b627f9abe · outbound

This paper cites Unveiling phase transitions with machine learning.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Unveiling phase transitions with machine learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.952235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b17733c7-66d1-418d-9744-82ae589fc63c · outbound

This paper cites Detecting quantum phase transitions in a frustrated spin chain via transfer learning of a quantum classifier algorithm.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Detecting quantum phase transitions in a frustrated spin chain via transfer learning of a quantum classifier algorithm

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:33.078529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7060bf07-a44f-430c-b3fb-03beacfff377 · outbound

This paper cites Quantum spin liquids: a review.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Quantum spin liquids: a review

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.901048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4a234837-fd8f-46a5-a2c7-d6f3c95013aa · outbound

This paper cites Broholm, R.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Broholm, R

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.907531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7ddb24df-0b6b-40c5-a5de-1905576d511d · outbound

This paper cites Ground state and low-energy excitations of the Kitaev-Heisenberg two-leg ladder.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Ground state and low-energy excitations of the Kitaev-Heisenberg two-leg ladder

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.988113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0a5eb270-528d-4efb-93a6-67fa207e2bc1 · outbound

This paper cites Rau, Eric Kin-Ho Lee, and Hae-Young Kee.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Rau, Eric Kin-Ho Lee, and Hae-Young Kee

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.882703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4d66674e-34d8-4f3e-93ed-bc2697500903 · outbound

This paper cites Beyond Kitaev physics in strong spin-orbit coupled magnets.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Beyond Kitaev physics in strong spin-orbit coupled magnets

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.966913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3629c54d-8638-49df-a11d-9f1fd1f8ce29 · outbound

This paper cites An introduction to topological data analysis: fundamental and practical aspects for data scientists.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows An introduction to topological data analysis: fundamental and practical aspects for data scientists

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.879090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 791b05a6-8637-46c9-a282-55f5461c123d · outbound

This paper cites k- means clustering for persistent homology.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows k- means clustering for persistent homology

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.886444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7bc586dc-7728-433a-b0d8-b1f2c12b9aed · outbound

This paper cites Efficient estimation of pauli observables by derandomization.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Efficient estimation of pauli observables by derandomization

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.889944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3071606f-f950-4c74-988f-8781b026dd66 · outbound

This paper cites Integration k-means clustering method and elbow method for identification of the best customer profile cluster.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Integration k-means clustering method and elbow method for identification of the best customer profile cluster

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.995401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 60871921-71f2-4735-9fac-4c5fc1d07688 · outbound

This paper cites Em algorithms for pca and spca.

Distinguishing Ordered Phases using Machine Learning and Classical Shadows Em algorithms for pca and spca

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:27:32.867714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation efcd039e-a626-4ff8-8046-463e30d13348 · inbound

Quantum Algorithm Software for Condensed Matter Physics cites this paper.

Quantum Algorithm Software for Condensed Matter Physics Distinguishing Ordered Phases using Machine Learning and Classical Shadows

Reference 79

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:54:37.469649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.