Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs | Paper Citation Record

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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This paper cites Unsupervised Representation Learning of DNA Sequences.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unsupervised Representation Learning of DNA Sequences

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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This paper cites A., Huang, W., Barlow, T.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs A., Huang, W., Barlow, T

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs and Karra Taniskidou, E

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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This paper cites S., Sjolander, K., and Haussler, D.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs S., Sjolander, K., and Haussler, D

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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This paper cites Hidden Quantum Markov Models and non-adaptive read-out of many-body states.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Hidden Quantum Markov Models and non-adaptive read-out of many-body states

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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This paper cites Identifying DNA Sequence Motifs Using Deep Learning.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Identifying DNA Sequence Motifs Using Deep Learning

Reference 26

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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This paper cites and Andolsi, A.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs and Andolsi, A

Reference 28

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Unresolved cited work

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This paper cites Robust Iterative Learning Hidden Quantum.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Robust Iterative Learning Hidden Quantum

Reference 37

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs A Hidden Quantum

Reference 38

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Quantum Hidden

Reference 39

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs and von Keyserlingk, Curt and Lamacraft, Austen , journal=

Reference 40

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Channel-Constrained

Reference 41

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This paper cites Discover Computing , volume=.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Discover Computing , volume=

Reference 42

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This paper cites SIAM Journal on Numerical Analysis , volume =.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs SIAM Journal on Numerical Analysis , volume =

Reference 43

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Observation dc23bc4f-a7d9-4054-92ae-d3b80d027e92 · outbound

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Higham , title =

Reference 44

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Optimizing

Reference 45

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Observation d2129835-8d4b-49e6-b7c4-a768fc99b28e · outbound

This paper cites Higham , title =.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Higham , title =

Reference 46

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This paper cites WIREs Computational Molecular Science , year=.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs WIREs Computational Molecular Science , year=

Reference 47

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This paper cites Scientific Reports , volume=.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Scientific Reports , volume=

Reference 48

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Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs and others , journal=

Reference 49

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Observation a95d88c2-cb3c-4fa5-a2db-4a730fcd30df · outbound

This paper cites Briefings in Bioinformatics , volume=.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Briefings in Bioinformatics , volume=

Reference 50

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Observation ad30c47f-26cd-45e5-b7eb-91d1a75e5614 · outbound

This paper cites Proceedings of the Eighth International Workshop on Machine Learning , pages=.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Proceedings of the Eighth International Workshop on Machine Learning , pages=

Reference 51

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This paper cites Expressiveness and Learning of Hidden Quantum.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Expressiveness and Learning of Hidden Quantum

Reference 52

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Observation e2ebe5f4-c819-4f44-8f78-138d96295c02 · outbound

This paper cites Learning Hidden Quantum.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Learning Hidden Quantum

Reference 53

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Observation 051718e9-e8f0-491b-8a45-bc00cf90f5c8 · outbound

This paper cites Learning and.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Learning and

Reference 54

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Observation 8bee7821-a8b7-488c-af5f-95b3dfac3a34 · outbound

This paper cites Hidden Quantum.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Hidden Quantum

Reference 55

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Observation b298b161-2941-474e-b9cd-95d8f0bbbc2f · outbound

This paper cites and Huang, Wei and Barlow, Thomas M.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs and Huang, Wei and Barlow, Thomas M

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:33.438542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3a2331ea-b324-48e2-a95a-cb7e56559fae · outbound

This paper cites Annals of Physics , volume=.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Annals of Physics , volume=

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:33.316030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 60d09720-0861-44db-ad90-5d7ae6298fec · outbound

This paper cites 2010 Ninth International Conference on Machine Learning and Applications , pages=.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs 2010 Ninth International Conference on Machine Learning and Applications , pages=

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:33.303219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a609df3c-9f2c-492c-a6b5-96f7383da1ef · outbound

This paper cites and Spekkens, Robert W.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs and Spekkens, Robert W

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:33.107753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f1e284b9-dead-4d50-967b-eea0fdb4a013 · outbound

This paper cites , author=.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs , author=

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:32.984451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8e83bfe6-d041-4c30-8d60-405729063700 · outbound

This paper cites Contemporary Physics , volume=.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Contemporary Physics , volume=

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:32.934741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0496820d-d423-4669-8360-ab9c08394ac8 · outbound

This paper cites Quantum Machine Learning.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Quantum Machine Learning

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-10T04:23:31.351690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f40bb9e-f300-4141-b7a2-3308f292090f · outbound

This paper cites Neural Computation , volume=.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Neural Computation , volume=

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:32.742992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4918b868-d09c-4a22-889f-303ff5e31d92 · outbound

This paper cites 2017 , howpublished =.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs 2017 , howpublished =

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:32.634242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation de6f65a5-39b3-47f7-961b-b91506845f24 · outbound

This paper cites Saira and Sjolander, Kimmen and Haussler, David , journal=.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Saira and Sjolander, Kimmen and Haussler, David , journal=

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:32.612915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 098202ff-5dc4-40fc-b79b-787aa32c06c6 · outbound

This paper cites , journal=.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs , journal=

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:32.590103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dc048a13-ba9b-49d7-958f-2fa7321a9fc3 · outbound

This paper cites and Karlin, Samuel , journal=.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs and Karlin, Samuel , journal=

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:32.498904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f02fbdde-61d9-42e6-a06e-d688dc965bb2 · outbound

This paper cites Jayanth Kumar and Anand, Ashish , journal=.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Jayanth Kumar and Anand, Ashish , journal=

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:32.352005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7951594f-b57c-42b2-9e64-1e2618257186 · outbound

This paper cites Identifying.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Identifying

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:32.326767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 51db5154-a2f9-4e82-87c9-0a10d6227e66 · outbound

This paper cites The Hierarchical Hidden.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs The Hierarchical Hidden

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:32.114455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d3d412ae-288b-4308-b777-6bc1d9e835ba · outbound

This paper cites and Neal, Radford M.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs and Neal, Radford M

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:32.051335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 47bf0b8b-2876-457e-9c9c-67610d19c3c2 · outbound

This paper cites Introductory lectures on convex optimization:.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Introductory lectures on convex optimization:

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:32.024613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b50fa367-e70c-4a16-b56b-4ef06726adf5 · outbound

This paper cites 2017 , publisher=.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs 2017 , publisher=

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-10T04:23:31.498895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4224606-138f-4601-a8e7-d29d0bcc5102 · outbound

This paper cites Revisiting.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Revisiting

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-10T04:23:31.516803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37e44c2e-c878-473c-9aa5-3f770c829d51 · outbound

This paper cites Weakly Convex Optimization over.

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs Weakly Convex Optimization over

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:23:31.994590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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