Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T08:32:05.907305Z
Paper Citation Record · LEDGER
As of 6 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 1 inbound Pith citation observation for arXiv:2601.16734.
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 2026-08-03T08:32:05.907305Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-31T09:32:57.164803Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T02:28:24.338817Z
79 of 79 outbound references displayed
External citation measurements
0
pith, observed 2026-08-05T02:28:24.338817Z
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library Schollwöck, The density-matrix renormalization group in the age of matrix product states, Annals of Physics 326 (1) (2011) 96–192.doi:10.1016/j
Reference 3
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library Verstraete, D
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library Image compression and entanglement
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library Gourianov, M
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library García-Molina, L
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library Zalka, Simulating quantum systems on a quantum computer, Proc
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library Creating superpositions that correspond to efficiently integrable probability distributions
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library Multiscale interpolative construction of quantized tensor trains
Reference 25
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library Chebyshev approximation and composition of functions in matrix product states for quantum-inspired numerical analysis
Reference 26
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library García-Molina, J
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library Dolgov, D
Reference 32
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library Núñez Fernández, M
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library Schollwöck, The density-matrix renormalization group, Rev
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library Dukelsky, M
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library Anomaly Detection with Tensor Networks
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library Improvements to Gradient Descent Methods for Quantum Tensor Network Machine Learning
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library An approximate Fourier transform useful in quantum factoring
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SeeMPS: A Python-based Matrix Product State and Tensor Train Library Driscoll, N
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