Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning to generate high-dimensional distributions with low-dimensional quantum Boltzmann machines
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.16363.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T19:45:07.065174Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T15:25:48.687119Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 7a8bbf80-00c8-4039-bbb6-038755c21e11 · inbound
How quantum computing can enhance biomarker discovery cites this paper.
How quantum computing can enhance biomarker discovery Learning to generate high-dimensional distributions with low-dimensional quantum Boltzmann machines
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:45:07.065174Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ed02734c-f57f-464f-9bbc-2aed69894a9f · inbound
Non-unitary extension of Grover's search algorithm cites this paper.
Non-unitary extension of Grover's search algorithm Learning to generate high-dimensional distributions with low-dimensional quantum Boltzmann machines
Reference 80
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:36:12.026229Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ac3fe0a6-25ac-4ee2-a3b5-7a4ff3b4a797 · inbound
Quantum Fourier Generative Models Trainable at Large Scale cites this paper.
Quantum Fourier Generative Models Trainable at Large Scale Learning to generate high-dimensional distributions with low-dimensional quantum Boltzmann machines
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T15:25:48.689336Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.