Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Improvements to Gradient Descent Methods for Quantum Tensor Network Machine Learning
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2203.03366.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T14:05:27.465805Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T06:29:00.612017Z
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 711a675b-918e-4fc1-988d-d9c86698e3e2 · inbound
Efficient Finite Initialization with Partial Norms for Tensorized Neural Networks and Tensor Networks Algorithms cites this paper.
Efficient Finite Initialization with Partial Norms for Tensorized Neural Networks and Tensor Networks Algorithms Improvements to Gradient Descent Methods for Quantum Tensor Network Machine Learning
Reference 32
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T06:29:00.616153Z
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 c81b837f-e7d9-43f4-95a7-9f40448a20c9 · inbound
Bayesian perspectives for quantum states and application to ab initio quantum chemistry cites this paper.
Bayesian perspectives for quantum states and application to ab initio quantum chemistry Improvements to Gradient Descent Methods for Quantum Tensor Network Machine Learning
Reference 105
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:05:27.465805Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2c5db24d-bb53-472b-8680-5ebd402632bb · inbound
SeeMPS: A Python-based Matrix Product State and Tensor Train Library cites this paper.
SeeMPS: A Python-based Matrix Product State and Tensor Train Library Improvements to Gradient Descent Methods for Quantum Tensor Network Machine Learning
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T08:32:01.337423Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.