Pith Number

pith:RH4QI35S

pith:2026:RH4QI35SVIDGEOVSX6OQNNANR6

not attested not anchored not stored refs resolved

Neural Fields for NV-Center Inverse Sensing

Christine Allen-Blanchette, Nathalie P. de Leon, Tao Zhong, Yixun Hu, Zhixuan Zhao

A coordinate neural field recovers sparse spin sources from NV-center magnetic noise by coupling to a tensor dipolar forward model.

arxiv:2605.13988 v1 · 2026-05-13 · cs.LG · quant-ph

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{RH4QI35SVIDGEOVSX6OQNNANR6}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Across sparse synthetic reconstructions generated by the corrected operator, NeTMY achieves the best localization and distributional metrics in the tested benchmark.

C2weakest assumption

That the tensor power-summed dipolar operator accurately represents real NV-center physics and that the synthetic data distributions capture the challenges of experimental measurements including noise and model mismatch.

C3one line summary

NeTMY neural fields with annealed encoding, multiscale optimization, and spectrum-fidelity losses achieve superior localization and distributional accuracy in NV-center inverse sensing by using a tensor power-summed dipolar operator that exposes and mitigates center-collapse failures.

References

87 extracted · 87 resolved · 5 Pith anchors

[1] Nv center based nano-nmr enhanced by deep learning.Scientific reports, 9(1):17802, 2019 2019

[2] Blind deconvolution using convex programming.IEEE Transactions on Information Theory, 60(3):1711–1732, 2013 2013

[3] Understanding untrained deep models for inverse problems: Algorithms and theory 2025

[4] On instabilities of deep learning in image reconstruction and the potential costs of ai.Proceedings of the National Academy of Sciences, 117(48):30088–30095, 2020 2020

[5] Solving inverse problems using data-driven models.Acta numerica, 28:1–174, 2019 2019

Receipt and verification

First computed	2026-05-17T23:39:13.288386Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

89f9046fb2aa06623ab2bf9d06b40d8f8b2bda0a9fee2119827851e338bcb0dd

Aliases

arxiv: 2605.13988 · arxiv_version: 2605.13988v1 · doi: 10.48550/arxiv.2605.13988 · pith_short_12: RH4QI35SVIDG · pith_short_16: RH4QI35SVIDGEOVS · pith_short_8: RH4QI35S

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/RH4QI35SVIDGEOVSX6OQNNANR6 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 89f9046fb2aa06623ab2bf9d06b40d8f8b2bda0a9fee2119827851e338bcb0dd

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "876f446e0cbd6fa023986061380f1d1d9d616fde14bab4867058b2f33214bc2e",
    "cross_cats_sorted": [
      "quant-ph"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T18:02:34Z",
    "title_canon_sha256": "ea1d70fb2df70c8548210d07ef9ea0038a8366b82cb0ea89c8c9b3896cd0a2c1"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13988",
    "kind": "arxiv",
    "version": 1
  }
}