Paper Citation Record · LEDGER

Towards Quantum Machine Learning for Malicious Code Analysis

As of 17 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 2 inbound Pith citation observations for arXiv:2508.19381.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2508.19381 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:51:29.735938Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-25T07:38:56.051810Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-25T07:40:28.827763Z

Reference resolution

33 of 33 outbound references displayed

verified exact3
verified fuzzy25
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7c1b955c-1321-4bee-9d52-907a90db16e8 · outbound

This paper cites Diffusion-inspired quantum noise mitigation in parameterized quantum circuits,.

Towards Quantum Machine Learning for Malicious Code Analysis Diffusion-inspired quantum noise mitigation in parameterized quantum circuits,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.182532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f42c20d0-c8d1-47df-b14c-41b10ffcf653 · outbound

This paper cites Challenges and opportunities in quantum machine learning,.

Towards Quantum Machine Learning for Malicious Code Analysis Challenges and opportunities in quantum machine learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.173798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 187a6a81-5140-4a23-815b-2fb5cb3b655b · outbound

This paper cites Quantum machine learning,.

Towards Quantum Machine Learning for Malicious Code Analysis Quantum machine learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.164835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a2069ea5-27d1-4ef6-a3d7-9805c87fa783 · outbound

This paper cites The quest for a quantum neural network,.

Towards Quantum Machine Learning for Malicious Code Analysis The quest for a quantum neural network,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.156254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c00942d6-3dfe-4456-a857-7e842e9c877a · outbound

This paper cites The power of quantum neural networks,.

Towards Quantum Machine Learning for Malicious Code Analysis The power of quantum neural networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.147616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1098f2bf-8e58-433a-878d-2bdfe5e86730 · outbound

This paper cites Quantum computing in the nisq era and beyond,.

Towards Quantum Machine Learning for Malicious Code Analysis Quantum computing in the nisq era and beyond,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.138599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 21ce99be-7718-4553-829f-81df16d41fba · outbound

This paper cites Quantum convolutional neural network for classical data classification.

Towards Quantum Machine Learning for Malicious Code Analysis Quantum convolutional neural network for classical data classification

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T15:51:29.658117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1530eb4-6758-49fa-8a35-2cccb08e8f74 · outbound

This paper cites QuantumNAS: Noise-adaptive search for robust quantum circuits,.

Towards Quantum Machine Learning for Malicious Code Analysis QuantumNAS: Noise-adaptive search for robust quantum circuits,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.129639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1adc78f6-53a8-41f2-9542-ec26c62a0654 · outbound

This paper cites QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization.

Towards Quantum Machine Learning for Malicious Code Analysis QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:51:29.941120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 06be2372-1559-41ff-8f8a-f9cb93344ab0 · outbound

This paper cites An evaluation of hardware-efficient quantum neural networks,.

Towards Quantum Machine Learning for Malicious Code Analysis An evaluation of hardware-efficient quantum neural networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.120968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 145a56f4-59a7-48af-b212-827258d3808f · outbound

This paper cites PennyLane: Automatic differentiation of hybrid quantum-classical computations.

Towards Quantum Machine Learning for Malicious Code Analysis PennyLane: Automatic differentiation of hybrid quantum-classical computations

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T15:51:29.670648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f82a984c-8693-4b07-870b-b9ad5e850651 · outbound

This paper cites TensorFlow Quantum: A Software Framework for Quantum Machine Learning.

Towards Quantum Machine Learning for Malicious Code Analysis TensorFlow Quantum: A Software Framework for Quantum Machine Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T15:51:29.674099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b2e6bb0-7e0e-41dc-8471-f9d7a4df8714 · outbound

This paper cites QMLP: An error-tolerant nonlinear quantum mlp architecture using parameterized two-qubit gates,.

Towards Quantum Machine Learning for Malicious Code Analysis QMLP: An error-tolerant nonlinear quantum mlp architecture using parameterized two-qubit gates,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.112048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e7f21e88-eeae-46f8-86fe-4778e9afd3d6 · outbound

This paper cites Quantum machine learning for chemistry and physics,.

Towards Quantum Machine Learning for Malicious Code Analysis Quantum machine learning for chemistry and physics,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.102981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 251353e9-3fe0-4341-bac3-abd095502cea · outbound

This paper cites Accelerated discovery of efficient solar cell materials using quantum and machine-learning methods,.

Towards Quantum Machine Learning for Malicious Code Analysis Accelerated discovery of efficient solar cell materials using quantum and machine-learning methods,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.094069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1343e435-6a82-4252-9729-bb7e5509ae4c · outbound

This paper cites Quantum convolutional neural networks,.

Towards Quantum Machine Learning for Malicious Code Analysis Quantum convolutional neural networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.085240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8cfb7877-e26a-4203-a3b8-173033eac3d1 · outbound

This paper cites EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models.

Towards Quantum Machine Learning for Malicious Code Analysis EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T15:51:29.689009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fe65e27-5076-4aee-b9af-f5f3c0aa4f80 · outbound

This paper cites Enhancing state-of-the-art classifiers with api semantics to detect evolved android malware,.

Towards Quantum Machine Learning for Malicious Code Analysis Enhancing state-of-the-art classifiers with api semantics to detect evolved android malware,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.076270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b999f492-8349-4d11-b92c-40cc4072c3ea · outbound

This paper cites Drebin: Effective and explainable detection of android malware in your pocket,.

Towards Quantum Machine Learning for Malicious Code Analysis Drebin: Effective and explainable detection of android malware in your pocket,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.067049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b136112a-2bff-4a0d-ab21-5b08b9972095 · outbound

This paper cites On the limitations of continual learning for malware classification,.

Towards Quantum Machine Learning for Malicious Code Analysis On the limitations of continual learning for malware classification,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.058170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 04ed17d1-f311-4b85-8a51-c9ae5505e9b8 · outbound

This paper cites MalCL: Lever- aging gan-based generative replay to combat catastrophic forgetting in malware classification,.

Towards Quantum Machine Learning for Malicious Code Analysis MalCL: Lever- aging gan-based generative replay to combat catastrophic forgetting in malware classification,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.048777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ac7c548d-ffef-4923-a54e-32a9634632db · outbound

This paper cites MADAR: Efficient continual learning for malware analysis with diversity-aware replay,.

Towards Quantum Machine Learning for Malicious Code Analysis MADAR: Efficient continual learning for malware analysis with diversity-aware replay,

Reference 22

Resolution: verified exact; raw_fallback, observed 2026-08-05T15:51:29.902675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9a37bf57-bb27-48d9-a5dd-78a3ff7bec0d · outbound

This paper cites A hybrid quantum-classical neural network architecture for binary classification,.

Towards Quantum Machine Learning for Malicious Code Analysis A hybrid quantum-classical neural network architecture for binary classification,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.039142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 76a107db-3954-4754-9626-acc08f6fbb51 · outbound

This paper cites Benchmarking adversarially robust quantum machine learning at scale,.

Towards Quantum Machine Learning for Malicious Code Analysis Benchmarking adversarially robust quantum machine learning at scale,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.029551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8b8e9273-5463-492b-b32d-522794897c05 · outbound

This paper cites Qucnn : A quantum convolu- tional neural network with entanglement based backpropagation,.

Towards Quantum Machine Learning for Malicious Code Analysis Qucnn : A quantum convolu- tional neural network with entanglement based backpropagation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.019397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 41e06e0f-b7f5-4244-b712-27cae3766aa7 · outbound

This paper cites AndroZoo: Collecting Millions of Android Apps for the Research Community,.

Towards Quantum Machine Learning for Malicious Code Analysis AndroZoo: Collecting Millions of Android Apps for the Research Community,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:30.008774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 43b60a96-f7ba-491d-bd42-b3693125724e · outbound

This paper cites X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation.

Towards Quantum Machine Learning for Malicious Code Analysis X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation

Reference 27

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T15:51:29.780633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b1c4d9c1-9648-4e14-854c-496207bf8d58 · outbound

This paper cites Realization of a quantum neural network using repeat-until- success circuits in a superconducting quantum processor,.

Towards Quantum Machine Learning for Malicious Code Analysis Realization of a quantum neural network using repeat-until- success circuits in a superconducting quantum processor,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:29.998671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4ce36a2b-e541-4963-abae-21d62f67e345 · outbound

This paper cites A co-design framework of neural networks and quantum circuits towards quantum advantage,.

Towards Quantum Machine Learning for Malicious Code Analysis A co-design framework of neural networks and quantum circuits towards quantum advantage,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:29.988771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d969ebba-fc3c-47f8-8837-22097ee0dce1 · outbound

This paper cites A lie algebraic theory of barren plateaus for deep parameterized quantum circuits,.

Towards Quantum Machine Learning for Malicious Code Analysis A lie algebraic theory of barren plateaus for deep parameterized quantum circuits,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:29.979579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 560fe483-8a46-496a-a0da-6fc327169081 · outbound

This paper cites Absence of barren plateaus in quantum convolutional neural networks,.

Towards Quantum Machine Learning for Malicious Code Analysis Absence of barren plateaus in quantum convolutional neural networks,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:29.969532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5f9dfe08-a8f5-4819-9126-884cf5f8997d · outbound

This paper cites Towards explainable quantum machine learning for mobile malware detection and classification,.

Towards Quantum Machine Learning for Malicious Code Analysis Towards explainable quantum machine learning for mobile malware detection and classification,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:51:29.960035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1a1c224d-8f1b-4388-883b-10a0354a8b07 · outbound

This paper cites Towards an in-depth detection of malware using distributed QCNN.

Towards Quantum Machine Learning for Malicious Code Analysis Towards an in-depth detection of malware using distributed QCNN

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:51:29.767471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 8335b7f2-77db-4a47-a92c-66015cbb8938 · inbound

SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness cites this paper.

SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness Towards Quantum Machine Learning for Malicious Code Analysis

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-21T18:44:18.925004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3bd5184d-4c7b-43d2-8e3d-beb93bddca36 · inbound

SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness cites this paper.

SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness Towards Quantum Machine Learning for Malicious Code Analysis

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-25T07:40:28.830967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.