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QuantumNeuroXAI: a quantum-inspired deep learning framework with explainability for brain signal analysis and neurological disorder detection.

Code ↔ Paper

5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › Algorithmic representation ↔ QuantumNeuroXAI/scripts/06_generate_explanations.py, lines 17–96 · score 0.72 · explanatory report, feature attributions, attention weights, bands, QuantumNeuroXAI, sensitivity
  2. [2] § Experimental results › Ablation study and component analysis ↔ QuantumNeuroXAI/src/models/quantum_neuro_xai.py, the whole file · a weak match · score 0.58 · quantum classical fusion, temporal CNN, adaptive, layer, weighting, modules
  3. [3] § Materials and methods › Training strategy and experimental protocol ↔ QuantumNeuroXAI/src/training/losses.py, lines 6–11 · score 0.54 · cross entropy loss, binary, class, Training
  4. [4] § Materials and methods › Quantum-inspired feature encoding module ↔ QuantumNeuroXAI/src/quantum/quantum_block.py, lines 8–29 · score 0.54 · amplitude encoded, phase encoding, map, module, inspired, Quantum
  5. [5] § Materials and methods › Explainability and interpretability design ↔ QuantumNeuroXAI/scripts/06_generate_explanations.py, lines 17–96 · score 0.52 · feature attribution, attention weights, explanation, fused, prediction, Model

Paper

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The authors' code

Python · 99 lines · 3.8 KB · MIT · 2 matches

  1. from __future__ import annotations
  2. import argparse
  3. import os
  4. import numpy as np
  5. import matplotlib.pyplot as plt
  6. import torch
  7. from src.utils.seed import set_global_seed
  8. from src.utils.device import get_device
  9. from src.utils.io import load_yaml
  10. from src.models.quantum_neuro_xai import QuantumNeuroXAI
  11. from src.explainability.signal_xai import input_saliency, integrated_gradients_signal, channel_band_summary
  12. from src.explainability.model_xai import extract_attention_weights, fused_feature_attribution
  13. from src.explainability.quantum_xai import quantum_sensitivity_analysis, rank_quantum_dimensions
  14. from src.explainability.report_builder import build_unified_report, save_explanation_report
  15. from scripts.common import build_loaders, infer_num_classes
  17. def main():
  18. parser = argparse.ArgumentParser()
  19. parser.add_argument("--checkpoint", required=True)
  20. parser.add_argument("--dataset", required=True, choices=["tuh", "chbmit", "bci2a"])
  21. parser.add_argument("--config", required=True)
  22. parser.add_argument("--dataset-config", required=True)
  23. parser.add_argument("--model-config", required=True)
  24. parser.add_argument("--xai-config", required=True)
  25. args = parser.parse_args()
  27. gcfg = load_yaml(args.config)
  28. dcfg = load_yaml(args.dataset_config)
  29. mcfg = load_yaml(args.model_config)
  30. xcfg = load_yaml(args.xai_config)
  32. set_global_seed(gcfg["seed"])
  33. device = get_device(gcfg["device"])
  34. _, _, test_loader = build_loaders(dcfg["processed_manifest_csv"], batch_size=1, num_workers=0)
  36. model = QuantumNeuroXAI(mcfg, task_mode=dcfg["task"]["mode"], num_classes=infer_num_classes(dcfg))
  37. first_batch = next(iter(test_loader))
  38. _ = model(first_batch["x"].to(device))
  39. ckpt = torch.load(args.checkpoint, map_location=device)
  40. model.load_state_dict(ckpt["model_state"], strict=False)
  41. model.to(device)
  42. model.eval()
  44. os.makedirs(gcfg["xai_dir"], exist_ok=True)
  46. for idx, batch in enumerate(test_loader):
  47. if idx >= xcfg["num_samples"]:
  48. break
  49. x = batch["x"].to(device)
  50. y = batch["y"].cpu().numpy().tolist()
  51. out = model(x)
  53. sal = input_saliency(model, x)
  54. sig_summary = channel_band_summary(sal)
  56. attn = extract_attention_weights(out)
  57. fused_attr = fused_feature_attribution(out)
  58. q_scores = quantum_sensitivity_analysis(model, x, eps=xcfg["quantum_eps"])
  59. q_rank = rank_quantum_dimensions(q_scores)
  61. report = build_unified_report(
  62. signal_summary=sig_summary,
  63. attention=attn,
  64. fused_attr=fused_attr,
  65. quantum_rank=q_rank,
  66. prediction={"target": y, "probs": out["probs"].detach().cpu().numpy().tolist()},
  67. )
  68. save_explanation_report(report, os.path.join(gcfg["xai_dir"], f"sample_{idx:03d}.json"))
  70. sal_mean = sal.mean(axis=(0, 1))
  71. plt.figure(figsize=(6, 4))
  72. plt.imshow(sal_mean.mean(axis=0), aspect="auto")
  73. plt.title(f"Signal Saliency #{idx}")
  74. plt.xlabel("Time")
  75. plt.ylabel("Frequency")
  76. plt.tight_layout()
  77. plt.savefig(os.path.join(gcfg["xai_dir"], f"sample_{idx:03d}_saliency.png"), dpi=300)
  78. plt.close()
  80. if attn is not None:
  81. plt.figure(figsize=(6, 3))
  82. plt.plot(attn[0])
  83. plt.title(f"Attention Weights #{idx}")
  84. plt.tight_layout()
  85. plt.savefig(os.path.join(gcfg["xai_dir"], f"sample_{idx:03d}_attention.png"), dpi=300)
  86. plt.close()
  88. if q_scores.size > 0:
  89. plt.figure(figsize=(6, 3))
  90. plt.bar(np.arange(len(q_scores)), q_scores)
  91. plt.title(f"Quantum Sensitivity #{idx}")
  92. plt.tight_layout()
  93. plt.savefig(os.path.join(gcfg["xai_dir"], f"sample_{idx:03d}_quantum.png"), dpi=300)
  94. plt.close()
  96. print(f"Saved explanations to {gcfg['xai_dir']}")
  98. if __name__ == "__main__":
  99. main()

06_generate_explanations.py at commit a8c776b, under MIT · at the source

Overview

Authors: T Gayathri1, G Manjula2, Harish H Kenchannavar3, Danthuluri Sudha4, Santosh Kumar Jankatti4, Ramandeep Kaur4, Bondu Venkateswarlu1
ORCID iDs: Santosh Kumar Jankatti, Ramandeep Kaur
  1. Department of Computer Science and Engineering, Dayananda Sagar University, Bangalore, Karnataka India
  2. Department of Computer Science and Design, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka India
  3. Department of Computer Science and Engineering (Data Science), Dayananda Sagar College of Engineering, Bangalore, Karnataka India
  4. Department of Computer Science and Technology, Dayananda Sagar University, Bangalore, Karnataka India
Institutions: Dayananda Sagar University (India); Dayananda Sagar Academy of Technology and Management (India); Dayananda Sagar College of Engineering (India)
Journal: Scientific reports, volume 16, issue 1, article 16863
Dates: received 10 January 2026; accepted 1 April 2026; published online 10 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-47627-y · PMID 41963527 · PMCID PMC13230557 · OpenAlex W7153188653
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Graphs, Statistics, Machine learning, Physiology & signal measures
Keywords: Electroencephalography, Quantum-inspired learning, Deep neural networks, Explainable artificial intelligence, Neurological disorder detection, Computational biology and bioinformatics, Mathematics and computing, Neuroscience
MeSH: Brain*, Deep Learning*, Electroencephalography*, Nervous System Diseases*, Signal Processing, Computer-Assisted*, Humans, Neural Networks, Computer, Quantum Theory (* major topic)
Topic: Quantum Computing Algorithms and Architecture (Artificial Intelligence, Computer Science), according to OpenAlex
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

venkateshwarlu-bondu/QuantumNeuroXAI

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: a8c776b5420840598737922475535ca315ff4f29, 24 March 2026
Languages: Python (54), Jupyter (5)
Size: 69 files, 59 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (requirements.txt), 5 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: PyTorch (22 files), NumPy (17 files), pandas (5 files), SciPy (4 files), scikit-learn (3 files), Matplotlib (2 files), MNE-Python (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
61 files
At the source: github.com/venkateshwarlu-bondu/QuantumNeuroXAI

Zenodo 19200650

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the text, “Implementation details”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (22 files), NumPy (17 files), pandas (5 files), SciPy (4 files), scikit-learn (3 files), Matplotlib (2 files), MNE-Python (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
61 files
At the source:

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Read it in the paper: doi.org/10.1038/s41598-026-47627-y.

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  • 118 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Availability statements

The paper has a data availability statement and a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing them here; in short, from what the harvester recognized in them:

Read them in the paper: doi.org/10.1038/s41598-026-47627-y.

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 8 keywords, 8 MeSH terms, 60 references.

Cite

This paper

Gayathri, T., Manjula, G., Kenchannavar, H. H., Sudha, D., Jankatti, S. K., Kaur, R., & Venkateswarlu, B. (2026). QuantumNeuroXAI: a quantum-inspired deep learning framework with explainability for brain signal analysis and neurological disorder detection. Scientific reports, 16(1), 16863. https://doi.org/10.1038/s41598-026-47627-y

BibTeX

@article{gayathri2026quantumneuroxai,
author = {Gayathri, T and Manjula, G and Kenchannavar, Harish H and Sudha, Danthuluri and Jankatti, Santosh Kumar and Kaur, Ramandeep and Venkateswarlu, Bondu},
title = {{QuantumNeuroXAI: a quantum-inspired deep learning framework with explainability for brain signal analysis and neurological disorder detection}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {16863},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-47627-y},
url = {https://doi.org/10.1038/s41598-026-47627-y},
pmid = {41963527},
pmcid = {PMC13230557}
}

RIS

TY - JOUR
AU - Gayathri, T
AU - Manjula, G
AU - Kenchannavar, Harish H
AU - Sudha, Danthuluri
AU - Jankatti, Santosh Kumar
AU - Kaur, Ramandeep
AU - Venkateswarlu, Bondu
TI - QuantumNeuroXAI: a quantum-inspired deep learning framework with explainability for brain signal analysis and neurological disorder detection
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/10
VL - 16
IS - 1
SP - 16863
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-47627-y
UR - https://doi.org/10.1038/s41598-026-47627-y
LA - en
ER -

CSL-JSON

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"container-title": "Scientific reports",
"author": [
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"given": "T"
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"date-parts": [
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10
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]
}
}

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