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An efficient hybrid quantum machine learning framework for Alzheimer classification
Tajinder Kumar, Neha Chachra, Purushottam Sharma, Vikram Verma, Chaman Verma, Zoltan llles. Scientific Reports, 2026-08-05
Topic: Amyloid, tau and blood-test diagnosis
Effect size analysis proves the fact that the hybrid model produces statistically significant performance, as well as cases of clinically meaningful improvements over lightweight CNN baselines. Similar to the state-of-the-art deep networks with significantly reduced trainable parameters in the classification stage. These findings show that hybrid QML is a data-efficient and powerful substitute in the diagnosis of Alzheimer’s.
Key findings
- Effect size analysis proves the fact that the hybrid model produces statistically significant performance, as well as cases of clinically meaningful improvements over lightweight CNN baselines.
- Similar to the state-of-the-art deep networks with significantly reduced trainable parameters in the classification stage.
- These findings show that hybrid QML is a data-efficient and powerful substitute in the diagnosis of Alzheimer’s.
Why it matters
This hybrid quantum machine learning framework showed statistically significant performance and clinically meaningful improvements over lightweight CNN baselines for Alzheimer's classification. It is a diagnostic approach and does not report any treatment, cure, or prevention.
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Study details
- Evidence strengthLow
How this study scored 31/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 30 of the 96 points we could judge it on, which is 31 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Scientific Reports) |
| Journal's citation record | 3 / 4 | Scientific Reports: journal h-index 463 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 0 / 6 | Cited 0.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | OpenAlex's profile for last-listed author Zoltan llles holds only 1 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 0 / 3 | Eötvös Loránd University: institution h-index 518 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 2 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 30 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 30 of the 96 points we could judge it on is 31 out of 100. |
| Total | 31 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Kumar, T., Chachra, N., Sharma, P., Verma, V., Verma, C., & llles, Z. (2026). An efficient hybrid quantum machine learning framework for Alzheimer classification. Scientific Reports. https://doi.org/10.1038/s41598-026-64291-4
Vancouver
Kumar T, Chachra N, Sharma P, Verma V, Verma C, llles Z. An efficient hybrid quantum machine learning framework for Alzheimer classification. Scientific Reports. 2026. doi:10.1038/s41598-026-64291-4.
BibTeX
@article{kumar2026-7b816ce9,
author = {Kumar, Tajinder and Chachra, Neha and Sharma, Purushottam and Verma, Vikram and Verma, Chaman and llles, Zoltan},
title = {{An efficient hybrid quantum machine learning framework for Alzheimer classification}},
journal = {Scientific Reports},
year = {2026},
doi = {10.1038/s41598-026-64291-4},
url = {https://doi.org/10.1038/s41598-026-64291-4}
}