Quantum machine learning & fair health-AI comparisons
Does quantum AI always beat regular AI?
A fancy name, a kidney-disease study, and a better way to ask who won.
Imagine two apps enter a race. One says “AI.” The other says “QUANTUM AI” in letters that seem to need their own soundtrack. Which one would you trust with a health question? In this imagined scene, I would ask to see the test—not award a medal for the label.
The short answer is no: quantum does not automatically mean better. A 2024 preprint [a publicly shared research draft] compared quantum and regular learning models on small test problems. The regular models did better overall in those experiments. That is not proof that quantum methods can never help. It is a good reason to ask what was tested.[1]
First, what does “quantum AI” mean?
Here, we mean quantum machine learning [using quantum methods in systems that learn from data]. Ordinary learning models run on regular computers. Quantum approaches bring quantum calculations into the work. Adding them does not, by itself, prove better answers or faster results.[2]
Think of choosing a tool for a job. A shiny new drill may be great, but it is not the tool I would pick to butter toast. That is an analogy, not a physics lesson. The useful question is “What job does this tool do well?”
Our research spotlight: a real comparison
Research spotlight · Coauthored work
Performance Comparison of Quantum and Classical Machine Learning Models for Chronic Kidney Disease Prediction
In this 2025 paper I coauthored, our team compared two quantum learning models with two regular models. We used a public kidney-disease dataset. It started with 400 patient records; after removing one unusual record, the study used 399. The task was to sort records into existing “CKD” or “not CKD” labels—not predict when a healthy person would become ill.[3][4]
CKD means chronic kidney disease. The paper selected eight input features [pieces of information used by a model]. It used 80% of the records for training [learning patterns] and 20% for testing. The quantum methods ran in simulators [software that imitates quantum calculations] on a regular laptop, not on a physical quantum computer.[3]
The twist? One quantum model had the highest reported accuracy [share of predictions marked correct]. But both quantum models took longer to train than the regular ones in this setup. The other quantum model had the lowest reported accuracy of the four. The word “quantum” alone did not pick the winner.[3]
My take: the useful contribution is putting the methods side by side and measuring more than one kind of result. It makes the trade-offs visible. It does not settle which method will work best for new patients, new data, or different hardware. This was a dataset comparison, not a clinical trial.[3]
Coauthors: Parama Sridevi, Paramita Basak Upama, Masud Rabbani, and Sheikh Iqbal Ahamed. All list UbiComp Lab, Computer Science Department, Marquette University. If this comparison supports your own research, read and cite the original paper—not this explainer.[3]
View citation & all coauthors
Parama Sridevi, Paramita Basak Upama, Masud Rabbani, Sheikh Iqbal Ahamed. “Performance Comparison of Quantum and Classical Machine Learning Models for Chronic Kidney Disease Prediction”. 2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC), 788–793. (2025). https://doi.org/10.1109/COMPSAC65507.2025.00105.
If this work supports your research, cite the original paper rather than this blog explanation.
A better finish line
My suggested checklist: ask “Better at what?” Then look for prediction quality, time taken, and the test setup. Check whether the quantum part used hardware or a simulator. Also ask whether both models had a fair chance to be adjusted before testing; the benchmark study shows that those settings can matter.[1][2]
Quick check: would you give it the trophy?
A quick thought experiment
An imaginary headline says: “Quantum model wins one small test.” Does that prove it will beat regular AI on every health problem? Choose before revealing the answer.
Next question to explore: can AI learn together while the data stays in different places?
Sources & further reading
- Better than classical? The subtle art of benchmarking quantum machine learning modelsJoseph Bowles; Shahnawaz Ahmed; Maria Schuld · 2024 (arXiv v2 preprint, March 14)
- Introduction to Quantum Machine LearningIBM Quantum · Undated course; accessed October 1, 2026
- Performance Comparison of Quantum and Classical Machine Learning Models for Chronic Kidney Disease PredictionParama Sridevi; Paramita Basak Upama; Masud Rabbani; Sheikh Iqbal Ahamed · 2025 — IEEE COMPSAC, pp. 788–793
- Chronic Kidney Disease [Dataset]L. Rubini; P. Soundarapandian; P. Eswaran — UCI Machine Learning Repository · 2015
Disclaimer: This post is for education, not medical or other professional advice; information may contain errors or become outdated, no outcome is guaranteed, views are my own, and third-party materials remain subject to their owners' rights.