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Limitations

This project is built to be honest about its scope. The headline negative result is real, but it is a result about a specific setting, not a general claim.

What the benchmark does not claim

  • It does not show quantum machine learning fails. It shows that, at N=200 on a statevector simulator with these feature maps, the quantum models do not beat chance on this task. That is a statement about scale and setup, not about quantum methods in general.
  • It is not a clinical study. The models are trained and evaluated on competition or synthetic data, with no external validation, calibration, or fairness analysis. Nothing here is fit for clinical use.

Data

The numbers on this site were produced on the synthetic fallback, not the real WiDS data, because the public site is built without Kaggle credentials. The synthetic generator is schema-matched and realistic, but it is a model of the data, not the data. Running on the real dataset can shift the absolute numbers, though the classical-versus-quantum gap is expected to persist.

Scale and simulation

  • The fidelity quantum kernel is O(N squared), which forces a small training subsample (N=200) and a small feature count (6 qubits). Larger N might change the picture, but it is not feasible on a CPU simulator.
  • All quantum models run on an exact statevector simulator with no shot noise and no hardware effects. Real devices would add noise, not remove the scaling problem.
  • The quantum models are evaluated with bootstrap confidence intervals only, not cross-validation, because refitting an O(N squared) kernel per fold is prohibitive. The classical models do get 5-fold cross-validation, so the two families are not measured identically. The bootstrap intervals are wide enough that this asymmetry does not change the conclusion.

The live demo

The live demo is a simplified model, not the benchmark model:

  • It is a logistic regression on six interpretable raw inputs, retrained specifically so the coefficients map to labeled sliders.
  • One of those inputs is the APACHE-IVa baseline risk score, an existing clinical predictor, so the demo scores well for reasons that have nothing to do with quantum computing.
  • Its held-out ROC-AUC is reported in the widget itself. Treat it as an illustration of client-side inference, not as a validated risk calculator.

Where the result could change

Liu, Arunachalam, and Temme (2021) prove there exist learning problems where quantum kernels offer a provable advantage and are hard to simulate classically. ICU mortality on tabular features is not known to be such a problem. The value of this repository is the audited, reproducible pipeline: the feature maps, fidelity kernel, PSD enforcement, and Qiskit primitives transfer directly to a setting where a quantum advantage is plausible.