Research map / Learning theory and algorithms / Prediction and decision losses
Quantum Multi-Model Fitting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use iterative decomposition with simulated annealing instead of the classical RanSaCov solver | Multi-model fitting · AdelaideRMF multi-model sequences | misclassification error (%) mean: 9.79 -> 0.77 | Helped |
| Use iterative decomposition with quantum annealing instead of simulated annealing | DeQuMF · AdelaideRMF multi-model sequences | misclassification error (%) mean: 0.77 -> 16.22 | Hurt |
| Use iterative decomposition with simulated annealing instead of RanSaCov | Multi-model fitting · Hopkins Traffic3 | misclassification error (%) mean: 0.35 -> 0.55 | Mixed |
| Use iterative decomposition with simulated annealing instead of RanSaCov | Multi-model fitting · Hopkins Traffic2 | misclassification error (%) mean: 0.54 -> 0.10 | Mixed |
| Use iterative decomposition with simulated annealing instead of RanSaCov | Multi-model fitting · York Urban Line Segment Database | misclassification error (%) mean: 0.19 -> 0.74 | Hurt |
| Apply DeQuMF to single-model fitting instead of Hqc-RF | Single-model fitting · AdelaideRMF, 10% outliers | misclassification error (%): 3.71 -> 2.41 | Helped |
| Apply DeQuMF (SA) to single-model fitting instead of Hqc-RF | Single-model fitting · AdelaideRMF, 20% outliers | misclassification error (%): 37.0 -> 8.28 | Helped |
| Apply DeQuMF (SA) to single-model fitting instead of Hqc-RF | Single-model fitting · AdelaideRMF, full sequences | misclassification error (%): 45.84 -> 10.83 | Helped |
| Use iterative decomposition for large preference matrices | DeQuMF (SA) · Star5, up to 1000 models | misclassification error (%): nearly zero misclassification error | Helped |
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