Research map / Learning theory and algorithms / Combinatorial and search optimization
A Hybrid Quantum-Classical Algorithm for Robust Fitting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Run until the maximum iterations instead of stopping at the first consensus set (ablation) | Hybrid quantum-classical robust fitting · Fundamental matrix estimation | consensus size | Mixed |
| Run until the maximum iterations instead of stopping at the first consensus set (ablation) | Hybrid quantum-classical robust fitting · Multi-view triangulation | consensus size | Mixed |
| Decay the penalty instead of keeping it fixed (ablation) | Hybrid quantum-classical robust fitting · Fundamental matrix estimation | error bound | Helped |
| Use a large fixed penalty (ablation) | Hybrid quantum-classical robust fitting · Fundamental matrix estimation | error bound | Hurt |
| Use a small fixed penalty (ablation) | Hybrid quantum-classical robust fitting · Fundamental matrix estimation | iterations to find a consensus set | Hurt |
| Replace quantum annealing with simulated annealing for the QUBO subroutine (ablation) | Hybrid quantum-classical robust fitting · Synthetic 1D linear regression, N = 20 and outlier ratio 0.2 | solution similarity | No clear effect |
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