Research map / Learning theory and algorithms / Sample complexity
Quantum Boosting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use quantum boosting instead of classical AdaBoost | Weak quantum PAC learner for a Boolean concept class · Strong PAC learning | time complexity: \widetilde{O}\Bigg(\sqrt{\VC({\mathcal C})} \cdot Q({\mathcal C})^{3/2} \cdot \frac{n^2}{\gamma^{11}}\Bigg); classical c | Helped |
| Estimate weighted errors using quantum techniques and use modified distribution updates that tolerate approximate estimates | AdaBoost-style quantum boosting · Training-error reduction on a training set S | time complexity: \widetilde{O}(n^2 \sqrt{M}\cdot Q({\mathcal C})^{3/2}); training error at most 1/10 | Helped |
| Use classical AdaBoost to boost a weak quantum learner | Weak quantum PAC learner · Strong PAC learning | time complexity: \widetilde{O}\Bigg(\frac{\VC({\mathcal C})}{\eta^2}\cdot Q({\mathcal C}) \cdot \frac{n^2}{\gamma^4}\Bigg) | Mixed |
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