# Quantum Boosting

@article{Arunachalam2020QuantumB, title={Quantum Boosting}, author={Srinivasan Arunachalam and Reevu Maity}, journal={ArXiv}, year={2020}, volume={abs/2002.05056} }

Suppose we have a weak learning algorithm $\mathcal{A}$ for a Boolean-valued problem: $\mathcal{A}$ produces hypotheses whose bias $\gamma$ is small, only slightly better than random guessing (this could, for instance, be due to implementing $\mathcal{A}$ on a noisy device), can we boost the performance of $\mathcal{A}$ so that $\mathcal{A}$'s output is correct on $2/3$ of the inputs? Boosting is a technique that converts a weak and inaccurate machine learning algorithm into a strong accurate… CONTINUE READING

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