# Quantum Inspired Adaptive Boosting

arXiv: https://arxiv.org/abs/2102.00949 · HTML: https://mlsanity.com/paper/2102.00949/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [Towards Quantum Machine Learning with Tensor Networks](https://mlsanity.com/paper/1803.11537/) (2018)
- [A quantum-inspired classical algorithm for recommendation systems](https://mlsanity.com/paper/1807.04271/) (2018)
- [Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning](https://mlsanity.com/paper/1910.06151/) (2019)
- [Quantum algorithms for feedforward neural networks](https://mlsanity.com/paper/1812.03089/) (2018)
- [Quantum ensembles of quantum classifiers](https://mlsanity.com/paper/1704.02146/) (2017)
- [Supervised Quantum Learning without Measurements](https://mlsanity.com/paper/1612.05535/) (2016)
- [Quantum classification of the MNIST dataset with Slow Feature Analysis](https://mlsanity.com/paper/1805.08837/) (2018)

## Abstract (arXiv)

Building on the quantum ensemble based classifier algorithm of Schuld and Petruccione [arXiv:1704.02146v1], we devise equivalent classical algorithms which show that this quantum ensemble method does not have advantage over classical algorithms. Essentially, we simplify their algorithm until it is intuitive to come up with an equivalent classical version. One of the classical algorithms is extremely simple and runs in constant time for each input to be classified. We further develop the idea and, as the main contribution of the paper, we propose methods inspired by combining the quantum ensemble method with adaptive boosting. The algorithms were tested and found to be comparable to the AdaBoost algorithm on publicly available data sets.