# Quantum Machine Learning hyperparameter search

arXiv: https://arxiv.org/abs/2302.10298 · HTML: https://mlsanity.com/paper/2302.10298/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 71 references that are in the map.
- First posted on arXiv 20 Feb 2023.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS](https://mlsanity.com/paper/1912.06059/) (2019)
- [Quantum-enhanced machine learning](https://mlsanity.com/paper/1610.08251/) (2016)
- [Hybrid quantum ResNet for car classification and its hyperparameter optimization](https://mlsanity.com/paper/2205.04878/) (2022)
- [Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning](https://mlsanity.com/paper/1801.01596/) (2018)
- [Dynamic Pricing for Airline Ancillaries with Customer Context](https://mlsanity.com/paper/1902.02236/) (2019)
- [Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework](https://mlsanity.com/paper/2202.08024/) (2022)
- [quantum Case-Based Reasoning (qCBR)](https://mlsanity.com/paper/2104.00409/) (2021)
- [Quantum Enhanced Filter: QFilter](https://mlsanity.com/paper/2104.03418/) (2021)
- [Quantum Algorithms for solving Hard Constrained Optimisation Problems](https://mlsanity.com/paper/2202.13125/) (2022)
- [Aircraft routing and crew pairing: updated algorithms at Air France](https://mlsanity.com/paper/1706.06901/) (2017)

## Abstract (arXiv)

This paper presents a quantum-based Fourier-regression approach for machine learning hyperparameter optimization applied to a benchmark of models trained on a dataset related to a forecast problem in the airline industry. Our approach utilizes the Fourier series method to represent the hyperparameter search space, which is then optimized using quantum algorithms to find the optimal set of hyperparameters for a given machine learning model. Our study evaluates the proposed method on a benchmark of models trained to predict a forecast problem in the airline industry using a standard HyperParameter Optimizer (HPO). The results show that our approach outperforms traditional hyperparameter optimization methods in terms of accuracy and convergence speed for the given search space. Our study provides a new direction for future research in quantum-based machine learning hyperparameter optimization.