# Optimizing quantum heuristics with meta-learning

## TL;DR

This paper demonstrates that meta-learning techniques outperform traditional optimization methods in tuning variational quantum algorithms, especially under noisy conditions, indicating their potential for near-term quantum computing applications.

## Contribution

The study shows that meta-learning approaches are more effective and noise-resilient than existing methods for optimizing quantum heuristics in noisy environments.

## Key findings

- Meta-learning nearly reaches global optima more often than other optimizers.
- Meta-learning exhibits greater resistance to noise, maintaining performance in noisy and sampling environments.
- Meta-learning outperforms L-BFGS-B on average, as measured by the 'gain' metric.

## Abstract

Variational quantum algorithms, a class of quantum heuristics, are promising candidates for the demonstration of useful quantum computation. Finding the best way to amplify the performance of these methods on hardware is an important task. Here, we evaluate the optimization of quantum heuristics with an existing class of techniques called `meta-learners'. We compare the performance of a meta-learner to Bayesian optimization, evolutionary strategies, L-BFGS-B and Nelder-Mead approaches, for two quantum heuristics (quantum alternating operator ansatz and variational quantum eigensolver), on three problems, in three simulation environments. We show that the meta-learner comes near to the global optima more frequently than all other optimizers we tested in a noisy parameter setting environment. We also find that the meta-learner is generally more resistant to noise, for example seeing a smaller reduction in performance in Noisy and Sampling environments and performs better on average by a `gain' metric than its closest comparable competitor L-BFGS-B. These results are an important indication that meta-learning and associated machine learning methods will be integral to the useful application of noisy near-term quantum computers.