Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework

TL;DR

This paper introduces AutoQML, a cloud-based framework for automating the search for optimal quantum circuit architectures, demonstrated through training a quantum GAN for energy price generation.

Contribution

It presents the first concrete description of AutoQML and develops a hybrid classical-quantum cloud architecture for hyperparameter optimization.

Findings

Successful training of a quantum GAN for energy price generation

Demonstration of parallelized hyperparameter exploration in AutoQML

Potential applications in energy economics and quantum advantage

Abstract

The learning process of classical machine learning algorithms is tuned by hyperparameters that need to be customized to best learn and generalize from an input dataset. In recent years, Quantum Machine Learning (QML) has been gaining traction as a possible application of quantum computing which may provide quantum advantage in the future. However, quantum versions of classical machine learning algorithms introduce a plethora of additional parameters and circuit variations that have their own intricacies in being tuned. In this work, we take the first steps towards Automated Quantum Machine Learning (AutoQML). We propose a concrete description of the problem, and then develop a classical-quantum hybrid cloud architecture that allows for parallelized hyperparameter exploration and model training. As an application use-case, we train a quantum Generative Adversarial neural Network (qGAN) to generate energy prices that follow a known historic data distribution. Such a QML model can be used for various applications in the energy economics sector.