Training Variational Quantum Circuits through Genetic Algorithms

Recently, Variational Quantum Circuits (VQCs) are attracting considerable attention among quantum algorithms thanks to their robustness to the noise characterizing the current quantum devices. In detail, VQCs involve parameterized quantum circuits to be trained by means of a classical optimizer that makes queries to the quantum device. VQCs play a key role in several applications including quantum classifiers where the Hilbert space is used as feature space. Currently, the most used classical optimizer to learn VQCs is the gradient descent method. However, the so-called barren plateaus issue causes gradients of cost functions to become exceedingly small as the dimension of the classification problem is increased. As consequence, gradient descent method could be not efficient in real-world classification problems. This paper proposes to apply Genetic Algorithms (GAs) to train VQCs used as quantum classifiers. As shown in the experiments, the application of GAs results in accurate solutions obtained with a reduced number of queries to quantum devices.

Automated summary

Variational Quantum Circuits (VQCs) are gaining attention for their robustness to noise in quantum devices. However, the widely used gradient descent method becomes inefficient in high-dimensional classification problems. This paper proposes using Genetic Algorithms (GAs) to train quantum classifiers, resulting in accurate solutions with reduced queries to quantum devices.