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QNEAT: Natural Evolution of Variational Quantum Circuit Architecture

MCML Authors

Yunpu Ma

Dr.

→ Group Volker Tresp
Database Systems, Data Mining and AI

Matthias Schubert

Prof. Dr.

Principal Investigator

Spatial Artificial Intelligence

Volker Tresp

Prof. Dr.

Principal Investigator

Database Systems, Data Mining and AI

Abstract

Quantum Machine Learning (QML) is a recent and rapidly evolving field where the theoretical framework and logic of quantum mechanics is employed to solve machine learning tasks. A variety of techniques that have a different level of quantum-classical hybridization has been presented. Here we focus on variational quantum circuits (VQC), which emerged as the most promising candidates for the quantum counterpart of neural networks in the noisy intermediate-scale quantum (NISQ) era. Although showing promising results, VQCs can be hard to train because of different issues e.g. barren plateau, periodicity of the weights or choice of the architecture. In this paper we focus on this last problem and in order to address it we propose a gradient free algorithm inspired by natural evolution to optimise both the weights and the architecture of the VQC. In particular, we present a version of the well known neuroevolution of augmenting topologies (NEAT) algorithm adapted to the case of quantum variational circuits. We test the algorithm with different benchmark problems of classical fields of machine learning i.e. reinforcement learning and optimization.

inproceedings