Quantum Neural Network Design via Quantum Deep Reinforcement Learning

Anca-Ioana Muscalagiu

2024

Abstract

Quantum neural networks (QNNs) are a significant advancement at the intersection of quantum computing and artificial intelligence, potentially offering superior performance over classical models. However, designing optimal QNN architectures is challenging due to the necessity for deep quantum mechanics knowledge and the complexity of manual design. To address these challenges, this paper introduces a novel approach to automated QNN design using quantum deep reinforcement learning. Our method extends beyond simple quantum circuits by applying quantum reinforcement learning to design parameterized quantum circuits, integrating them into trainable QNNs. As one of the first methods to autonomously generate optimal QNN architectures using quantum reinforcement learning, we aim to evaluate these architectures on various machine learning datasets to determine their accuracy and effectiveness, moving towards more efficient quantum computing solutions.

Paper Citation

in Harvard Style

Muscalagiu A. (2024). Quantum Neural Network Design via Quantum Deep Reinforcement Learning. In Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA; ISBN 978-989-758-721-4, SciTePress, pages 560-567. DOI: 10.5220/0012997500003837

in Bibtex Style

@conference{ncta24,
author={Anca-Ioana Muscalagiu},
title={Quantum Neural Network Design via Quantum Deep Reinforcement Learning},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA},
year={2024},
pages={560-567},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012997500003837},
isbn={978-989-758-721-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA
TI - Quantum Neural Network Design via Quantum Deep Reinforcement Learning
SN - 978-989-758-721-4
AU - Muscalagiu A.
PY - 2024
SP - 560
EP - 567
DO - 10.5220/0012997500003837
PB - SciTePress