ORCID: https://orcid.org/0000-0001-5727-9151; Rohe, Tobias; Nappi, Francesco; Hager, Julian; Bucher, David; Zorn, Maximilian
ORCID: https://orcid.org/0009-0006-2750-7495; Kölle, Michael und Linnhoff-Popien, Claudia
ORCID: https://orcid.org/0000-0001-6284-9286
(2024):
Introducing Reduced-Width QNNs, an AI-Inspired Ansatz Design Pattern.
ICAART 2024: 16th International Conference on Agents and Artificial Intelligence, Rome, Italy, 24. - 26. Februar 2024.
Rocha, Ana Paula; Steels, Luc und Herik, Jaap van den (eds.) :
In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence,
Vol. 3
Setúbal: SciTePress. pp. 1127-1134
Abstract
Variational Quantum Algorithms are one of the most promising candidates to yield the first industrially relevant quantum advantage. Being capable of arbitrary function approximation, they are often referred to as Quantum Neural Networks (QNNs) when being used in analog settings as classical Artificial Neural Networks (ANNs). Similar to the early stages of classical machine learning, known schemes for efficient architectures of these networks are scarce. Exploring beyond existing design patterns, we propose a reduced-width circuit ansatz design, which is motivated by recent results gained in the analysis of dropout regularization in QNNs. More precisely, this exploits the insight, that the gates of overparameterized QNNs can be pruned substantially until their expressibility decreases. The results of our case study show, that the proposed design pattern can significantly reduce training time while maintaining the same result quality as the standard "full-width" design in the presence of noise. We thus argue, that quantum architecture search should not blindly follow the classical overparameterization trend.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Faculties: | Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning |
| Subjects: | 000 Computer science, information and general works > 004 Data processing computer science |
| ISBN: | 978-989-758-680-4 |
| Place of Publication: | Setúbal |
| Language: | English |
| Item ID: | 128878 |
| Date Deposited: | 11. Nov 2025 14:47 |
| Last Modified: | 11. Nov 2025 14:47 |
