Introducing Reduced-Width QNNs, an AI-Inspired Ansatz Design Pattern

Stein, Jonas ; Rohe, Tobias; Nappi, Francesco; Hager, Julian; Bucher, David; Zorn, Maximilian ; Kölle, Michael und Linnhoff-Popien, Claudia (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 (Hrsg.): In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence, Bd. 3 Setúbal: SciTePress. S. 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.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Informatik > Künstliche Intelligenz und Maschinelles Lernen
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 978-989-758-680-4
• Ort: Setúbal
• Sprache: Englisch