Weight Re-mapping for Variational Quantum Algorithms

Kölle, Michael; Giovagnoli, Alessandro; Stein, Jonas ; Mansky, Maximilian; Hager, Julian; Rohe, Tobias; Müller, Robert und Linnhoff-Popien, Claudia (2024): Weight Re-mapping for Variational Quantum Algorithms. ICAART 2023: 15th International Conference on Agents and Artificial Intelligence, Lisbon, Portugal, 22. - 24. Februar 2023. Rocha, Ana Paula; Steels, Luc und Herik, Jaap van den (Hrsg.): In: Agents and Artificial Intelligence : 15th International Conference, ICAART 2023 Lisbon, Portugal, February 22–24, 2023 Revised Selected Papers, Cham: Springer. S. 286-309

Abstract

Inspired by the remarkable success of artificial neural networks across a broad spectrum of AI tasks, variational quantum circuits (VQCs) have recently seen an upsurge in quantum machine learning applications. The promising outcomes shown by VQCs, such as improved generalization and reduced parameter training requirements, are attributed to the robust algorithmic capabilities of quantum computing. However, the current gradient-based training approaches for VQCs do not adequately accommodate the fact that trainable parameters (or weights) are typically used as angles in rotational gates. To address this, we extend the concept of weight re-mapping for VQCs, as introduced by Kölle et al. 9. This approach unambiguously maps the weights to an interval of length 2π, mirroring data rescaling techniques in conventional machine learning that have proven to be highly beneficial in numerous scenarios. In our study, we employ seven distinct weight re-mapping functions to assess their impact on eight classification datasets, using variational classifiers as a representative example. Our results indicate that weight re-mapping can enhance the convergence speed of the VQC. We assess the efficacy of various re-mapping functions across all datasets and measure their influence on the VQC’s average performance. Our findings indicate that weight re-mapping not only consistently accelerates the convergence of VQCs, regardless of the specific re-mapping function employed, but also significantly increases accuracy in certain cases.

• Dokumententyp: Konferenzbeitrag (Paper)
• Keywords: Variational quantum circuits ; Variational classifier ; Weight re-mapping
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 978-3-031-55326-4
• Ort: Cham