Quantum Transfer Learning: The Impact of Classical Preprocessing

Kölle, Michael; Maurer, Jonas; Altmann, Philipp ; Sünkel, Leo; Stein, Jonas ; Hager, Julian; Zielinski, Sebastian und Linnhoff-Popien, Claudia (April 2025): Quantum Transfer Learning: The Impact of Classical Preprocessing. ICAART 2024: 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: Agents and Artificial Intelligence : 16th International Conference, ICAART 2024, Bd. 2 Cham: Springer Nature Switzerland. S. 368-403

Abstract

Quantum computing promises performance advantages, especially for data-intensive and complex computations. However, the current limitations of quantum hardware impose significant constraints on input sizes. To mitigate this, hybrid transfer learning solutions have been developed, which combine pre-trained classical models capable of managing large inputs with variational quantum circuits. Despite these advancements, the individual contributions of the classical and quantum components to the model's overall performance remain unclear. We propose a novel hybrid architecture that, instead of using a pre-trained network for data compression, employs an autoencoder to generate a compressed version of the input data. This compressed data is then passed to the quantum component. To evaluate our model's classification performance, we compare it against two state-of-the-art hybrid transfer learning architectures, two purely classical architectures, and one quantum architecture. Additionally, we check our compression method with the autoencoder against a Principal Component Analysis (PCA) and Gaussian Mixture Model (GMM). Finally we train our model with another technique where the autoencoder and variational quantum circuit are trained in parallel. The accuracy of each model is tested on four datasets: Banknote Authentication, Breast Cancer Wisconsin, MNIST digits, and AudioMNIST. Our findings indicate that the classical components play a significant role in classification within hybrid transfer learning models, a contribution that is often incorrectly attributed to the quantum component. The performance of our proposed model is comparable to that of a variational quantum circuit utilizing amplitude embedding, and also to a variational quantum circuit which uses compressed data through a PCA or a GMM, suggesting that our approach is a viable alternative.

• Dokumententyp: Konferenzbeitrag (Paper)
• Keywords: Quantum transfer learning ; Variational quantum circuits ; Autoencoder ; Dimensionality reduction
• Fakultät: Mathematik, Informatik und Statistik > Informatik > Künstliche Intelligenz und Maschinelles Lernen
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 978-3-031-87327-0
• Ort: Cham
• Sprache: Englisch