5 papers
Towards Scaling Quantum Fine-Tuning of Foundational Time Series Models for Classification
Sang Hyub Kim, Julien Baglio, Rajiv Krishnakumar +7
Time-series foundation models produce rich embeddings, but whether quantum models can exploit them, and how far hybrid classical-quantum architectures scale, remains unclear. We ad…
Latent Style-based Quantum Wasserstein GAN for Drug Design
Julien Baglio, Yacine Haddad, Richard Polifka
The development of new drugs is a tedious, time-consuming, and expensive process, for which the average costs are estimated to be up to around $2.5 billion. The first step in this…
Exponential capacity scaling of classical GANs compared to hybrid latent style-based quantum GANs
Milan Liepelt, Julien Baglio
Quantum generative modeling is a very active area of research in looking for practical advantage in data analysis. Quantum generative adversarial networks (QGANs) are leading candi…
Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification
Rajiv Krishnakumar, Julien Baglio, Frederik F. Flöther +3
Whole-slide image classification represents a key challenge in computational pathology and medicine. Attention-based multiple instance learning (MIL) has emerged as an effective ap…
Cross-platform hardware benchmark of style-based quantum GANs for data augmentation on superconducting and trapped-ion processors
Julien Baglio
In the noisy intermediate-scale quantum era, controlled benchmarks of quantum machine-learning workloads across hardware modalities are needed to quantify how given algorithms beha…