arXiv:2505. 22533v2 Announce Type: replace-cross Abstract: In this paper, we introduce a novel quantum generative model for synthesizing tabular data.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2607. 09113v1 Announce Type: cross Abstract: Data scarcity and class imbalance are persistent challenges in machine learning that degrade model generalization and introduce predictive bias.
arXiv:2508. 19857v3 Announce Type: replace Abstract: Many successful families of generative models leverage a low-dimensional latent distribution that is mapped to a data distribution.
arXiv:2606. 18970v1 Announce Type: cross Abstract: Medical image classification is often constrained by limited labeled data, motivating generative augmentation; recently, quantum generative models have been proposed for this purpose, frequently reporting accuracy gains.
arXiv:2606. 02785v1 Announce Type: new Abstract: Large machine learning models benefit substantially from multimodal inputs that provide a complementary view of the same example.
Medical image classification is often constrained by limited labeled data, motivating generative augmentation; recently, quantum generative models have been proposed for this purpose, frequently reporting accuracy gains. However, such claims are typically based on single training runs, do not match the parameter budgets of the quantum and classical generators, and do not characterize the data regime in which any benefit appears.
arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.