20.500.12556/RUL-171597 A matrix product state model for simultaneous classification and generation Quantum machine learning (QML) is a rapidly expanding field that merges the principles of quantum computing with the techniques of machine learning. One of the powerful mathematical frameworks in this domain is tensor networks. These networks are used to approximate high-order tensors by contracting tensors with lower ranks. Initially developed for simulating quantum systems, tensor networks have become integral to quantum computing and, by extension, to QML. Drawing inspiration from these quantum methods, specifically the Matrix Product States (MPS), we apply them in a classical machine learning setting. Their ability to efficiently represent and manipulate complex, high-dimensional data makes them effective in a supervised learning framework. Here, we present an MPS model, in which the MPS functions as both a classifier and a generator. The dual functionality of this novel MPS model permits a strategy that enhances the traditional training of supervised MPS models. This framework is inspired by generative adversarial networks and is geared towards generating more realistic samples by reducing outliers. In addition, our contributions offer insights into the mechanics of tensor network methods for generation tasks. Specifically, we discuss alternative embedding functions and a new sampling method from non-normalized MPSs. tensor networks MPS GAN noise robustness outlier reduction quantum embeddings tenzorske mreže matrično produktni nastavki generativni modeli robustnost na šum kvantne vložitve zmanjšanje večjih odstopanj true false true Angleški jezik Slovenski jezik Članek v reviji 2025-08-28 14:17:12 2025-08-28 14:17:14 2025-09-08 03:44:21 0000-00-00 00:00:00 2025 0 0 18 str. art.. ǂ48 Vol. 7 Jun. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.85 2524-4914 10.1007/s42484-025-00272-6 232768771 RAZ_Mossi_Alex_2025.pdf RAZ_Mossi_Alex_2025.pdf 1 B18B00BC6907D1945804D1A4DDFC72C4 d7b2ff5409f313f8a178d10fe3a7e70c24e74805cb4699a579391a17ab587e1c 6f1840bd-840c-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=214976 https://link.springer.com/article/10.1007/s42484-025-00272-6 1 ce6e2ff2-8408-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=214975 Fakulteta za računalništvo in informatiko 0 0 0