A matrix product state model for simultaneous classification and generation

Mossi, Alex (avtor), Žunkovič, Bojan (avtor), Flouris, Kyriakos (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: tensor networks, MPS, GAN, noise robustness, outlier reduction, quantum embeddings
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: 18 str.
• Številčenje: Vol. 7, art.. ǂ48
• PID: 20.500.12556/RUL-171597
• UDK: 004.85
• ISSN pri članku: 2524-4914
• DOI: 10.1007/s42484-025-00272-6
• COBISS.SI-ID: 232768771
• Datum objave v RUL: 28.08.2025

Gradivo je del revije

• Naslov: Quantum machine intelligence
• Skrajšan naslov: Quantum Mach. Intell.
• Založnik: Springer Nature
• ISSN: 2524-4914
• COBISS.SI-ID: 80757763

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: tenzorske mreže, matrično produktni nastavki, generativni modeli, robustnost na šum, kvantne vložitve, zmanjšanje večjih odstopanj

Projekti

• Financer: Drugi - Drug financer ali več financerjev
• Program financ.: 2022-531

• Financer: Drugi - Drug financer ali več financerjev
• Številka projekta: 2022-643