A Matrix Product State Model for Simultaneous Classification and Generation

TL;DR

This paper introduces a Matrix Product State (MPS) model that functions as both a classifier and generator in classical machine learning, inspired by quantum tensor network methods, to improve data representation and sample generation.

Contribution

The paper presents a novel dual-purpose MPS model for classification and generation, incorporating strategies inspired by GANs and new sampling methods for enhanced performance.

Findings

Effective in representing complex data

Improved sample realism and reduced outliers

Insights into tensor network generation mechanisms

Abstract

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.