Matrix Product State Based Quantum Classifier

TL;DR

This paper demonstrates that matrix product state (MPS) tensor networks can be effectively used as quantum classifiers for both classical and quantum data, achieving improved accuracy on real datasets using quantum computing.

Contribution

It introduces the application of MPS tensor networks as quantum classifiers for classical and quantum data, with experimental validation on IBM quantum hardware.

Findings

MPS circuits can classify classical datasets with high accuracy.

MPS-based quantum classifiers perform well on real quantum hardware.

The approach effectively classifies environmental data using quantum machine learning.

Abstract

In recent years, interest in expressing the success of neural networks to the quantum computing has increased significantly. Tensor network theory has become increasingly popular and widely used to simulate strongly entangled correlated systems. Matrix product state (MPS) is the well-designed class of tensor network states, which plays an important role in processing of quantum information. In this paper, we have shown that matrix product state as one-dimensional array of tensors can be used to classify classical and quantum data. We have performed binary classification of classical machine learning dataset Iris encoded in a quantum state. Further, we have investigated the performance by considering different parameters on the ibmqx4 quantum computer and proved that MPS circuits can be used to attain better accuracy. Further, the learning ability of MPS quantum classifier is tested to classify evapotranspiration ($ET_{o}$) for Patiala meteorological station located in Northern Punjab (India), using three years of historical dataset (Agri). Furthermore, we have used different performance metrics of classification to measure its capability. Finally, the results are plotted and degree of correspondence among values of each sample is shown.