Matrix Product State-Based Quantum Classifier.

Autor: Bhatia AS; Department of Computer Science, Thapar Institute of Engineering and Technology, Patiala 147004, India amandeepbhatia.singh@gmail.com., Saggi MK; Department of Computer Science, Thapar Institute of Engineering and Technology, Patiala 147004, India mandeepsaggi90@gmail.com., Kumar A; Department of Computer Science, Thapar Institute of Engineering and Technology, Patiala 147004, India ajayloura@gmail.com., Jain S; Department of Computer Science, Thapar Institute of Engineering and Technology, Patiala 147004, India sjain@thapar.edu.
Jazyk: angličtina
Zdroj: Neural computation [Neural Comput] 2019 Jul; Vol. 31 (7), pp. 1499-1517. Date of Electronic Publication: 2019 May 21.
DOI: 10.1162/neco_a_01202
Abstrakt: Interest in 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 a well-designed class of tensor network states that plays an important role in processing quantum information. In this letter, we show that MPS, as a one-dimensional array of tensors, can be used to classify classical and quantum data. We have performed binary classification of the classical machine learning data set Iris encoded in a quantum state. We have also investigated its performance by considering different parameters on the ibmqx4 quantum computer and proved that MPS circuits can be used to attain better accuracy. Furthermore the learning ability of an MPS quantum classifier is tested to classify evapotranspiration (ET o ) for the Patiala meteorological station located in northern Punjab (India), using three years of a historical data set (Agri). We have used different performance metrics of classification to measure its capability. Finally, the results are plotted and the degree of correspondence among values of each sample is shown.
Databáze: MEDLINE
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