Quantum Inf Process (2016) 15:4049–4069
Quantum computation for large-scale image
· Hanwu Chen
· Jianing Tan
Received: 28 January 2016 / Accepted: 11 July 2016 / Published online: 25 July 2016
© Springer Science+Business Media New York 2016
Abstract Due to the lack of an effective quantum feature extraction method, there
is currently no effective way to perform quantum image classiﬁcation or recognition.
In this paper, for the ﬁrst time, a global quantum feature extraction method based on
Schmidt decomposition is proposed. A revised quantum learning algorithm is also pro-
posed that will classify images by computing the Hamming distance of these features.
From the experimental results derived from the benchmark database Caltech 101, and
an analysis of the algorithm, an effective approach to large-scale image classiﬁcation
is derived and proposed against the background of big data.
Keywords Quantum image · Quantum learning · Feature extraction · Image
classiﬁcation · Schmidt decomposition · Hamming distance
In the context of big data, how to make full use of images, the maximum quantity of
unstructured big data in the internet, is a problem worthy of thorough consideration.
At present, tagging images (i.e., classifying them automatically into different classes)
Yue R u a n
School of Computer Science and Engineering, Southeast University, Nanjing, China
School of Computer Science and Technology, Anhui University of Technology, Maanshan, China
Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry
of Education, Nanjing, China