An All-Pair Quantum SVM Approach for Big Data Multiclass Classification
This addresses the computational bottleneck in big data classification for researchers and practitioners, offering a potential breakthrough in efficiency.
The paper tackles the multiclass classification problem for big data by proposing a quantum all-pair SVM approach, achieving exponential speed-up with logarithmic runtime complexity compared to classical polynomial methods.
In this paper, we have discussed a quantum approach for the all-pair multiclass classification problem. We have shown that the multiclass support vector machine for big data classification with a quantum all-pair approach can be implemented in logarithm runtime complexity on a quantum computer. In an all-pair approach, there is one binary classification problem for each pair of classes, and so there are k (k-1)/2 classifiers for a k-class problem. As compared to the classical multiclass support vector machine that can be implemented with polynomial run time complexity, our approach exhibits exponential speed up in the quantum version. The quantum all-pair algorithm can be used with other classification algorithms, and a speed up gain can be achieved as compared to their classical counterparts.