# An All-Pair Quantum SVM Approach for Big Data Multiclass Classification

arXiv: https://arxiv.org/abs/1704.07664 · HTML: https://mlsanity.com/paper/1704.07664/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2017.

## Built on by (most cited first, 6 of 6)

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- [Investigation into the Potential of Parallel Quantum Annealing for Simultaneous Optimization of Multiple Problems: A Comprehensive Study](https://mlsanity.com/paper/2403.05764/) (2024)
- [Quantum-Assisted Machine Learning Models for Enhanced Weather Prediction](https://mlsanity.com/paper/2503.23408/) (2025)
- [Empirical Study of Observable Sets in Multiclass Quantum Classification](https://mlsanity.com/paper/2602.08485/) (2026)
- [Quantum-Secure-By-Construction (QSC): A Paradigm Shift For Post-Quantum Agentic Intelligence](https://mlsanity.com/paper/2603.15668/) (2026)

## Abstract (arXiv)

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.