Quantum Support Vector Machine for Classifying Noisy Data

doi:10.1109/TC.2024.3416619

Quantum Support Vector Machine for Classifying Noisy Data

2024-09-01

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DOI：10.1109/TC.2024.3416619

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Noisy data is ubiquitous in quantum computer, greatly affecting the performance of various algorithms. However, existing quantum support vector machine models are not equipped with anti-noise ability, and often deliver low performance when learning accurate hyperplane normal vectors from noisy data. To attack this issue, an anti-noise quantum support vector machine algorithm is developed in this paper. Specifically, a weight factor is first embedded into the hinge loss, so as to construct the objective function of anti-noise support vector machine. And then, an alternative iterative optimization strategy and a quantum circuit are designed for solving the objective function, aiming to obtain the normal vector and intercept of the hyperplane that finally divides the data. Finally, the classification and anti-noise effect of the algorithm are verified on artificial dataset and public dataset. Experimental results show that the proposed algorithm is efficient, yet maintains stable accuracy in noisy data.

Keywords:

Support vector machines

Quantum computing

Classification algorithms

Accuracy

Quantum entanglement

Computers

Vectors

Quantum support vector machine

quantum computer

noisy data

classification

Quantum Support Vector Machine for Classifying Noisy Data

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