Quantum Support Vector Machine Based on Gradient Descent

Abstract Support vector machine is a supervised machine learning algorithm, which is usually solved by iterative method. Quantum algorithms have significant advantages over classical algorithms in terms of speed and capacity. In this work, a quantum support vector machine algorithm based on gradient...
Ausführliche Beschreibung

Abstract Support vector machine is a supervised machine learning algorithm, which is usually solved by iterative method. Quantum algorithms have significant advantages over classical algorithms in terms of speed and capacity. In this work, a quantum support vector machine algorithm based on gradient descent is proposed. Firstly, in the training stage, quantum gradient descent was used to obtain the training parameters, which reduces the time complexity of a single iteration from the classical polynomial level to the logarithmic level. Then, in the classification stage, we built a quantum classifier that can classify multiple samples at the same time, thereby improving the efficiency of classification. Finally, the simulation experiments proved the classification ability of our quantum support vector machine. Ausführliche Beschreibung