Quantum algorithm for neural network enhanced multi-class parallel classification

Abstract

Using the properties of quantum superposition, we propose a quantum classification algorithm to efficiently perform multi-class classification tasks, where the training data are loaded into parameterized operators which are applied to the basis of the quantum state in quantum circuit composed by sample register and label register, and the parameters of quantum gates are optimized by a hybrid quantum-classical method, which is composed of a trainable quantum circuit and a gradientbased classical optimizer. After several quantum-to-class repetitions, the quantum state is optimal that the state in sample register is the same as that in label register. For a classification task of Lclass, the analysis shows that the space and time complexity of the quantum circuit are O(L * logL) and O(logL), respectively. The numerical simulation results of 2-class task and 5-class task show that the proposed algorithm has a higher classification accuracy, faster convergence and higher expression ability. The classification accuracy and the speed of converging can also be improved by increasing the number times of applying multi-qubit controlled operators on the quantum circuit, especially for multiple classes classification.

Single-qubit quantum classifier based on gradient-free optimization algorithm

“…[9][10][11][12][13] These quantum classifiers always require expensive subroutines. Others are termed quantum circuit learning with a hybrid quantum-classical (HQC) structure [14][15][16][17][18][19][20][21] and train a shallow-depth parameterized quantum circuit with a classical optimizer.…”

“…Furthermore, Ref. [20] trained multiclass data, more than 2 classes, simultaneously for classification tasks in the qubit system. Reference [21] presented single-qubit processing units which can be seen as a singlequbit quantum classifier (SQC) and the quantum circuit was organized as a series of data re-uploads, in which a multiple data re-uploading method is used to increase circuit express ability.…”