Quantum One-class Classification With a Distance-based Classifier

A Grover-search Based Quantum Learning Scheme for Classification

The hybrid quantum-classical learning scheme provides a prominent way to achieve quantum advantages on near-term quantum devices. A concrete example towards this goal is the quantum neural network (QNN), which has been developed to accomplish various supervised learning tasks such as classification and regression. However, there are two central issues that remain obscure when QNN is exploited to accomplish classification tasks. First, a quantum classifier that can well balance the computational cost such as the number of measurements and the learning performance is unexplored. Second, it is un…

QUILT：利用多样化量子分类器集成在量子计算机上实现有效的多类分类

QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers

本文提出 Quilt 框架，通过多样化量子分类器集成，在当前噪声较大、量子比特有限的硬件上完成多类分类，并在五量子比特系统的 MNIST 任务中达到最高 85% 准确率。

可训练的量子神经网络用于多类图像分类：借助预训练的树张量网络的力量

Trainable Quantum Neural Network for Multiclass Image Classification with the Power of Pre-trained Tree Tensor Networks

本文提出基于森林张量网络的分类器，通过量子神经网络实现多类图像分类，提升性能并保持预训练模型效果。

一种混合变分量子电路方法用于稳定子状态分类器

A hybrid variational quantum circuit approach for stabilizer states classifiers

本文提出利用变分量子电路方法对多体量子纠缠进行分类，针对四量子比特状态构建了分类器。

Implementing a distance-based classifier with a quantum interference circuit

Lately, much attention has been given to quantum algorithms that solve pattern recognition tasks in machine learning. Many of these quantum machine learning algorithms try to implement classical models on large-scale universal quantum computers that have access to non-trivial subroutines such as Hamiltonian simulation, amplitude amplification and phase estimation. We approach the problem from the opposite direction and analyse a distance-based classifier that is realised by a simple quantum interference circuit. After state preparation, the circuit only consists of a Hadamard gate as well as t…

Quantum-inspired classification based on quantum state discrimination

We present quantum-inspired algorithms for classification tasks inspired by the problem of quantum state discrimination. By construction, these algorithms can perform multiclass classification, prevent overfitting, and generate probability outputs. While they could be implemented on a quantum computer, we focus here on classical implementations of such algorithms. The training of these classifiers involves Semi-Definite Programming. We also present a relaxation of these classifiers that utilizes Linear Programming (but that can no longer be interpreted as a quantum measurement). Additionally, …