Quantum ensemble of trained classifiers

Quantum ensembles of quantum classifiers

Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, aft…

On quantum ensembles of quantum classifiers

Quantum machine learning seeks to exploit the underlying nature of a quantum computer to enhance machine learning techniques. A particular framework uses the quantum property of superposition to store sets of parameters, thereby creating an ensemble of quantum classifiers that may be computed in parallel. The idea stems from classical ensemble methods where one attempts to build a stronger model by averaging the results from many different models. In this work, we demonstrate that a specific implementation of the quantum ensemble of quantum classifiers, called the accuracy-weighted quantum ens…

The Quantum Version of Prediction for Binary Classification Problem by Ensemble Methods

In this work, we consider the performance of using a quantum algorithm to predict a result for a binary classification problem if a machine learning model is an ensemble from any simple classifiers. Such an approach is faster than classical prediction and uses quantum and classical computing, but it is based on a probabilistic algorithm. Let $N$ be a number of classifiers from an ensemble model and $O(T)$ be the running time of prediction on one classifier. In classical case, an ensemble model gets answers from each classifier and "averages" the result. The running time in classical case is $O…

同构量子分类器的加权量子集成

A weighted quantum ensemble of homogeneous quantum classifiers

提出一种加权同构量子集成方法，利用带索引寄存器的量子分类器，通过叠加态和受控酉操作实现数据子采样与量子并行执行，并结合经典权重优化，有效提升了预测性能。

量子分类器集成

Ensembles of Quantum Classifiers

针对NISQ时代量子分类的数据编码与噪声挑战，提出混合量子分类器集成方法，结合三种经典集成方法与三种量子分类器，在减少量子比特需求的同时提升了准确率与稳健性。

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。