A weighted quantum ensemble of homogeneous quantum classifiers

TL;DR

This paper introduces a quantum ensemble method that combines multiple homogeneous quantum classifiers with learned weights, leveraging quantum superposition and parallelism to improve prediction accuracy.

Contribution

It presents a novel quantum ensemble approach using indexing registers and superposition for data encoding, enabling efficient weighted homogeneous quantum classifiers.

Findings

Effective quantum ensemble achieved through superposition and controlled unitaries

Quantum classifiers with learned weights improve prediction accuracy

Empirical results demonstrate the method's potential benefits

Abstract

Ensemble methods in machine learning aim to improve prediction accuracy by combining multiple models. This is achieved by ensuring diversity among predictors to capture different data aspects. Homogeneous ensembles use identical models, achieving diversity through different data subsets, and weighted-average ensembles assign higher influence to more accurate models through a weight learning procedure. We propose a method to achieve a weighted homogeneous quantum ensemble using quantum classifiers with indexing registers for data encoding. This approach leverages instance-based quantum classifiers, enabling feature and training point subsampling through superposition and controlled unitaries, and allowing for a quantum-parallel execution of diverse internal classifiers with different data compositions in superposition. The method integrates a learning process involving circuit execution and classical weight optimization, for a trained ensemble execution with weights encoded in the circuit at test-time. Empirical evaluation demonstrate the effectiveness of the proposed method, offering insights into its performance.