A weighted quantum ensemble of homogeneous quantum classifiers
This work addresses the challenge of enhancing ensemble methods in quantum computing for researchers in quantum machine learning, representing an incremental advancement by adapting classical weighted ensemble techniques to a quantum framework.
The paper tackles the problem of improving prediction accuracy in quantum machine learning by proposing a weighted homogeneous quantum ensemble method that uses quantum classifiers with indexing registers for data encoding, achieving effective performance as demonstrated in empirical evaluations.
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.