A weighted quantum ensemble of homogeneous quantum classifiers

2025-06-09

AI Summary

Quantum classifiers often suffer from limited expressivity, restricting both accuracy and robustness. Method: We propose the first learnable-weight, hardware-encoded weighted homogeneous quantum ensemble classifier framework. It leverages an index register to encode multiple subsampled datasets in quantum superposition, instantiating homogeneous base classifiers in parallel; learns base classifier weights via classical optimization (e.g., gradient descent or Bayesian optimization); and compiles these weights directly into controlled-unitary operations embedded within the inference circuit. Contribution/Results: Our approach unifies quantum-parallel subsampling with classical–quantum co-optimization, breaking the conventional single-model paradigm. Experiments on benchmark datasets demonstrate significant improvements in classification accuracy and noise robustness, validating the effectiveness of achieving ensemble diversity and high predictive performance under constrained quantum resources.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Achieving weighted homogeneous quantum ensemble using quantum classifiers
• Leveraging superposition for diverse internal classifiers execution
• Integrating quantum-parallel execution with classical weight optimization

Innovation

• Weighted quantum ensemble with indexing registers
• Quantum-parallel execution via superposition
• Classical weight optimization for circuit encoding