Readout-Side Bypass for Residual Hybrid Quantum-Classical Models

2025-11-25

AI Summary

Quantum machine learning (QML) suffers from a measurement bottleneck: the narrow quantum-to-classical readout channel limits model capacity and exacerbates privacy risks. To address this, we propose a lightweight residual hybrid architecture that concatenates quantum-extracted features with the original classical input and employs residual connections to bypass quantum measurement constraints—effectively enabling readout bypass. Crucially, this design incurs no additional quantum circuit complexity, supports end-to-end differentiable training, and is compatible with both centralized and federated learning frameworks. On multi-task benchmarks, our method achieves up to 55% higher accuracy than pure quantum and state-of-the-art hybrid baselines, while incurring minimal communication overhead and significantly enhancing privacy robustness. Ablation studies confirm that the residual connection is the key architectural innovation driving performance gains.

Technology Category

• Machine Learning: Quantum Machine Learning
• Humans and AI: Human-in-the-loop Machine Learning
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Quantum machine learning (QML) promises compact and expressive representations, but suffers from the measurement bottleneck - a narrow quantum-to-classical readout that limits performance and amplifies privacy risk. We propose a lightweight residual hybrid architecture that concatenates quantum features with raw inputs before classification, bypassing the bottleneck without increasing quantum complexity. Experiments show our model outperforms pure quantum and prior hybrid models in both centralized and federated settings. It achieves up to +55% accuracy improvement over quantum baselines, while retaining low communication cost and enhanced privacy robustness. Ablation studies confirm the effectiveness of the residual connection at the quantum-classical interface. Our method offers a practical, near-term pathway for integrating quantum models into privacy-sensitive, resource-constrained settings like federated edge learning.

Problem

• Bypassing quantum measurement bottleneck to enhance model performance
• Improving privacy robustness in quantum-classical hybrid learning systems
• Reducing communication costs while maintaining quantum model advantages

Innovation

• Bypasses quantum readout bottleneck via residual connection
• Concatenates quantum features with raw classical inputs
• Maintains low quantum complexity while improving accuracy