Readout-Side Bypass for Residual Hybrid Quantum-Classical Models

TL;DR

This paper introduces a residual hybrid quantum-classical architecture that bypasses the measurement bottleneck in quantum machine learning, improving accuracy and privacy in resource-constrained, federated settings.

Contribution

It proposes a novel residual hybrid model that concatenates quantum features with raw inputs, enhancing performance without increasing quantum complexity.

Findings

Up to +55% accuracy over quantum baselines

Outperforms pure quantum and prior hybrid models

Retains low communication cost and improves privacy robustness

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.