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Readout-Side Bypass for Residual Hybrid Quantum-Classical Models

Guilin Zhang, Wulan Guo, Ziqi Tan, Hongyang He, Qiang Guan, Hailong Jiang

TL;DR

The paper tackles the quantum readout bottleneck that hampers quantum machine learning and privacy in distributed settings. It introduces a readout-side residual hybrid architecture that concatenates raw inputs with quantum features before the classifier, preserving information without adding quantum depth. Empirical results show near-classical accuracy with fewer parameters and improved privacy robustness in centralized and federated scenarios, illustrating practical benefits for privacy-sensitive, resource-constrained applications. The approach is protocol-agnostic, easily pluggable into existing hybrid systems, and offers a near-term pathway to integrating quantum models in edge and federated environments.

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.

Readout-Side Bypass for Residual Hybrid Quantum-Classical Models

TL;DR

The paper tackles the quantum readout bottleneck that hampers quantum machine learning and privacy in distributed settings. It introduces a readout-side residual hybrid architecture that concatenates raw inputs with quantum features before the classifier, preserving information without adding quantum depth. Empirical results show near-classical accuracy with fewer parameters and improved privacy robustness in centralized and federated scenarios, illustrating practical benefits for privacy-sensitive, resource-constrained applications. The approach is protocol-agnostic, easily pluggable into existing hybrid systems, and offers a near-term pathway to integrating quantum models in edge and federated environments.

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.

Paper Structure

This paper contains 15 sections, 5 equations, 1 figure, 6 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Quantum Data Encoding
  4. Variational Quantum Circuit (PQC)
  5. Measurement and Feature Construction
  6. Projection and Classification
  7. Experiments
  8. Experimental Setup
  9. Baselines and Metrics
  10. Results and Analysis
  11. Residual Hybrids Restore Accuracy with Compact Size
  12. Privacy Robustness
  13. Federated Performance and Communication Efficiency
  14. Ablation Study
  15. Conclusion

Figures (1)

  • Figure 1: Model comparison. (a) Pure QML: quantum-only readout; (b) Hybrid QML: classical MLP on $Q(x)$; (c) Ours: residual hybrid with $[x \| Q(x)]$ bypassing the measurement bottleneck.