[Paper] Readout-Side Bypass for Residual Hybrid Quantum-Classical Models

Source: arXiv - 2511.20922v1

Overview

The paper introduces a residual hybrid quantum‑classical architecture that sidesteps the notorious “measurement bottleneck” in quantum machine learning (QML). By feeding raw input data directly to the classical classifier alongside quantum‑generated features, the authors achieve markedly higher accuracy without adding quantum depth—making QML more viable for real‑world, privacy‑sensitive applications such as federated edge learning.

Key Contributions

• Readout‑Side Bypass (RSB) design: A lightweight residual connection that concatenates the original input vector with quantum feature embeddings before the final classification layer.
• Performance boost: Demonstrates up to +55 % accuracy over pure quantum baselines and improves upon existing hybrid QML models in both centralized and federated scenarios.
• Communication‑efficient federated learning: Maintains low uplink/downlink traffic because the quantum component remains shallow, while the residual path carries no extra quantum data.
• Privacy robustness: The residual shortcut reduces the amount of information that must be inferred from quantum measurements, mitigating leakage risks inherent to the measurement bottleneck.
• Comprehensive ablation study: Validates that the performance gains stem from the readout‑outside bypass rather than simply increasing model capacity.

Methodology

1. Hybrid Model Structure

   • Quantum Encoder: A shallow variational quantum circuit (VQC) processes the input and outputs a low‑dimensional quantum feature vector after measurement.
   • Residual Path: The original (classical) input vector is concatenated with the quantum feature vector, forming a richer representation.
   • Classical Classifier: A standard neural network (e.g., a fully‑connected layer or small MLP) consumes the combined vector and produces the final prediction.

2. Training Pipeline

   • The quantum circuit parameters are optimized jointly with the classical classifier using gradient‑based methods (parameter‑shift rule for quantum gradients, back‑propagation for classical parts).
   • In federated experiments, each client runs the same hybrid model locally, sending only the classical classifier weights to the server; quantum parameters stay on‑device, keeping communication overhead minimal.

3. Evaluation Setup

   • Benchmarks include classic classification datasets (e.g., MNIST, CIFAR‑10 subsets) and synthetic federated partitions to emulate edge‑device heterogeneity.
   • Baselines: pure quantum classifiers, prior hybrid schemes without residual connections, and fully classical deep nets of comparable size.

Results & Findings

• Ablation: Removing the residual concatenation drops accuracy back to the level of prior hybrids, confirming the bypass’s central role.
• Scalability: Increasing quantum circuit depth beyond 4 layers yields diminishing returns, while the residual path continues to drive gains, underscoring the method’s near‑term suitability for NISQ devices.

Practical Implications

• Edge‑AI & Federated Learning: Developers can embed a shallow quantum encoder on resource‑constrained devices (e.g., smartphones, IoT sensors) without inflating bandwidth or power consumption, while still leveraging quantum expressivity for better model generalization.
• Privacy‑First Deployments: Since the raw input bypasses the quantum measurement, sensitive features need not be fully exposed to the quantum subsystem, reducing attack surfaces in privacy‑critical domains like healthcare or finance.
• Rapid Prototyping: The architecture plugs into existing ML pipelines (PyTorch, TensorFlow) via standard quantum SDKs (Qiskit, Pennylane), allowing teams to experiment with QML without rewriting data loaders or training loops.
• Hardware‑agnostic: Because the quantum part stays shallow, the approach works on current noisy intermediate‑scale quantum (NISQ) hardware and can be simulated classically for early development.

Limitations & Future Work

• Quantum Hardware Noise: While the residual bypass mitigates depth‑related errors, the method still inherits the stochastic nature of current quantum measurements, which can affect reproducibility on real devices.
• Model Size Trade‑off: The concatenation increases the dimensionality of the classifier input, potentially requiring larger classical layers for very high‑dimensional raw data.
• Task Scope: Experiments focus on image classification; extending the approach to sequential or graph‑structured data remains an open question.
• Theoretical Guarantees: The paper provides empirical evidence of privacy robustness but lacks formal proofs of differential privacy or information‑theoretic bounds.

Future research directions include: (1) formalizing privacy guarantees under the residual bypass, (2) exploring adaptive residual weighting schemes, and (3) testing the architecture on larger, real‑world federated deployments (e.g., smart‑city sensor networks).

Authors

• Guilin Zhang
• Wulan Guo
• Ziqi Tan
• Hongyang He
• Hailong Jiang

Paper Information

• arXiv ID: 2511.20922v1
• Categories: cs.CR, cs.DC, cs.LG
• Published: November 25, 2025
• PDF: Download PDF