Noise-Resilient Quantum Aggregation on NISQ for Federated ADAS Learning

TL;DR

This paper proposes a hybrid quantum-classical federated learning framework that enhances noise resilience, security, and efficiency for vehicular ADAS systems operating under NISQ quantum conditions.

Contribution

It introduces a novel quantum federated learning framework with adaptive encoding, client selection, and multi-server coordination to improve robustness and scalability in noisy vehicular networks.

Findings

Achieves consistent convergence with reduced gradient variance.

Demonstrates lower communication overhead.

Shows enhanced noise tolerance under edge constraints.

Abstract

Advanced Driver Assistance Systems (ADAS) increasingly employ Federated Learning (FL) to collaboratively train models across distributed vehicular nodes while preserving data privacy. Yet, conventional FL aggregation remains susceptible to noise, latency, and security constraints inherent to real-time vehicular networks. This paper introduces Noise-Resilient Quantum Federated Learning (NR-QFL), a hybrid quantum-classical framework that enables secure, low-latency aggregation through variational quantum circuits (VQCs) operating under Noisy Intermediate-Scale Quantum (NISQ) conditions. The framework encodes model parameters as quantum states with adaptive gate reparameterization, ensuring bounded-error convergence and provable resilience under Completely Positive Trace-Preserving (CPTP) dynamics. NR-QFL employs quantum entropy-based client selection and multi-server coordination for fairness and stability. Empirical validation shows consistent convergence with reduced gradient variance, lower communication overhead, and enhanced noise tolerance under constrained edge conditions. The framework establishes a scalable foundation for quantum-enhanced federated learning, enabling secure, efficient, and dynamically stable ADAS intelligence at the vehicular edge.