Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Conference on Information Networking

Fidelity-Aware Angular Aggregation for Quantum Federated Learning over Noisy Entanglement

Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel

Published Jan 14, 2026• 1 citation
Publisher

Abstract

We propose $\text{FAAA}-\beta$ (Fidelity-Aware Angular Aggregation), a novel teleportation-conditioned aggregation framework for quantum learning (QFL) that incorporates quantum channel quality metrics into the model update process; $\beta$ is a tunable blending parameter. $\text{FAAA}-\beta$ harnesses variational parameters as angular coordinates on a torus and performs fidelity-weighted circular consensus with parameterized linear blending. Our method computes trust weights based on measured fidelity $F_{i}$, latency $\tau_{i}$, and instability $\sigma_{i}^{2}$, establishing a crosslayer coupling between the physical-layer teleportation quality and the model-layer aggregation. Experimental evaluation using SamplerQNN on binary classification tasks under synthetically degraded Bell-pair fidelities demonstrates that $\text{FAAA}-\beta$ achieves competitive accuracy with significantly reduced variance and enhanced stability.

DOI
10.1109/icoin68469.2026.11480586

Related papers

A Stable Aggregation Method for Quantum Federated Learning

Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel

Abstract—Quantum federated learning (QFL) enables clients to train quantum neural network (QNN) models without sharing private data. We find that aggregation in QFL is unstable under heterogeneous data, unreliable communication, variable fidelity, latency, and quantum hardware noise. Moreover, QFL is non-trivial to address because several QNN parameters are periodic angles, and Euclidean averaging often fails to capture the inherent dynamics. We develop a novel self-consistent midpoint aggregation method for the stable design and implementation of QFLs. We combine QoS-aware client weighting, circular parameter aggregation, and bounded midpoint-based update control. We perform several angular tests and experiments on IBM's real Quantum machines for validation, confirming our approach. Extensive evaluations and experiments on medical and financial datasets show improved stability, lower volatility, and competitive accuracy. Index Terms—Quantum federated learning, adaptive aggregation, geometry-aware aggregation, self-consistent midpoint, quality of service, angular aggregation, quantum neural networks.

arXiv:2609.00356•Jun 25, 2026•0 citations

A2G-QFL: Adaptive Aggregation with Two Gains in Quantum Federated Learning

Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel

Federated learning (FL) deployed over quantumenabled and heterogeneous classical networks can degrade under uneven client quality, modeled teleportation/link fidelity, and communication instability, while standard aggregation rules assume Euclidean topology and uniform reliability, limiting their suitability for emerging quantum federated systems. We introduce A2G (Adaptive Aggregation with Two Gains). This dualgain framework jointly regulates server-side relaxation through a geometry gain. It modulates client importance using a QoS gain derived from teleportation/link fidelity, latency, and instability (variance of the QoS signal). The proposed A2G update rule shows that A2G recovers baseline approaches such as FedAvg, Latency-only, QoS-aware averaging, and manifold-inspired aggregation as special cases. Experiments on a Qiskit Aer-based quantum-classical simulation testbed demonstrate improved stability and accuracy under heterogeneous, realistic, and simulated quantum-noisy conditions.

International Conference on Quantum Communications, Networking, and Computing (QCNC)•Apr 6, 2026•0 citations

Quantum Federated Aggregation using Joint Fidelity-and-Entropy Computation

Seok Bin Son, Soohyun Park

Quantum federated learning (QFL) incorporates the principles of quantum neural networks (QNN) over federated learning (FL). It updates quantum parameters to the server. However, QFL aggregation involves quantum parameters from all local QNN models, leading to performance degradation due to outlier models. Thus, a novel fair QFL algorithm is proposed to enhance learning performance by eliminating outliers using Lyapunov optimization at the QFL server. This approach is used to take care of the trade-off between accuracy and latency, where the accuracy is associated with the freshness of aggregation. Moreover, a fidelity/entropy-adaptive algorithm is proposed, which relies on the degree control to select outliers. This approach (i) quantifies the dissimilarity in quantum states between the target model and each local device using fidelity, and (ii) evaluates the imbalance of data classes in each local device using entropy. Experimental results demonstrate that the proposed fair QFL algorithm outperforms benchmarks.

IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW)•Jul 21, 2025•0 citations

Entanglement Aware Federated Quantum Learning in NISQ Era Edge Systems

Suresh Kumar Jha, Ashok Kumar Pasi, Muddada Murali Krishna +1 more

The convergence of quantum computing and distributed edge intelligence presents new possibilities for advancing federated learning in NISQ-era settings. Conventional federated learning, however, becomes increasingly vulnerable to decoherence, communication noise, and heterogeneous client data when extended to quantum-enabled systems. This work proposes an Entanglement-Aware Federated Quantum Learning (EAFQL) framework that couples local variational quantum circuit (VQC) training with a fidelity-weighted aggregation rule at the server. The use of entanglement fidelity as a weighting factor allows the aggregation process to compensate for noise-induced degradation in the client-server links. A corresponding theoretical model is developed, and the framework is implemented in a hybrid simulation environment using Qiskit and PennyLane. Experimental results show that EAFQL attains accuracy comparable to classical FedAvg while offering improved stability. The method remains effective under non-IID data distributions, synthetic depolarizing and amplitude-damping noise, and varying entanglement-distribution topologies. These findings indicate that fidelity-aware aggregation can enhance the reliability of federated learning in emerging quantum-classical edge networks.

IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)•Mar 12, 2026•0 citations

Adaptive Aggregation with Two Gains in QFL

S Nanayakkara

Federated learning (FL) deployed over quantum enabled and heterogeneous classical networks faces significant performance degradation due to uneven client quality, stochastic teleportation fidelity, device instability, and geometric mismatch between local and global models. Classical aggregation rules assume euclidean topology and uniform communication reliability, limiting their suitability for emerging quantum federated systems. This paper introduces A2G (Adaptive Aggregation with Two Gains), a dual gain framework that jointly regulates geometric blending through a geometry gain and modulates client importance using a QoS gain derived from teleportation fidelity, latency, and instability.

cs.LGCross-listedarXiv:2512.03363•Dec 4, 2025•0 citations

Toward Uniform Quantum Federated Aggregation: Heterogeneity Exclusion Using Entropy and Fidelity

Seok Bin Son, Soohyun Park

Quantum federated learning (QFL) incorporates the principles of quantum neural networks (QNNs) and federated learning (FL), ensuring privacy by updating quantum parameters to the server. However, server aggregation in general QFL involves quantum parameters from all local models, leading to performance degradation due to the participation of outlier heterogeneous local models. Inspired by this, a novel QFL algorithm is proposed to enhance learning performance by excluding outliers before aggregation using Lyapunov optimization at QFL server. This Lyapunov optimization is used to take care of the tradeoff between accuracy and latency, where the accuracy is associated with the freshness of aggregation. Additionally, an entropy-and-fidelity aware algorithm is proposed, which relies on the degree control to select outliers for exclusion comprehensively. This algorithm addresses data and quantum aspects by evaluating the imbalance of data classes in each local model using entropy and quantifying the dissimilarity in quantum states between the target model and each local model through fidelity. Experimental results demonstrate that the proposed algorithm outperforms benchmarks, effectively excluding heterogeneous local models to improve performance while ensuring stability.

IEEE Internet of Things Journal•Oct 30, 2024•8 citations
We propose FAAA−β\text{FAAA}-\betaFAAA−β (Fidelity-Aware Angular Aggregation), a novel teleportation-conditioned aggregation framework for quantum learning (QFL) that incorporates quantum channel quality metrics into the model update process; β\betaβ is a tunable blending parameter. FAAA−β\text{FAAA}-\betaFAAA−β harnesses variational parameters as angular coordinates on a torus and performs fidelity-weighted circular consensus with parameterized linear blending. Our method computes trust weights based on measured fidelity FiF_{i}Fi​, latency τi\tau_{i}τi​, and instability σi2\sigma_{i}^{2}σi2​, establishing a crosslayer coupling between the physical-layer teleportation quality and the model-layer aggregation. Experimental evaluation using SamplerQNN on binary classification tasks under synthetically degraded Bell-pair fidelities demonstrates that FAAA−β\text{FAAA}-\betaFAAA−β achieves competitive accuracy with significantly reduced variance and enhanced stability.
Abstract—Quantum federated learning (QFL) enables clients to train quantum neural network (QNN) models without sharing private data. We find that aggregation in QFL is unstable under heterogeneous data, unreliable communication, variable fidelity, latency, and quantum hardware noise. Moreover, QFL is non-trivial to address because several QNN parameters are periodic angles, and Euclidean averaging often fails to capture the inherent dynamics. We develop a novel self-consistent midpoint aggregation method for the stable design and implementation of QFLs. We combine QoS-aware client weighting, circular parameter aggregation, and bounded midpoint-based update control. We perform several angular tests and experiments on IBM's real Quantum machines for validation, confirming our approach. Extensive evaluations and experiments on medical and financial datasets show improved stability, lower volatility, and competitive accuracy. Index Terms—Quantum federated learning, adaptive aggregation, geometry-aware aggregation, self-consistent midpoint, quality of service, angular aggregation, quantum neural networks.
Federated learning (FL) deployed over quantumenabled and heterogeneous classical networks can degrade under uneven client quality, modeled teleportation/link fidelity, and communication instability, while standard aggregation rules assume Euclidean topology and uniform reliability, limiting their suitability for emerging quantum federated systems. We introduce A2G (Adaptive Aggregation with Two Gains). This dualgain framework jointly regulates server-side relaxation through a geometry gain. It modulates client importance using a QoS gain derived from teleportation/link fidelity, latency, and instability (variance of the QoS signal). The proposed A2G update rule shows that A2G recovers baseline approaches such as FedAvg, Latency-only, QoS-aware averaging, and manifold-inspired aggregation as special cases. Experiments on a Qiskit Aer-based quantum-classical simulation testbed demonstrate improved stability and accuracy under heterogeneous, realistic, and simulated quantum-noisy conditions.
Quantum federated learning (QFL) incorporates the principles of quantum neural networks (QNN) over federated learning (FL). It updates quantum parameters to the server. However, QFL aggregation involves quantum parameters from all local QNN models, leading to performance degradation due to outlier models. Thus, a novel fair QFL algorithm is proposed to enhance learning performance by eliminating outliers using Lyapunov optimization at the QFL server. This approach is used to take care of the trade-off between accuracy and latency, where the accuracy is associated with the freshness of aggregation. Moreover, a fidelity/entropy-adaptive algorithm is proposed, which relies on the degree control to select outliers. This approach (i) quantifies the dissimilarity in quantum states between the target model and each local device using fidelity, and (ii) evaluates the imbalance of data classes in each local device using entropy. Experimental results demonstrate that the proposed fair QFL algorithm outperforms benchmarks.
The convergence of quantum computing and distributed edge intelligence presents new possibilities for advancing federated learning in NISQ-era settings. Conventional federated learning, however, becomes increasingly vulnerable to decoherence, communication noise, and heterogeneous client data when extended to quantum-enabled systems. This work proposes an Entanglement-Aware Federated Quantum Learning (EAFQL) framework that couples local variational quantum circuit (VQC) training with a fidelity-weighted aggregation rule at the server. The use of entanglement fidelity as a weighting factor allows the aggregation process to compensate for noise-induced degradation in the client-server links. A corresponding theoretical model is developed, and the framework is implemented in a hybrid simulation environment using Qiskit and PennyLane. Experimental results show that EAFQL attains accuracy comparable to classical FedAvg while offering improved stability. The method remains effective under non-IID data distributions, synthetic depolarizing and amplitude-damping noise, and varying entanglement-distribution topologies. These findings indicate that fidelity-aware aggregation can enhance the reliability of federated learning in emerging quantum-classical edge networks.
Federated learning (FL) deployed over quantum enabled and heterogeneous classical networks faces significant performance degradation due to uneven client quality, stochastic teleportation fidelity, device instability, and geometric mismatch between local and global models. Classical aggregation rules assume euclidean topology and uniform communication reliability, limiting their suitability for emerging quantum federated systems. This paper introduces A2G (Adaptive Aggregation with Two Gains), a dual gain framework that jointly regulates geometric blending through a geometry gain and modulates client importance using a QoS gain derived from teleportation fidelity, latency, and instability.
Quantum federated learning (QFL) incorporates the principles of quantum neural networks (QNNs) and federated learning (FL), ensuring privacy by updating quantum parameters to the server. However, server aggregation in general QFL involves quantum parameters from all local models, leading to performance degradation due to the participation of outlier heterogeneous local models. Inspired by this, a novel QFL algorithm is proposed to enhance learning performance by excluding outliers before aggregation using Lyapunov optimization at QFL server. This Lyapunov optimization is used to take care of the tradeoff between accuracy and latency, where the accuracy is associated with the freshness of aggregation. Additionally, an entropy-and-fidelity aware algorithm is proposed, which relies on the degree control to select outliers for exclusion comprehensively. This algorithm addresses data and quantum aspects by evaluating the imbalance of data classes in each local model using entropy and quantifying the dissimilarity in quantum states between the target model and each local model through fidelity. Experimental results demonstrate that the proposed algorithm outperforms benchmarks, effectively excluding heterogeneous local models to improve performance while ensuring stability.