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

Topological Quantum Error Correction for Scalable Quantum Networks: A Unified Framework for Fault-Tolerant Distributed Quantum Computing

Uchechukwu Ajuzieogu

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.13140/rg.2.2.34129.39528

Related papers

Topological Quantum Error Correction with Tensor-Network Optimization for Scalable Photonic Quantum Computing Architectures

Murali Krishna Pasupuleti

Abstract: This study investigates a topological quantum error-correction framework for scalable photonic quantum-computing architectures in which fusion-based entangling primitives are combined with tensor-network optimization. The central problem addressed is the gap between high-fidelity photonic components and the stringent logical-error rates required for fault-tolerant quantum computation. Photonic platforms offer room-temperature operation, manufacturable silicon-photonic process flows, high-bandwidth optical interconnects and natural compatibility with measurement-based computation; however, photon loss, probabilistic fusion and decoder latency impose architectural constraints that cannot be resolved by hardware fidelity alone. The proposed framework therefore treats hardware metrics, code geometry and decoding as a coupled optimization problem rather than as separate engineering layers. The manuscript develops a mathematically grounded model of surface-code and cluster-state error correction under photonic loss and fusion noise, then evaluates tensor-network contraction as a decoder and resource-allocation method. The data section explicitly uses published real-data-based benchmark inputs from contemporary photonic and quantum-error-correction literature, supplemented by transparent, domain-plausible projections where complete open photonic logical-QEC datasets are not yet available. Results show that tensor-network optimization improves the logical-error index relative to matching-based baselines in the modelled photonic regime, while the feasibility boundary remains dominated by loss, fusion infidelity and real-time decoding latency. The contribution is a reproducible evaluation framework that links topological protection, photonic architecture constraints and decoder design for future scalable fault-tolerant systems. Keywords: topological quantum error correction; surface code; photonic quantum computing; fusion-based quantum computation; tensor-network decoding; loss tolerance; scalable quantum architecture; measurement-based quantum computing; fault tolerance; silicon photonics.

International Journal of Academic and Industrial Research Innovations(IJAIRI)•Jun 10, 2026•0 citations

QUANTUM ERROR CORRECTION AND FAULT-TOLERANT QUANTUM COMPUTING

Frank Gaitan, Ran Li

•Nov 1, 2009•38 citations

Fault-Tolerant Quantum Error Correction and Fault-Tolerant Quantum Computing

Ivan B. Djordjević

Elsevier eBooks•Jan 1, 2021•1 citation

Quantum Error Correction Based Topological Protected Quantum Neural Networks

Jincheng Zhang

This paper proposes a novel architecture for quantum neural networks (QNNs) that leverages the benefits of topological protection and quantum error correction (QEC). The core idea is to construct a QNN based on a network of non-Abelian anyons, exploiting their inherent robustness against local perturbations. We introduce a framework where topological protection significantly reduces the impact of noise on the network, while LDPC (Low-Density Parity-Check) codes are employed to actively correct errors in the quantum information. The resulting system demonstrates a high tolerance to noise, a critical requirement for scalable QNN implementations. This approach represents a significant advancement in QNN design, merging the strengths of topological quantum computing and established QEC techniques. The theoretical framework outlines the key components and provides a pathway toward realizing fault-tolerant QNNs. The proposed architecture offers a promising route toward building practical and reliable quantum computing systems.

•Aug 28, 2026•0 citations

Homotopy-Guided Adaptive Error Correction Frameworks for Fault-Tolerant Quantum Computing: A Multi-Scale Topological Approach

Murali Krishna Pasupuleti

Abstract: Quantum error correction (QEC) is fundamental to achieving fault-tolerant quantum computing. Recent advances indicate that homotopy theory and related topological methods offer powerful tools for constructing adaptive, noise-resilient error correction frameworks. By critically interpreting findings across homological and homotopical quantum error correction studies, this paper proposes a multi-scale, homotopy-guided adaptive error correction (HGAEC) framework. Statistical analysis, visual models, and scientific methods support the design. This research highlights novel pathways for topological fault tolerance, dynamic code adaptation, and resource optimization in quantum systems. Keywords: Homotopy Theory, Quantum Error Correction, Fault-Tolerant Computing, Topological Codes, Adaptive Error Correction

International Journal of Academic and Industrial Research Innovations(IJAIRI)•Apr 28, 2025•0 citations

Fault-Tolerant Quantum Error Correction and Fault-Tolerant Quantum Computing

Ivan B. Djordjević

Elsevier eBooks•Jan 1, 2012•0 citations
Abstract: This study investigates a topological quantum error-correction framework for scalable photonic quantum-computing architectures in which fusion-based entangling primitives are combined with tensor-network optimization. The central problem addressed is the gap between high-fidelity photonic components and the stringent logical-error rates required for fault-tolerant quantum computation. Photonic platforms offer room-temperature operation, manufacturable silicon-photonic process flows, high-bandwidth optical interconnects and natural compatibility with measurement-based computation; however, photon loss, probabilistic fusion and decoder latency impose architectural constraints that cannot be resolved by hardware fidelity alone. The proposed framework therefore treats hardware metrics, code geometry and decoding as a coupled optimization problem rather than as separate engineering layers. The manuscript develops a mathematically grounded model of surface-code and cluster-state error correction under photonic loss and fusion noise, then evaluates tensor-network contraction as a decoder and resource-allocation method. The data section explicitly uses published real-data-based benchmark inputs from contemporary photonic and quantum-error-correction literature, supplemented by transparent, domain-plausible projections where complete open photonic logical-QEC datasets are not yet available. Results show that tensor-network optimization improves the logical-error index relative to matching-based baselines in the modelled photonic regime, while the feasibility boundary remains dominated by loss, fusion infidelity and real-time decoding latency. The contribution is a reproducible evaluation framework that links topological protection, photonic architecture constraints and decoder design for future scalable fault-tolerant systems. Keywords: topological quantum error correction; surface code; photonic quantum computing; fusion-based quantum computation; tensor-network decoding; loss tolerance; scalable quantum architecture; measurement-based quantum computing; fault tolerance; silicon photonics.
This paper proposes a novel architecture for quantum neural networks (QNNs) that leverages the benefits of topological protection and quantum error correction (QEC). The core idea is to construct a QNN based on a network of non-Abelian anyons, exploiting their inherent robustness against local perturbations. We introduce a framework where topological protection significantly reduces the impact of noise on the network, while LDPC (Low-Density Parity-Check) codes are employed to actively correct errors in the quantum information. The resulting system demonstrates a high tolerance to noise, a critical requirement for scalable QNN implementations. This approach represents a significant advancement in QNN design, merging the strengths of topological quantum computing and established QEC techniques. The theoretical framework outlines the key components and provides a pathway toward realizing fault-tolerant QNNs. The proposed architecture offers a promising route toward building practical and reliable quantum computing systems.
Abstract: Quantum error correction (QEC) is fundamental to achieving fault-tolerant quantum computing. Recent advances indicate that homotopy theory and related topological methods offer powerful tools for constructing adaptive, noise-resilient error correction frameworks. By critically interpreting findings across homological and homotopical quantum error correction studies, this paper proposes a multi-scale, homotopy-guided adaptive error correction (HGAEC) framework. Statistical analysis, visual models, and scientific methods support the design. This research highlights novel pathways for topological fault tolerance, dynamic code adaptation, and resource optimization in quantum systems. Keywords: Homotopy Theory, Quantum Error Correction, Fault-Tolerant Computing, Topological Codes, Adaptive Error Correction