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

arXiv:2511.01062 · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS)

ECCentric: An Empirical Analysis of Quantum Error Correction Codes

Aleksandra Świerkowska, Jannik Pflieger, Emmanouil Giortamis, Pramod Bhatotia

quant-phSubmitted Nov 2, 2025• Announced Nov 4, 2025• 0 citations
v1 · Nov 2, 2025v2 · Aug 18, 2026
arXivHTMLPDF

Abstract

Quantum error correction (QEC) is essential for building scalable quantum computers, but a lack of systematic, end-to-end evaluation methods makes it difficult to assess how different QEC codes perform under realistic conditions. The vast diversity of codes, an expansive experimental search space, and the absence of a standardized framework prevent a thorough, holistic analysis. To address this, we introduce ECCentric, an end-to-end benchmarking framework designed to systematically evaluate QEC codes across the full quantum computing stack. ECCentric is designed to be modular, extensible, and general, allowing for a comprehensive analysis of QEC code families under varying hardware topologies, noise models, and compilation strategies. Using ECCentric, we conduct the first systematic benchmarking of major QEC code families against realistic, mid-term quantum device parameters. Our empirical analysis reveals that intra-QPU execution significantly outperforms distributed methods, that qubit connectivity is a far more critical factor for reducing logical errors than increasing code distance, and that compiler overhead remains a major source of error. Furthermore, our findings suggest that trapped-ion architectures with qubit shuttling are the most promising near-term platforms and that on noisy devices, a strategic and selective application of QEC is necessary to avoid introducing more errors than are corrected. This study provides crucial, actionable insights for both hardware designers and practitioners, guiding the development of fault-tolerant quantum systems.

Journal reference
Proceedings of the ACM on Measurement and Analysis of Computing Systems, Vol. 10, Issue 2, Article 37 (2026), pp. 1-33
DOI
10.1145/3805635

Related papers

A Cross-Platform Analysis of Practical Quantum Error Correction Codes

Bryan Pan, Yufeng Xin

The theory of quantum error correction was established decades ago. Yet limitations in physical noise levels and available physical-qubit counts continue to hinder the development of scalable quantum computing systems. In this paper, we present analytical estimates of logical error rates for advanced QEC codes across leading hardware platforms and distributed quantum computing systems using a simple but unified framework. The analysis captures dominant contributors to logical error, including effective circuit volume and two-qubit gate overhead. The framework provides a fast and conservative estimate of logical error rates and identification of dominating factors in different hardware platforms, such as circuit volume, routing overhead, inter-QPU operations, or asymmetric noise protection. We show that several qualitative trends observed in larger-scale simulations can be reproduced and interpreted analytically within this framework. We further demonstrate that the framework can be used to find the sweet spot design region of distributed QEC, which is critical for the design of distributed quantum computing systems.

quant-pharXiv:2607.04082•Jul 7, 2026•0 citations

Advances in Quantum Error Correction: From Theory to Experimental Realization

Shilpi Verma, Markandey Singh

Quantum error correction (QEC) has emerged as a cornerstone in the quest for scalable and fault-tolerant quantum technologies. While quantum systems promise exponential advantages over classical computation, their extreme susceptibility to noise and decoherence presents a formidable challenge. This review critically examines the trajectory of QEC from its theoretical foundations to recent experimental realizations. Foundational codes, including Shor’s and Steane’s constructions, laid the groundwork for the stabilizer formalism and the development of topological and bosonic codes. Over the past decade, experimental demonstrations have validated these concepts, with logical qubits surpassing the lifetimes of physical qubits on platforms such as superconducting circuits, trapped ions, and photonics. Emerging approaches, including low-density parity-check codes, hybrid mitigation–correction strategies, and artificial intelligence–driven decoders, further illustrate the field’s dynamism. By synthesizing theoretical progress, empirical validation, and cross-platform innovations, this review highlights both achievements and persistent challenges particularly the overhead of physical qubits, the speed of real-time decoding, and integration into full-stack architectures. The analysis concludes that while scalable fault-tolerant quantum computing remains a work in progress, recent breakthroughs mark a decisive transition from abstraction to practicality, establishing QEC as a central pillar in the development of quantum computation, communication, and sensing.

•Sep 29, 2025•0 citations

Oraqle: An Empirical Analysis of Qubit Readout and Discriminators in Quantum Error Correction

Emmanouil Giortamis, Aleksandra Świerkowska, Sandra Stankovic +3 more

Quantum error correction (QEC) is the most promising route toward fault-tolerant quantum computing and, thus, useful quantum computers. QEC operates as a continuous measure-decode-correct cycle: ancilla qubits are read out, a decoder infers errors from the resulting syndromes, and corrections are applied before the next round begins. Within this loop, readout occupies a uniquely critical role, as it is the sole source of ground truth available to the decoder. Yet readout is also the slowest and most error-prone operation in the stack, with characteristics that vary across qubits and drift over time; This complexity propagates directly to the classical control hardware, and in particular to the FPGA-hosted machine-learning (ML) discriminator that must classify each analog signal into a binary syndrome outcome. Despite this central role, QEC performance has not yet been studied in depth from the perspective of readout characteristics, readout length, and their co-design with an ML discriminator. We introduce Oraqle, an end-to-end benchmarking framework that evaluates qubit-state readout and its impact on QEC performance across real experimentally extracted qubit-state-readout datasets, state-of-the-art ML discriminators, multiple QEC codes, and hardware regimes spanning current to projected devices. Our study reveals three asymmetric findings: The measurement duration can be significantly reduced with nearly no penalty to the logical error rate; The discriminator complexity barely affects the QEC performance, as residual errors are written into device physics rather than the model; and the impact of qubit-state readout on the logical error rate is conditional on where the hardware sits in the QEC landscape, a window that widens as devices mature.

quant-pharXiv:2608.01939•Aug 4, 2026•0 citations

Towards automatic evaluation of circuit fault-tolerance

Ayman Mondol

Quantum error correction codes are essential for fault-tolerant quantum computing. They are a method of detecting and correcting errors in a quantum computer by encoding logical qubits across multiple physical qubits. Several prominent error correction codes have been developed and larger and more complex codes are being developed continuously. However, evaluating the fault-tolerance of these larger codes is becoming exponentially more complicated. While conventional methods of proving and evaluating the fault-tolerance of these circuits have worked thus far, they are soon to be obsolete. The aim of this thesis is to develop a framework that automatically evaluates the fault-tolerance of a quantum error correction circuit across different hardware architectures. The framework works as follows: it takes a noiseless quantum error correction code; injects hardware-realistic noise into it; and evaluates the fault-tolerance of the resulting noisy circuit by estimating its logical error rate through Monte Carlo sampling. For the injection of hardware-realistic noise, this thesis considers noise stemming from five different quantum computing hardware architectures: superconducting qubits, neutral atoms, trapped ions, photonic qubits, and spin qubits. The noise model was constructed by extracting single-qubit gate, two-qubit gate and measurement error fidelities from current experimental literature. The noise model and circuit generation is implemented in Python, circuit representation and noise injection is executed in Stim and decoding and estimating the logical error rate is achieved through PyMatching. The framework is evaluated on the repetition code, the surface code, and the color code; the logical error rate is estimated across all five hardware architectures under the noise model; and the results are discussed in the context of hardware compatibility for the given codes.

•May 9, 2026•0 citations

Analyzing Decoders for Quantum Error Correction

Abtin Molavi, Feras Saad, Aws Albarghouthi

Quantum error correction (QEC) enables reliable computation on noisy hardware by encoding logical information across many physical qubits and periodically measuring parities to detect errors. A decoder is the classical algorithm that uses these measurements to infer which error most likely occurred, so that the system can correct it. The decoder's accuracy-how rarely it makes the wrong guess-directly determines the scale of quantum computation that can be reliably executed. With a wealth of competing decoding algorithms, a QEC system designer needs reliable methods to evaluate them. Today, the dominant approach is to evaluate decoders using Monte Carlo simulation. However, simulation has several drawbacks such as requiring many samples to produce low variance estimates. In this work, we develop a new systematic analysis for evaluating decoders. We introduce a novel formal semantics of a core language for QEC programs that captures the de facto standard Stim circuit format, providing a principled theoretical foundation for the emerging space of fault-tolerant quantum systems design. Given a QEC program and a decoder, our verifier can quantify both the decoder accuracy and the decoder robustness to drift in physical error rate. Our approach has two key components: (i) a structured search over the space of possible errors; and (ii) a constrained polynomial optimization kernel. A thorough empirical evaluation of our approach suggests that it can outperform simulation, especially in low error rate regimes, and that it can be deployed to quantify decoder robustness over an interval of physical error rates.

cs.PLCross-listedarXiv:2603.20127•Mar 23, 2026•0 citations

Quantum Error Correction and Fault-Tolerant Computing: Recent Progress in Codes, Decoders, and Architectures

Volkan Erol

Quantum error correction (QEC) represents the cornerstone technology for realizing fault-tolerant quantum computing, addressing the fundamental challenge of quantum state decoherence in noisy intermediate-scale quantum (NISQ) devices. This comprehensive review examines recent advances in QEC implementations from 2020-2024, spanning theoretical foundations to practical hardware demonstrations. We analyze the evolution from pioneering stabilizer codes to modern topological approaches, with particular emphasis on surface codes and low-density parity-check (LDPC) codes that have demonstrated below-threshold error correction in superconducting and trapped-ion systems. The paper provides a comparative analysis of classical and machine learning-based decoder algorithms, evaluating their performance through complexity analysis and threshold comparisons in real-time error correction scenarios. Through examination of breakthrough experiments by Google, IBM, Quantinuum, and emerging platforms, we assess the current state of fault-tolerant quantum computing and identify critical engineering challenges including correlated noise models, cryogenic control systems, and scalable logical gate implementations. Our quantitative analysis reveals that while significant progress has been made toward practical fault-tolerance thresholds, achieving the resource efficiency and reliability required for large-scale quantum computation remains challenging. We provide detailed resource estimates for key applications including Shor's algorithm and optimization problems, along with analysis of industry roadmaps toward thousand-logical-qubit systems. This review serves as a comprehensive guide for researchers and engineers working toward the next generation of fault-tolerant quantum computers, with particular focus on information-theoretic perspectives relevant to quantum information entropy and syndrome processing.

•Sep 30, 2025•3 citations
Quantum error correction (QEC) is essential for building scalable quantum computers, but a lack of systematic, end-to-end evaluation methods makes it difficult to assess how different QEC codes perform under realistic conditions. The vast diversity of codes, an expansive experimental search space, and the absence of a standardized framework prevent a thorough, holistic analysis. To address this, we introduce ECCentric, an end-to-end benchmarking framework designed to systematically evaluate QEC codes across the full quantum computing stack. ECCentric is designed to be modular, extensible, and general, allowing for a comprehensive analysis of QEC code families under varying hardware topologies, noise models, and compilation strategies.
Using ECCentric, we conduct the first systematic benchmarking of major QEC code families against realistic, mid-term quantum device parameters. Our empirical analysis reveals that intra-QPU execution significantly outperforms distributed methods, that qubit connectivity is a far more critical factor for reducing logical errors than increasing code distance, and that compiler overhead remains a major source of error. Furthermore, our findings suggest that trapped-ion architectures with qubit shuttling are the most promising near-term platforms and that on noisy devices, a strategic and selective application of QEC is necessary to avoid introducing more errors than are corrected. This study provides crucial, actionable insights for both hardware designers and practitioners, guiding the development of fault-tolerant quantum systems.
The theory of quantum error correction was established decades ago. Yet limitations in physical noise levels and available physical-qubit counts continue to hinder the development of scalable quantum computing systems. In this paper, we present analytical estimates of logical error rates for advanced QEC codes across leading hardware platforms and distributed quantum computing systems using a simple but unified framework. The analysis captures dominant contributors to logical error, including effective circuit volume and two-qubit gate overhead. The framework provides a fast and conservative estimate of logical error rates and identification of dominating factors in different hardware platforms, such as circuit volume, routing overhead, inter-QPU operations, or asymmetric noise protection. We show that several qualitative trends observed in larger-scale simulations can be reproduced and interpreted analytically within this framework. We further demonstrate that the framework can be used to find the sweet spot design region of distributed QEC, which is critical for the design of distributed quantum computing systems.
Quantum error correction (QEC) has emerged as a cornerstone in the quest for scalable and fault-tolerant quantum technologies. While quantum systems promise exponential advantages over classical computation, their extreme susceptibility to noise and decoherence presents a formidable challenge. This review critically examines the trajectory of QEC from its theoretical foundations to recent experimental realizations. Foundational codes, including Shor’s and Steane’s constructions, laid the groundwork for the stabilizer formalism and the development of topological and bosonic codes. Over the past decade, experimental demonstrations have validated these concepts, with logical qubits surpassing the lifetimes of physical qubits on platforms such as superconducting circuits, trapped ions, and photonics. Emerging approaches, including low-density parity-check codes, hybrid mitigation–correction strategies, and artificial intelligence–driven decoders, further illustrate the field’s dynamism. By synthesizing theoretical progress, empirical validation, and cross-platform innovations, this review highlights both achievements and persistent challenges particularly the overhead of physical qubits, the speed of real-time decoding, and integration into full-stack architectures. The analysis concludes that while scalable fault-tolerant quantum computing remains a work in progress, recent breakthroughs mark a decisive transition from abstraction to practicality, establishing QEC as a central pillar in the development of quantum computation, communication, and sensing.
Quantum error correction (QEC) is the most promising route toward fault-tolerant quantum computing and, thus, useful quantum computers. QEC operates as a continuous measure-decode-correct cycle: ancilla qubits are read out, a decoder infers errors from the resulting syndromes, and corrections are applied before the next round begins. Within this loop, readout occupies a uniquely critical role, as it is the sole source of ground truth available to the decoder. Yet readout is also the slowest and most error-prone operation in the stack, with characteristics that vary across qubits and drift over time; This complexity propagates directly to the classical control hardware, and in particular to the FPGA-hosted machine-learning (ML) discriminator that must classify each analog signal into a binary syndrome outcome. Despite this central role, QEC performance has not yet been studied in depth from the perspective of readout characteristics, readout length, and their co-design with an ML discriminator. We introduce Oraqle, an end-to-end benchmarking framework that evaluates qubit-state readout and its impact on QEC performance across real experimentally extracted qubit-state-readout datasets, state-of-the-art ML discriminators, multiple QEC codes, and hardware regimes spanning current to projected devices. Our study reveals three asymmetric findings: The measurement duration can be significantly reduced with nearly no penalty to the logical error rate; The discriminator complexity barely affects the QEC performance, as residual errors are written into device physics rather than the model; and the impact of qubit-state readout on the logical error rate is conditional on where the hardware sits in the QEC landscape, a window that widens as devices mature.
Quantum error correction codes are essential for fault-tolerant quantum computing. They are a method of detecting and correcting errors in a quantum computer by encoding logical qubits across multiple physical qubits. Several prominent error correction codes have been developed and larger and more complex codes are being developed continuously. However, evaluating the fault-tolerance of these larger codes is becoming exponentially more complicated. While conventional methods of proving and evaluating the fault-tolerance of these circuits have worked thus far, they are soon to be obsolete. The aim of this thesis is to develop a framework that automatically evaluates the fault-tolerance of a quantum error correction circuit across different hardware architectures. The framework works as follows: it takes a noiseless quantum error correction code; injects hardware-realistic noise into it; and evaluates the fault-tolerance of the resulting noisy circuit by estimating its logical error rate through Monte Carlo sampling. For the injection of hardware-realistic noise, this thesis considers noise stemming from five different quantum computing hardware architectures: superconducting qubits, neutral atoms, trapped ions, photonic qubits, and spin qubits. The noise model was constructed by extracting single-qubit gate, two-qubit gate and measurement error fidelities from current experimental literature. The noise model and circuit generation is implemented in Python, circuit representation and noise injection is executed in Stim and decoding and estimating the logical error rate is achieved through PyMatching. The framework is evaluated on the repetition code, the surface code, and the color code; the logical error rate is estimated across all five hardware architectures under the noise model; and the results are discussed in the context of hardware compatibility for the given codes.
Quantum error correction (QEC) enables reliable computation on noisy hardware by encoding logical information across many physical qubits and periodically measuring parities to detect errors. A decoder is the classical algorithm that uses these measurements to infer which error most likely occurred, so that the system can correct it. The decoder's accuracy-how rarely it makes the wrong guess-directly determines the scale of quantum computation that can be reliably executed. With a wealth of competing decoding algorithms, a QEC system designer needs reliable methods to evaluate them. Today, the dominant approach is to evaluate decoders using Monte Carlo simulation. However, simulation has several drawbacks such as requiring many samples to produce low variance estimates. In this work, we develop a new systematic analysis for evaluating decoders. We introduce a novel formal semantics of a core language for QEC programs that captures the de facto standard Stim circuit format, providing a principled theoretical foundation for the emerging space of fault-tolerant quantum systems design. Given a QEC program and a decoder, our verifier can quantify both the decoder accuracy and the decoder robustness to drift in physical error rate. Our approach has two key components: (i) a structured search over the space of possible errors; and (ii) a constrained polynomial optimization kernel. A thorough empirical evaluation of our approach suggests that it can outperform simulation, especially in low error rate regimes, and that it can be deployed to quantify decoder robustness over an interval of physical error rates.
Quantum error correction (QEC) represents the cornerstone technology for realizing fault-tolerant quantum computing, addressing the fundamental challenge of quantum state decoherence in noisy intermediate-scale quantum (NISQ) devices. This comprehensive review examines recent advances in QEC implementations from 2020-2024, spanning theoretical foundations to practical hardware demonstrations. We analyze the evolution from pioneering stabilizer codes to modern topological approaches, with particular emphasis on surface codes and low-density parity-check (LDPC) codes that have demonstrated below-threshold error correction in superconducting and trapped-ion systems. The paper provides a comparative analysis of classical and machine learning-based decoder algorithms, evaluating their performance through complexity analysis and threshold comparisons in real-time error correction scenarios. Through examination of breakthrough experiments by Google, IBM, Quantinuum, and emerging platforms, we assess the current state of fault-tolerant quantum computing and identify critical engineering challenges including correlated noise models, cryogenic control systems, and scalable logical gate implementations. Our quantitative analysis reveals that while significant progress has been made toward practical fault-tolerance thresholds, achieving the resource efficiency and reliability required for large-scale quantum computation remains challenging. We provide detailed resource estimates for key applications including Shor's algorithm and optimization problems, along with analysis of industry roadmaps toward thousand-logical-qubit systems. This review serves as a comprehensive guide for researchers and engineers working toward the next generation of fault-tolerant quantum computers, with particular focus on information-theoretic perspectives relevant to quantum information entropy and syndrome processing.