Selected work

Representative Papers

Decoding Algorithm to Composite Errors Consisting of Deletions and Insertions for Quantum Deletion-Correcting Codes Based on Quantum Reed-Solomon Codes

May 12, 2026

Although Hagiwara codes are capable of correcting combined deletion and insertion errors, the longstanding absence of an efficient decoding algorithm has hindered their practical deployment. This work presents the first efficient decoding scheme for Hagiwara codes constructed from quantum Reed–Solomon codes. By integrating classical synchronization error correction techniques with the structural properties of quantum codes, the proposed method establishes a decoding framework tailored to quantum deletion–insertion errors. The approach successfully corrects composite synchronization errors, substantially enhancing the practicality and feasibility of Hagiwara codes and addressing a critical gap in efficient decoding for this class of quantum error-correcting codes.

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On the existence of fair zero-determinant strategies in the periodic prisoner's dilemma game

Mar 20, 2026

This study investigates the existence of fair zero-determinant (ZD) strategies in the stochastic game setting of the repeated Prisoner’s Dilemma with environmental state transitions—a canonical example of a simple stochastic game. Unlike standard repeated games, the presence of exogenous state dynamics renders the conditions for such strategies significantly more intricate. By employing stochastic game modeling, Markov decision process analysis, and linear algebraic derivations, this work rigorously demonstrates for the first time that fair ZD strategies are not generally attainable in this framework. Notably, the classic Tit-for-Tat strategy no longer necessarily possesses the fair ZD property under these conditions. These findings reveal a fundamental structural distinction between stochastic games and repeated games regarding strategic possibilities, thereby challenging and extending established theoretical understandings in the field.

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Estimation of Geometric Transformation Matrices Using Grid-shaped Pilot Signals

Jan 26, 2026APSIPA Transactions on Signal and Information Processing

This work proposes a synchronization method for image digital watermarking based on directionally distinguishable grid-based pilot signals to address the common failure of watermark extraction after geometric attacks such as cropping. By embedding a grid structure with distinct horizontal and vertical spacings into the image, the method leverages the Radon transform to accurately estimate parameters of single or composite geometric transformations—including rotation, scaling, shearing, and cropping—thereby resolving issues of origin offset and directional ambiguity. Experimental results demonstrate that the proposed approach reliably recovers the transformation matrix with low estimation error under various complex geometric distortions, significantly enhancing both watermark robustness and synchronization accuracy.

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Probing and Enhancing the Robustness of GNN-based QEC Decoders with Reinforcement Learning

Aug 05, 2025

Graph neural network (GNN) decoders for quantum error correction (QEC) exhibit poor robustness against adversarial perturbations, particularly in surface-code decoding. Method: We propose the first reinforcement learning (RL)-based automated vulnerability discovery and adversarial enhancement framework. An RL agent is designed to minimize syndrome bit flips required to induce decoder misclassification, precisely identifying fragile nodes in graph attention network (GAT) decoders; robustness is then improved via end-to-end adversarial training. Contribution/Results: This work introduces the first RL-driven approach for generating adversarial examples and analyzing vulnerabilities of QEC decoders. It further proposes a lightweight, interpretable adversarial training paradigm tailored to surface codes. Experiments on real surface-code data from Google Quantum AI demonstrate that the RL attack achieves high misclassification rates with minimal bit flips, while the adversarially trained decoder reduces logical error rates significantly, substantially improving robustness.

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Do GNN-based QEC Decoders Require Classical Knowledge? Evaluating the Efficacy of Knowledge Distillation from MWPM

Aug 05, 2025

It remains unclear whether graph neural networks (GNNs) for quantum error correction decoding require knowledge distillation from classical algorithms—such as minimum-weight perfect matching (MWPM)—to achieve high performance. Method: We propose a temporal node-feature-enhanced graph attention network (GAT) and systematically compare purely data-driven training against training augmented with MWPM-based distillation loss. Experiments use error syndromes extracted from real quantum hardware. Contribution/Results: Our results demonstrate that GNNs can directly learn complex, non-Markovian error correlations from raw data without relying on approximate theoretical models like MWPM. While distillation yields test accuracy comparable to the baseline, it slows convergence and increases training time by approximately 5×. These findings challenge the prevailing assumption that knowledge distillation is necessary for improving GNN-based decoders, and empirically validate the effectiveness and feasibility of end-to-end, data-driven learning for quantum error correction decoding.

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Recent publications

Latest Papers

Decoding Algorithm to Composite Errors Consisting of Deletions and Insertions for Quantum Deletion-Correcting Codes Based on Quantum Reed-Solomon Codes

May 12, 2026

Although Hagiwara codes are capable of correcting combined deletion and insertion errors, the longstanding absence of an efficient decoding algorithm has hindered their practical deployment. This work presents the first efficient decoding scheme for Hagiwara codes constructed from quantum Reed–Solomon codes. By integrating classical synchronization error correction techniques with the structural properties of quantum codes, the proposed method establishes a decoding framework tailored to quantum deletion–insertion errors. The approach successfully corrects composite synchronization errors, substantially enhancing the practicality and feasibility of Hagiwara codes and addressing a critical gap in efficient decoding for this class of quantum error-correcting codes.

0 citationsRead paper

On the existence of fair zero-determinant strategies in the periodic prisoner's dilemma game

Mar 20, 2026

This study investigates the existence of fair zero-determinant (ZD) strategies in the stochastic game setting of the repeated Prisoner’s Dilemma with environmental state transitions—a canonical example of a simple stochastic game. Unlike standard repeated games, the presence of exogenous state dynamics renders the conditions for such strategies significantly more intricate. By employing stochastic game modeling, Markov decision process analysis, and linear algebraic derivations, this work rigorously demonstrates for the first time that fair ZD strategies are not generally attainable in this framework. Notably, the classic Tit-for-Tat strategy no longer necessarily possesses the fair ZD property under these conditions. These findings reveal a fundamental structural distinction between stochastic games and repeated games regarding strategic possibilities, thereby challenging and extending established theoretical understandings in the field.

0 citationsRead paper

Estimation of Geometric Transformation Matrices Using Grid-shaped Pilot Signals

Jan 26, 2026APSIPA Transactions on Signal and Information Processing

This work proposes a synchronization method for image digital watermarking based on directionally distinguishable grid-based pilot signals to address the common failure of watermark extraction after geometric attacks such as cropping. By embedding a grid structure with distinct horizontal and vertical spacings into the image, the method leverages the Radon transform to accurately estimate parameters of single or composite geometric transformations—including rotation, scaling, shearing, and cropping—thereby resolving issues of origin offset and directional ambiguity. Experimental results demonstrate that the proposed approach reliably recovers the transformation matrix with low estimation error under various complex geometric distortions, significantly enhancing both watermark robustness and synchronization accuracy.

0 citationsRead paper

Probing and Enhancing the Robustness of GNN-based QEC Decoders with Reinforcement Learning

Aug 05, 2025

Graph neural network (GNN) decoders for quantum error correction (QEC) exhibit poor robustness against adversarial perturbations, particularly in surface-code decoding. Method: We propose the first reinforcement learning (RL)-based automated vulnerability discovery and adversarial enhancement framework. An RL agent is designed to minimize syndrome bit flips required to induce decoder misclassification, precisely identifying fragile nodes in graph attention network (GAT) decoders; robustness is then improved via end-to-end adversarial training. Contribution/Results: This work introduces the first RL-driven approach for generating adversarial examples and analyzing vulnerabilities of QEC decoders. It further proposes a lightweight, interpretable adversarial training paradigm tailored to surface codes. Experiments on real surface-code data from Google Quantum AI demonstrate that the RL attack achieves high misclassification rates with minimal bit flips, while the adversarially trained decoder reduces logical error rates significantly, substantially improving robustness.

0 citationsRead paper

Do GNN-based QEC Decoders Require Classical Knowledge? Evaluating the Efficacy of Knowledge Distillation from MWPM

Aug 05, 2025

It remains unclear whether graph neural networks (GNNs) for quantum error correction decoding require knowledge distillation from classical algorithms—such as minimum-weight perfect matching (MWPM)—to achieve high performance. Method: We propose a temporal node-feature-enhanced graph attention network (GAT) and systematically compare purely data-driven training against training augmented with MWPM-based distillation loss. Experiments use error syndromes extracted from real quantum hardware. Contribution/Results: Our results demonstrate that GNNs can directly learn complex, non-Markovian error correlations from raw data without relying on approximate theoretical models like MWPM. While distillation yields test accuracy comparable to the baseline, it slows convergence and increases training time by approximately 5×. These findings challenge the prevailing assumption that knowledge distillation is necessary for improving GNN-based decoders, and empirically validate the effectiveness and feasibility of end-to-end, data-driven learning for quantum error correction decoding.

0 citationsRead paper