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Paper 1

Decoder Performance in Hybrid CV-Discrete Surface-Code Threshold Estimation Using LiDMaS+

Dennis Delali Kwesi Wayo, Chinonso Onah, Vladimir Milchakov, Leonardo Goliatt, Sven Groppe

Year: 2026
Journal: arXiv preprint
DOI: arXiv:2603.06730
arXiv: 2603.06730

Threshold estimation is central to fault-tolerant quantum computing, but the reported threshold depends not only on the code and noise model, but also on the decoder used to interpret syndrome data. We study this dependence for surface-code threshold estimation under both a standard Pauli noise model and a hybrid continuous-variable/discrete model motivated by GKP-style digitization. Using LiDMaS+ as a common experimental platform, we compare minimum-weight perfect matching (MWPM) and Union-Find under matched sweep grids, matched distances, and deterministic seeding, and we additionally evaluate trained neural-guided MWPM in the hybrid regime. In the Pauli baseline at distance $d=5$, MWPM consistently outperforms Union-Find, reducing the mean sampled logical error rate from $0.384$ to $0.260$, and producing a stable threshold summary with crossing median $p_c \approx 0.053$. In the hybrid fixed-distance run, Union-Find is substantially worse than MWPM (mean LER $0.1657$ versus $0.1195$), while trained neural-guided MWPM tracks MWPM closely (mean LER $0.1158$). Across hybrid multi-distance sweeps, the distance-dependent reversal in logical-error ordering remains visible, but the grid-based crossing estimator still returns boundary-valued $σ_c=0.05$ for all decoders. Neural-guided runs also show elevated decoder-failure diagnostics at high noise ($\max$ decoder-failure rate $0.1335$ at $d=7,σ=0.60$), indicating that learned guidance quality and decoder robustness must be reported alongside threshold curves. These results show that decoder choice and estimator design both materially affect threshold inference.

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Paper 2

Assessment of quantum chemical predictors for anti-colorectal cancer agents using QSAR modeling.

Alharbi Y, Yadav K, Alkwai LM, Dutta D, Abumalek A

Year: 2026
Journal: Journal of computer-aided molecular design
DOI: 10.1007/s10822-026-00783-9
arXiv: -

Optimizing the prediction of anti-colorectal cancer agent activity is essential aspect in the identification and creation of medications. Machine learning (ML) techniques, which have gained widespread adoption in computational chemistry, offer a rapid and reliable approach for evaluating the relationship between molecular structures and bioactivity. In this paper, a comprehensive dataset of molecular descriptors and quantum chemical properties was compiled, encompassing general molecular properties, electronic and quantum characteristics, aromatic ring structure, halogen effects, functional groups, specific structural features, and molecular charge characteristics. This dataset enhances the adaptability of data-driven models and mitigates the risk of overfitting. Seven tree-based ML algorithms, including Gradient Boosting, Random Forest, Decision Tree, Light gradient boosting (LightGBM), Categorical boosting (CatBoost), Extreme gradient boosting (XGBoost), and Extra Trees, were utilized to forecast the bioactivity of candidate compounds against colon cancer cell lines. Key molecular predictors were analyzed, and interaction terms between predictors were incorporated to improve model accuracy. The study utilizes the Tree-Structured Parzen Estimator for fine-tuning hyperparameters to enhance model efficiency and predictive accuracy. Additionally, k-fold cross-validation is utilized to avoid overfitting and guarantee a strong model evaluation and adaptability. These approaches enhance the dependability and effectiveness of data-driven models. The findings revealed that all models exhibited exceptional performance, with Extra Trees emerging as the top-performing algorithm due to its swift optimization process and superior performance in F1-Score and Recall metrics. These results highlight the potential of ML-driven methods to significantly enhance the prediction of anti-colorectal cancer agent activity by optimizing predictor selection based on quantum chemical properties and molecular interactions. This research offers novel perspectives on leveraging ML for quantitative structure-activity relationship (QSAR) modeling in drug discovery. By addressing challenges such as scarce labeled data and data gaps, and conducting an in-depth analysis of multiple ML algorithms, our study provides vital insights for computational chemists and pharmaceutical researchers, aiding them in selecting the most suitable algorithms for QSAR-based drug design. Ultimately, this work contributes to the advancement of anti-colorectal cancer drug discovery, enabling more efficient and sustainable drug development practices.

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