Learning to Decode in Parallel: Self-Coordinating Neural Network for Real-Time Quantum Error Correction

Fast, reliable decoders are pivotal components for enabling fault-tolerant quantum computation (FTQC). Neural network decoders like AlphaQubit have demonstrated potential, achieving higher accuracy than traditional human-designed decoding algorithms. However, existing implementations of neural network decoders lack the parallelism required to decode the syndrome stream generated by a superconducting logical qubit in real time. Moreover, integrating AlphaQubit with sliding window-based parallel decoding schemes presents non-trivial challenges: AlphaQubit is trained solely to output a single bit corresponding to the global logical correction for an entire memory experiment, rather than local physical corrections that can be easily integrated. We address this issue by training a recurrent, transformer-based neural network specifically tailored for parallel window decoding. While it still outputs a single bit, we derive training labels from a consistent set of local corrections and train on various types of decoding windows simultaneously. This approach enables the network to self-coordinate across neighboring windows, facilitating high-accuracy parallel decoding of arbitrarily long memory experiments. As a result, we overcome the throughput bottleneck that previously precluded the use of AlphaQubit-type decoders in FTQC. Our work presents the first scalable, neural-network-based parallel decoding framework that simultaneously achieves SOTA accuracy and the stringent throughput required for real-time quantum error correction. Using an end-to-end experimental workflow, we benchmark our decoder on the Zuchongzhi 3.2 superconducting quantum processor on surface codes with distances up to 7, demonstrating its superior accuracy. Moreover, we demonstrate that, using our approach, a single TPU v6e is capable of decoding surface codes with distances up to 25 within 1us per decoding round.

A Prototype-Guided Coarse Annotations Refining Approach for Whole Slide Images

The fine-grained annotations in whole slide images (WSIs) show the boundaries of various pathological regions. However, generating such detailed annotation is often costly, whereas the coarse annotations are relatively simpler to produce. Existing methods for refining coarse annotations often rely on extensive training samples or clean datasets, and fail to capture both intra-slide and inter-slide latent sematic patterns, limiting their precision. In this paper, we propose a prototype-guided approach. Specifically, we introduce a local-to-global approach to construct non-redundant representative prototypes by jointly modeling intra-slide local semantics and inter-slide contextual relationships. Then a prototype-guided pseudo-labeling module is proposed for refining coarse annotations. Finally, we employ dynamic data sampling and re-finetuning strategy to train a patch classifier. Extensive experiments on three publicly available WSI datasets, covering lymph, liver, and colorectal cancers, demonstrate that our method significantly outperforms existing state-of-the-art (SOTA) methods. The code will be available.

Unveiling Institution-Specific Bias in Pathology Foundation Models: Detriments, Causes, and Potential Solutions

Pathology foundation models (PFMs) extract valuable discriminative features from images for downstream clinical tasks. PFMs have simplified the development of deep learning models, effectively leveraging prior knowledge to improve diagnostic accuracy in diverse scenarios. However, we find that PFMs sometimes struggle with certain challenges. Specifically, features extracted by PFMs are often contaminated by diagnosis-irrelevant information, i.e., institution-specific features associated with the images. This contamination can lead to spurious correlations, undermining the models' generalization ability when applied in real-world clinical settings. In this work, we first reveal the issue of feature contamination in PFMs, demonstrate the presence of institution-specific features, thoroughly investigate its negative impacts, analyze the underlying causes, and provide insights into potential solutions. Specifically, we find that institution-specific information is embedded in pathological images and can be readily captured by current PFMs. Through extensive experiments, we demonstrate the detrimental impact of this irrelevant information, particularly in out-of-distribution (OOD) settings, where reliance on contaminated features leads to significant performance degradation. This indicates that the models are being misled by non-diagnostic information. We further delve into the reasons PFMs extract such institution-specific information and validate our findings. Finally, we propose a simple yet effective solution to mitigate the influence of irrelevant information. This study is not intended to criticize existing PFMs, as they have indeed greatly advanced the development of computational pathology. our aim is to inspire future research to focus on innovative training strategies, rather than relying exclusively on scaling laws, to realize more generalized PFMs.