arXiv:2512.07808v1 Announce Type: cross 
Abstract: Qubit readout is a critical operation in quantum computing systems, which maps the analog response of qubits into discrete classical states. Deep neural networks (DNNs) have recently emerged as a promising solution to improve readout accuracy . Prior hardware implementations of DNN-based readout are resource-intensive and suffer from high inference latency, limiting their practical use in low-latency decoding and quantum error correction (QEC) loops. This paper proposes LUNA, a fast and efficient superconducting qubit readout accelerator that combines low-cost integrator-based preprocessing with Look-Up Table (LUT) based neural networks for classification. The architecture uses simple integrators for dimensionality reduction with minimal hardware overhead, and employs LogicNets (DNNs synthesized into LUT logic) to drastically reduce resource usage while enabling ultra-low-latency inference. We integrate this with a differential evolution based exploration and optimization framework to identify high-quality design points. Our results show up to a 10.95x reduction in area and 30% lower latency with little to no loss in fidelity compared to the state-of-the-art. LUNA enables scalable, low-footprint, and high-speed qubit readout, supporting the development of larger and more reliable quantum computing systems.

تم اقتراح بنية جديدة تُدعى LUNA لتحسين قراءة الكيوبتات في الحوسبة الكمومية، باستخدام مزيج من المعالجة المسبقة القائمة على المُتكاملات منخفضة التكلفة والشبكات العصبية القائمة على جداول البحث (LUT). تهدف هذه الطريقة إلى تحسين دقة وسرعة قراءة الكيوبتات، وهو أمر أساسي لتصحيح الأخطاء الكمومية وعمليات فك التشفير ذات الكمون المنخفض.

Se ha propuesto una nueva arquitectura llamada LUNA para mejorar la lectura de qubits en la computación cuántica, utilizando una combinación de preprocesamiento basado en integradores de bajo costo y redes neuronales basadas en tablas de búsqueda (LUT). Este enfoque busca mejorar la precisión y velocidad de la lectura de qubits, que es esencial para la corrección de errores cuánticos y los procesos de decodificación de baja latencia.

Une nouvelle architecture nommée LUNA a été proposée pour améliorer la lecture des qubits dans l'informatique quantique, utilisant une combinaison de prétraitement basé sur des intégrateurs à faible coût et de réseaux neuronaux basés sur des tables de correspondance (LUT). Cette approche vise à améliorer la précision et la rapidité de la lecture des qubits, essentielle pour la correction d'erreurs quantiques et les processus de décodage à faible latence.

A new architecture named LUNA has been proposed to enhance qubit readout in quantum computing, utilizing a combination of low-cost integrator-based preprocessing and Look-Up Table (LUT) based neural networks. This approach aims to improve the accuracy and speed of qubit readout, which is essential for quantum error correction and low-latency decoding processes.

LUNA: LUT-Based Neural Architecture for Fast and Low-Cost Qubit Readout

arXiv:2512.08061v1 Announce Type: cross 
Abstract: Scaling attention faces a critical bottleneck: the $\mathcal{O}(n^2)$ quadratic computational cost of softmax attention, which limits its application in long-sequence domains. While linear attention mechanisms reduce this cost to $\mathcal{O}(n)$, they typically rely on fixed random feature maps, such as random Fourier features or hand-crafted functions. This reliance on static, data-agnostic kernels creates a fundamental trade-off, forcing practitioners to sacrifice significant model accuracy for computational efficiency. We introduce \textsc{LUNA}, a kernelized linear attention mechanism that eliminates this trade-off, retaining linear cost while matching and surpassing the accuracy of quadratic attention. \textsc{LUNA} is built on the key insight that the kernel feature map itself should be learned rather than fixed a priori. By parameterizing the kernel, \textsc{LUNA} learns a feature basis tailored to the specific data and task, overcoming the expressive limitations of fixed-feature methods. \textsc{Luna} implements this with a learnable feature map that induces a positive-definite kernel and admits a streaming form, yielding linear time and memory scaling in the sequence length. Empirical evaluations validate our approach across diverse settings. On the Long Range Arena (LRA), \textsc{Luna} achieves state-of-the-art average accuracy among efficient Transformers under compute parity, using the same parameter count, training steps, and approximate FLOPs. \textsc{Luna} also excels at post-hoc conversion: replacing softmax in fine-tuned BERT and ViT-B/16 checkpoints and briefly fine-tuning recovers most of the original performance, substantially outperforming fixed linearizations.

تم تقديم آلية انتباه خطية جديدة تُدعى LUNA، والتي تعالج عنق الزجاجة الحاسوبي لآلية الانتباه softmax التقليدية، التي تعمل بتكلفة تربيعية. تحقق LUNA تكلفة خطية مع الحفاظ على دقة تعادل أو تتجاوز دقة الانتباه التربيعي من خلال تعلم خريطة ميزات النواة المخصصة للبيانات والمهام المحددة.

Se ha introducido un nuevo mecanismo de atención lineal llamado LUNA, que aborda el cuello de botella computacional de la atención softmax tradicional, que opera a un costo cuadrático. LUNA logra un costo lineal mientras mantiene o supera la precisión de la atención cuadrática al aprender el mapa de características del núcleo adaptado a datos y tareas específicas.

Un nouveau mécanisme d'attention linéaire nommé LUNA a été introduit, abordant le goulot d'étranglement computationnel de l'attention softmax traditionnelle, qui fonctionne à un coût quadratique. LUNA atteint un coût linéaire tout en maintenant ou en dépassant la précision de l'attention quadratique en apprenant la carte de caractéristiques du noyau adaptée à des données et tâches spécifiques.

A new linear attention mechanism named LUNA has been introduced, addressing the computational bottleneck of traditional softmax attention, which operates at a quadratic cost. LUNA achieves linear cost while maintaining or exceeding the accuracy of quadratic attention by learning the kernel feature map tailored to specific data and tasks.

LUNA: LUT-Based Neural Architecture for Fast and Low-Cost Qubit Readout

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