arXiv:2410.07961v2 Announce Type: replace-cross 
Abstract: Quantum computing is an emerging field recognized for the significant speedup it offers over classical computing through quantum algorithms. However, designing and implementing quantum algorithms pose challenges due to the complex nature of quantum mechanics and the necessity for precise control over quantum states. Despite the significant advancements in AI, there has been a lack of datasets specifically tailored for this purpose. In this work, we introduce QCircuitBench, the first benchmark dataset designed to evaluate AI's capability in designing and implementing quantum algorithms using quantum programming languages. Unlike using AI for writing traditional codes, this task is fundamentally more complicated due to highly flexible design space. Our key contributions include: 1. A general framework which formulates the key features of quantum algorithm design for Large Language Models. 2. Implementations for quantum algorithms from basic primitives to advanced applications, spanning 3 task suites, 25 algorithms, and 120,290 data points. 3. Automatic validation and verification functions, allowing for iterative evaluation and interactive reasoning without human inspection. 4. Promising potential as a training dataset through preliminary fine-tuning results. We observed several interesting experimental phenomena: LLMs tend to exhibit consistent error patterns, and fine-tuning does not always outperform few-shot learning. In all, QCircuitBench is a comprehensive benchmark for LLM-driven quantum algorithm design, and it reveals limitations of LLMs in this domain.

QCircuitBench هو مجموعة بيانات رائدة مصممة لتعزيز تقييم تصميم الخوارزميات الكمومية، مما يعالج فجوة حاسمة في مجال الحوسبة الكمومية. حيث تعد الخوارزميات الكمومية بميزات كبيرة مقارنة بالطرق التقليدية، فإن وجود مجموعة بيانات مخصصة سيسهل البحث والتطوير، مما يسرع من التقدم في هذا المجال المثير من التكنولوجيا. هذه المبادرة مهمة بشكل خاص لأنها تجمع بين تعقيدات الميكانيكا الكمومية والدقة المطلوبة لتنفيذ الخوارزميات بشكل فعال.

QCircuitBench es un conjunto de datos innovador diseñado para mejorar la evaluación del diseño de algoritmos cuánticos, abordando una brecha crítica en el campo de la computación cuántica. Dado que los algoritmos cuánticos prometen ventajas significativas sobre los métodos clásicos, contar con un conjunto de datos dedicado facilitará la investigación y el desarrollo, acelerando así los avances en esta emocionante área de la tecnología. Esta iniciativa es especialmente importante ya que combina las complejidades de la mecánica cuántica con la precisión necesaria para la implementación efectiva de algoritmos.

QCircuitBench est un ensemble de données révolutionnaire conçu pour améliorer l'évaluation de la conception d'algorithmes quantiques, comblant une lacune essentielle dans le domaine de l'informatique quantique. Alors que les algorithmes quantiques promettent des avantages significatifs par rapport aux méthodes classiques, disposer d'un ensemble de données dédié facilitera la recherche et le développement, accélérant ainsi les avancées dans ce domaine technologique passionnant. Cette initiative est particulièrement importante car elle combine les complexités de la mécanique quantique avec la précision requise pour une mise en œuvre efficace des algorithmes.

QCircuitBench is a groundbreaking dataset designed to enhance the benchmarking of quantum algorithm design, addressing a critical gap in the field of quantum computing. As quantum algorithms promise significant advantages over classical methods, having a dedicated dataset will facilitate better research and development, ultimately accelerating advancements in this exciting area of technology. This initiative is particularly important as it combines the complexities of quantum mechanics with the precision required for effective algorithm implementation.

QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Design

arXiv:2511.17677v1 Announce Type: new 
Abstract: Fine-tuning BERT for text classification can be computationally challenging and requires careful hyper-parameter tuning. Recent studies have highlighted the potential of quantum algorithms to outperform conventional methods in machine learning and text classification tasks. In this work, we propose a hybrid approach that integrates an n-qubit quantum circuit with a classical BERT model for text classification. We evaluate the performance of the fine-tuned classical-quantum BERT and demonstrate its feasibility as well as its potential in advancing this research area. Our experimental results show that the proposed hybrid model achieves performance that is competitive with, and in some cases better than, the classical baselines on standard benchmark datasets. Furthermore, our approach demonstrates the adaptability of classical-quantum models for fine-tuning pre-trained models across diverse datasets. Overall, the hybrid model highlights the promise of quantum computing in achieving improved performance for text classification tasks.

تشير دراسة جديدة إلى نموذج هجين يجمع بين بنية BERT التقليدية ودائرة كمومية ذات n-qubits لتصنيف النصوص، مما يعالج التحديات الحسابية المرتبطة بضبط BERT. تشير النتائج التجريبية إلى أن هذا النهج الهجين التقليدي-الكمومي يمكن أن يحقق أداءً تنافسياً مقارنة بالأساليب التقليدية على مجموعات البيانات القياسية.

Un nuevo estudio propone un modelo híbrido que combina una arquitectura BERT clásica con un circuito cuántico de n-qubits para la clasificación de texto, abordando los desafíos computacionales del ajuste fino de BERT. Los resultados experimentales indican que este enfoque clásico-cuántico puede lograr un rendimiento competitivo en comparación con los métodos tradicionales en conjuntos de datos de referencia.

Une nouvelle étude propose un modèle hybride qui combine une architecture BERT classique avec un circuit quantique à n-qubits pour la classification de texte, abordant les défis computationnels liés à l'affinage de BERT. Les résultats expérimentaux indiquent que cette approche classique-quantique peut atteindre des performances compétitives par rapport aux méthodes traditionnelles sur des ensembles de données de référence.

A new study proposes a hybrid model that combines a classical BERT architecture with an n-qubit quantum circuit for text classification, addressing the computational challenges of fine-tuning BERT. The experimental results indicate that this classical-quantum approach can achieve competitive performance compared to traditional methods on benchmark datasets.

QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Design

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