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 in the form of quantum circuit codes. 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 task for Large Language Models.2. Implementation 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 and interactive evaluation without human inspection.4. Promising potential as a training dataset through primitive fine-tuning results.We observed several interesting experimental phenomena: fine-tuning does not always outperform few-shot learning, and LLMs tend to exhibit consistent error patterns. QCircuitBench provides a comprehensive benchmark for AI-driven quantum algorithm design, while also revealing some limitations of LLMs in this domain.

Adversarial examples are notably characterized by their strong transferability, allowing attackers to craft these examples on their models and subsequently deploy them against other models. This poses significant threats to existing deep learning systems and raises substantial security concerns. While several methods have been developed to enhance transferability, ensemble attacks stand out for their effectiveness. However, previous approaches of ensemble attacks typically rely on simple averaging of logits, probabilities, or losses for ensembling, without delving into the underlying reasons for the improved transferability. In this work, we propose a new approach that makes full use of the information of each surrogate model by regularizing the optimization direction to concurrently attack all surrogate models. This is achieved by promoting cosine similarity between their gradients. Extensive experiments conducted on the ImageNet dataset demonstrate the superior efficacy of our method in enhancing adversarial transferability. Notably, our approach outperforms leading state-of-the-art attackers across 18 discriminative classifiers and adversarially trained models, underscoring its potential in this domain.