arXiv:2606. 13811v1 Announce Type: cross Abstract: Can Large Language Models (LLMs) understand and reason about quantum operators?
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2607. 15313v1 Announce Type: new Abstract: The scaling hypothesis assumes that increasing model parameters yields emergent reasoning capabilities.
arXiv:2606. 13380v1 Announce Type: cross Abstract: The design of high performing quantum circuits remains largely dependent on human expertise.
arXiv:2605. 25572v2 Announce Type: replace-cross Abstract: The growing complexity of quantum programming frameworks has exposed a critical limitation in existing large language model (LLM)-based code assistants: general-purpose models hallucinate PennyLane-specific gate names, misplace device configurations, and produce structurally invalid circuits when faced with specialized quantum coding challenges.
arXiv:2508. 20134v2 Announce Type: replace Abstract: Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices, yet it remains challenging due to the need for domain-specific planning, iterative code synthesis, and low-level calibration.
arXiv:2410. 17397v2 Announce Type: replace-cross Abstract: We introduce a framework for seamlessly integrating quantum computing into pretrained large language models (LLMs).
arXiv:2308. 11290v2 Announce Type: replace-cross Abstract: Understanding the dynamics of large quantum systems is hindered by the curse of dimensionality.