arXiv:2606. 13380v1 Announce Type: cross Abstract: The design of high performing quantum circuits remains largely dependent on human expertise.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.
arXiv:2606. 13811v1 Announce Type: cross Abstract: Can Large Language Models (LLMs) understand and reason about quantum operators?
arXiv:2608. 12936v1 Announce Type: cross Abstract: As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algorithmic feasibility with system-level optimization across heterogeneous hardware and software stacks.
As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algorithmic feasibility with system-level optimization across heterogeneous hardware and software stacks. Quantum resource estimation (QRE) plays a central role in this transition, yet existing approaches remain largely compilation-heavy or domain-knowledge-guided symbolic annotations, and tightly coupled to long-term fault-tolerant assumptions, limiting their topical applicability.
arXiv:2512. 09586v2 Announce Type: replace-cross Abstract: Quantum circuit design is a key bottleneck for practical quantum machine learning on complex, real-world data.
arXiv:2606. 26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning compact, structured representations.