Superconducting quantum devices

Abstract:

We present a framework for implementing two-qubit entangling operations between distant superconducting qubits using a space-time modulated Josephson junction (JJ) metasurface. By modulating the surface in both space and time, we engineer sidebands with controllable wavevectors that selectively couple target qubits. The metasurface acts as a reconfigurable coupling medium, where the interaction strength is determined by engineered transmission coefficients $T_{\mu}\left(\mathrm{k}_{\mu} \cdot \mathrm{r}\right)$ rather than by exponentially decaying near-field coupling, thus reducing the dependence on physical proximity. We investigated the implementation of two-qubit interactions via iSWAP gates driven resonantly through the metasurface and controlled phase gates via geometric phase accumulation. Simulations show entangling fidelity exceeding 98% maintained over centimeter-scale separations.

Abstract:

Large language models (LLMs) excel in generating unstructured text. However, they struggle with producing structured output while maintaining accuracy in zero-shot information extraction (IE), such as named entity recognition (NER) and relation extraction (RE). To address these challenges, we propose CROSSAGENTIE, a multi-agent framework that enhances zero-shot IE through multi-agent LLM collaboration. CROSSAGENTIE refines LLM predictions iteratively through two mechanisms: intra-group cross-type debate, which resolves entity-label conflicts through context-based evidence and confidence aggregation, and inter-group cross-task debate, where NER and RE mutually refine outputs via bidirectional feedback. Furthermore, we introduce template finetuning, distilling high-confidence multi-agent outputs into a single model, significantly reducing inference costs while preserving accuracy. Experiments across five NER and five RE datasets show that CROSSAGENTIE significantly outperforms state-of-the-art zero-shot baselines by a large margin. CROSSAGENTIE effectively addresses LLM limitations in structured prediction with an effective and efficient approach for zero-shot information extraction. Our GitHub can be found at https://github.com/Luca/CorssAgentIE.