发表机构
Department of Applied Science and Technology, Politecnico di Torino; Quantum Metrology and Nanotechnology Division, Istituto Nazionale di Ricerca Metrologica; Department of Engineering, University of Palermo（都灵理工大学应用科学与技术系; 意大利国家计量科学研究院量子计量与纳米技术部; 巴勒莫大学工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对约瑟夫森电路优化中线性阶段未捕获泵浦效应的问题，提出非线性反馈扩展方法，以反转克尔架构JTWPA为对象验证，发现输入反射系数相关差异可作为反馈可观测量用于优化。
AI 中文摘要
优化基于约瑟夫森的非线性微波器件计算成本高昂，因为直接方法需要通过昂贵的非线性模拟探索广阔的电路参数空间。约瑟夫森电路优化器（Josephson Circuits Optimizer）通过两阶段谐波平衡模拟解决该问题：首先用快速线性模拟根据阻抗、相位匹配等特性筛选有前景的电路配置，随后用非线性模拟优化其工作条件。然而，泵浦诱导的效应（如阻抗重正化和克尔诱导的相位匹配修改）在初始线性阶段未被捕获。因此，我们引入非线性反馈扩展，将泵浦响应的信息传回线性优化过程。该方法采用具有反转克尔架构的约瑟夫森行波参量放大器（JTWPA）进行研究，其模型通过实验增益测量得到验证。随后，我们评估基于输入反射系数的指标在线性响应与泵浦响应值之间的差异，同时排除三次谐波抑制不足的配置。这种差异被视为候选反馈可观测量，因其表现出与增益分布相似的特征，支持其在后续优化周期中的应用。
英文摘要
Optimizing Josephson-based nonlinear microwave devices is computationally demanding because the straightforward approach requires exploring broad circuit parameter spaces through expensive nonlinear simulations. Josephson Circuits Optimizer addresses this problem by using harmonic balance simulations in two stages: fast linear simulations to select promising circuit configurations according to properties such as impedance and phase matching, followed by nonlinear simulations to optimize their operating conditions. However, pump-induced effects such as impedance renormalization and Kerr-induced modifications of phase matching are not captured during the initial linear stage. We therefore introduce a nonlinear feedback extension that transfers information from the pumped response back to the linear optimization. The approach is investigated using a Josephson traveling-wave parametric amplifier with a reversed-Kerr architecture, whose model is validated against experimental gain measurements. We then evaluate the difference between the linear and pumped response values of a metric based on the input reflection coefficient, while excluding configurations with insufficient third-harmonic suppression. This discrepancy is considered a candidate feedback observable, as it exhibits features similar to the gain landscape, supporting its use in subsequent optimization cycles.
Comments4 pages, 4 figures
