AI 中文总结
研究超导量子芯片建模问题，提出quchip开源Python工具包，它能明确表示器件各部分并组装模拟，在五器件模型上演示实验循环，可实现预测、校正及逆参数恢复，有效抑制串扰、降低希尔伯特空间维度。
AI 中文摘要
超导量子芯片的预测建模所需的不仅仅是哈密顿量：该模型必须连接器件物理、控制线变换、选定的框架和近似、耗散以及测量的可观测量。我们展示了quchip，一个开源的Python工具包，它明确表示这些部分，并为QuTiP或dynamiqs组装独立于后端的模拟；对于dynamiqs，器件和控制参数在求解过程中保持可微。我们在一个拟合到修饰可观测量的五器件模型上演示了由此产生的实验循环。模拟的相位扫描识别出两条控制线之间的复杂串扰，并且推断响应的反转将有效泄漏抑制了两个以上数量级。在十六个同时的π脉冲上，校正后的脉冲末端种群与无串扰响应的偏差保持在0.3个百分点以内。绝热消除总线和读出谐振器将希尔伯特空间维度从576减少到16，之后通过模拟断层扫描的梯度恢复了四个注入的串扰参数，最大复误差为1.5×10⁻⁵。因此，一个单一的显式模型可以支持预测、校正和逆参数恢复。
英文摘要
Predictive modeling of a superconducting quantum chip requires more than a Hamiltonian: the model must connect device physics, control-line transformations, chosen frames and approximations, dissipation, and measured observables. We present quchip, an open-source Python toolkit that represents these parts explicitly and assembles backend-independent simulations for QuTiP or dynamiqs; with dynamiqs, device and control parameters remain differentiable through the solve. We demonstrate the resulting experimental loop on a five-device model fitted to dressed observables. Simulated phase sweeps identify the complex crosstalk between two control lines, and inversion of the inferred response suppresses the effective leakage by more than two orders of magnitude. Over sixteen simultaneous $π$ pulses, the corrected pulse-end populations remain within $0.3$ percentage points of the crosstalk-free response. Adiabatically eliminating the bus and readout resonators reduces the Hilbert-space dimension from 576 to 16, after which gradients through simulated tomography recover the four injected crosstalk parameters with a maximum complex error of $1.5\times10^{-5}$. A single explicit model can therefore support prediction, correction, and inverse parameter recovery.
Comments13 pages, 7 figures, 1 table. Source code: https://github.com/quchip/quchip
