通过正则化回归解析分子量子比特中的自旋-声子弛豫路径
Resolving Spin-Phonon Relaxation Pathways in Molecular Qubits via Regularized Regression
浏览论文内容
中文总结 AI 辅助
本文提出正则化回归增强的第一性原理方法，应用于Cu卟啉自旋-1/2分子量子比特，成功复现实验趋势并预测弛豫-温度曲线曲率，为构建长寿命分子量子比特提供通用策略。
中文摘要 AI 辅助
设计分子量子比特需要控制从根本上限制相干时间的自旋-晶格弛豫路径。对于自旋-1/2分子量子比特，自旋-声子弛豫路径的理论预测与实验测量之间仍存在显著差异，理论往往高估低频振动模式的作用。本文提出一种正则化回归增强的替代第一性原理方法，可自动识别自旋-声子弛豫路径，无需特设模式选择。将该方法应用于Cu卟啉自旋-1/2分子量子比特，成功复现弛豫时间的实验趋势，并预测弛豫-温度曲线的曲率。尽管这些量子比特结构相似，g张量时间序列仍揭示出不同的系统特定自相关函数和谱密度分布。进一步，通过分子动力学获得的g张量时间序列与模式投影振动振幅时间序列之间的回归，得到每个g张量分量的线性和双线性模式耦合贡献。由于该方法基于直接从分子动力学采样的原子位移，自然包含非简谐效应，无需简谐近似。该框架提出一种回归驱动、模式分辨且耦合阶分离的自旋-声子分析，作为预测和构建更长寿命分子量子比特的通用策略。
英文摘要
Designing molecular qubits requires controlling the spin-lattice relaxation pathways that fundamentally limit the coherence time. For spin-1/2 molecular qubits, significant discrepancies remain between theoretical predictions and experimental measurements of spin-phonon relaxation pathways, with theory often overestimating the role of low-frequency vibrational modes. Here, we present an alternative first-principles approach augmented with regularized regression that identifies spin-phonon relaxation pathways in an automated fashion without ad hoc mode selection. Applied to Cu porphyrins spin-1/2 molecular qubits, the method successfully reproduces experimental trends in relaxation times and predicts the curvature in the relaxation-vs-temperature profile. The g-tensor time series reveal distinct system-specific autocorrelation functions and spectral-density profiles despite the qubits' structural similarity. Further, regression between g-tensor time series and mode-projected vibrational-amplitude time series obtained from molecular dynamics yields linear and bilinear mode coupling contributions to each g-tensor component. Because the method is based on atomic displacements sampled directly from molecular dynamics, it naturally incorporates anharmonic effects and does not require the harmonic approximation. This framework puts forward a regression-driven, mode-resolved, and coupling-order-separated spin-phonon analysis as a general strategy for predicting and engineering longer-lived molecular qubits.
