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用于预测量子控制的有效哈密顿量

Effective Hamiltonians for Predictive Quantum Control

Razvan Stanescu, Hugo Ribeiro

arXiv 2607.27111首次发表:更新:

AI 中文总结

该研究针对超导transmon单量子比特门,对比杜芬近似与对角化transmon模型的控制脉冲,揭示模型差异会影响校正效果,提出纳入漏通路的扩展校正框架以提升门性能。

AI 中文摘要

高保真量子控制依赖于受驱动动力学的精确模型。我们通过比较从标准杜芬近似(Duffing approximation)和通过对transmon本征基对角化构造的哈密顿量得到的控制脉冲,研究超导transmon中单量子比特门的这一要求。对两种模型使用相同的校正脉冲构造,我们表明,从杜芬近似得到的校正场可大幅降低该模型预测的门误差,但在与对角化transmon模型中独立校准的基线脉冲结合时效果较差。在快速门机制中,这种转移的校正甚至可能无法优于未校正的对角化transmon基线。我们证明,能谱和驱动算符表示中与模型相关的微小差异会在受驱动演化过程中累积,导致不同的预测误差发生器和校正脉冲。累积的交流斯塔克相位失配是这种动力学模型依赖性的一个说明性诊断。我们进一步证明,模型哈密顿量会影响控制框架的选择:忽略相关漏通路或高阶误差通道会导致校正策略过于受限,而纳入这些通道则催生了扩展校正框架,该框架在使用相同物理控制资源的情况下提升了门性能。

英文摘要

High-fidelity quantum control relies on accurate models of driven dynamics. We examine this re- quirement for single-qubit gates in superconducting transmons by comparing control pulses derived from the standard Duffing approximation and from a Hamiltonian constructed by diagonalizing the transmon eigenbasis. Using the same correction-pulse construction for both models, we show that correction fields derived from the Duffing approximation can substantially reduce the gate error pre- dicted by that model while remaining less effective when combined with an independently calibrated baseline pulse in the diagonalized-transmon model. In the fast-gate regime, such transferred correc- tions can even fail to improve over the uncorrected diagonalized-transmon baseline. We show that small model-dependent differences in both the energy spectrum and the representation of the drive operator can compound during driven evolution, resulting in different predicted error generators and correction pulses. A mismatch in the accumulated AC Stark phase provides one illustrative di- agnostic of this dynamical model dependence. We further demonstrate that the model Hamiltonian informs the choice of control framework: Omitting relevant leakage pathways or higher-order error channels can lead to an overly restricted correction strategy. Including these channels motivates an extended correction framework that improves the gate performance using the same physical control resources.

Comments18 pages, 9 figures

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