解析控制的梯度优化：通向高精度量子最优控制的途径
Gradient optimization of analytic controls: the route to high accuracy quantum optimal control
摘要：
量子计算对门的性能提出了非常严格的要求，实验实现需要控制和由此产生的动力学符合硬件特定的约束。超导量子比特提出了额外的要求，即脉冲必须具有简单的参数化，因此它们可以在实验中进一步校准，以补偿系统参数的不确定性。其他量子技术，如传感，需要极高的量子效率。我们提出了一种新的，概念简单，易于实现的基于梯度的最优控制技术命名为梯度优化的解析控制（GOAT），它满足所有上述要求，不同于以前的方法。为了展示GOAT的能力，重点是灵活性和易于随后的校准，我们优化了两种领先的超导量子位架构的快速相干限制脉冲-通量可调transmons和固定频率transmons与可调耦合器。
英文摘要：
Quantum computation places very stringent demands on gate fidelities, and experimental implementations require both the controls and the resultant dynamics to conform to hardware-specific constraints. Superconducting qubits present the additional requirement that pulses must have simple parameterizations, so they can be further calibrated in the experiment, to compensate for uncertainties in system parameters. Other quantum technologies, such as sensing, require extremely high fidelities. We present a novel, conceptually simple and easy-to-implement gradient-based optimal control technique named Gradient Optimization of Analytic conTrols (GOAT), which satisfies all the above requirements, unlike previous approaches. To demonstrate GOAT's capabilities, with emphasis on flexibility and ease of subsequent calibration, we optimize fast coherence-limited pulses for two leading superconducting qubits architectures - flux-tunable transmons and fixed-frequency transmons with tunable couplers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
