Variational quantum algorithms (VQAs) have demonstrated great potentials in the NISQ era. In the workflow of VQA, the parameters of ansatz are iteratively updated to approximate the desired quantum states. We have seen various efforts to draft better ansatz with less gates. In quantum computers, the gate ansatz will eventually be transformed into control signals such as microwave pulses on transmons. And the control pulses need elaborate calibration to minimize the errors such as over-rotation and under-rotation. In the case of VQAs, this procedure will introduce redundancy, but the variational properties of VQAs can naturally handle problems of over-rotation and under-rotation by updating the amplitude and frequency parameters. Therefore, we propose PAN, a native-pulse ansatz generator framework for VQAs. We generate native-pulse ansatz with trainable parameters for amplitudes and frequencies. In our proposed PAN, we are tuning parametric pulses, which are natively supported on NISQ computers. Considering that parameter-shift rules do not hold for native-pulse ansatz, we need to deploy non-gradient optimizers. To constrain the number of parameters sent to the optimizer, we adopt a progressive way to generate our native-pulse ansatz. Experiments are conducted on both simulators and quantum devices to validate our methods. When adopted on NISQ machines, PAN obtained improved the performance with decreased latency by an average of 86%. PAN is able to achieve 96.482% and 99.336% accuracy for VQE tasks on H2 and HeH+ respectively, An average accuracy of 97.27% is achieved for medium-size VQE tasks on CO2, H2O, and NaH. PAN also demonstrates advantages on QAOA tasks even with considerable noises in NISQ machines.

翻译：变分量子算法（VQA）在NISQ时代展现出巨大潜力。在VQA的工作流程中，变分拟设参数通过迭代更新以逼近目标量子态。人们已尝试用更少量子门设计更优的变分拟设。在量子计算机中，门级变分拟设最终需转化为控制信号（如超导量子比特上的微波脉冲），这些控制脉冲需精细校准以最小化过旋转和欠旋转等误差。针对VQA场景，该过程虽会引入冗余，但VQA的变分特性可通过更新幅度与频率参数自然处理过旋转与欠旋转问题。为此，我们提出PAN——一种面向VQA的原生脉冲变分拟设生成框架。该框架生成具有可训练幅度与频率参数的原生脉冲变分拟设，直接利用NISQ计算机原生支持的参数化脉冲。鉴于参数平移规则不适用于原生脉冲变分拟设，我们采用非梯度优化器。为约束优化器参数量，我们采用渐进式方法生成原生脉冲变分拟设。在模拟器与量子设备上的实验验证了方法有效性：搭载于NISQ机器时，PAN平均降低86%延迟并提升性能；在H₂和HeH⁺的VQE任务中分别实现96.482%和99.336%的精度，对CO₂、H₂O、NaH等中型VQE任务平均精度达97.27%。即使在NISQ机器存在显著噪声的条件下，PAN在QAOA任务中仍展现优势。