Variational quantum algorithms (VQAs) have demonstrated great potentials in the Noisy Intermediate Scale Quantum (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. Some works consider the physical meaning of the underlying circuits, while others adopt the ideas of neural architecture search (NAS) for ansatz generator. However, these designs do not exploit the full advantages of VQAs. Because most techniques target gate ansatz, and the parameters are usually rotation angles of the gates. In quantum computers, the gate ansatz will eventually be transformed into control signals such as microwave pulses on superconducting qubits. These control pulses need elaborate calibrations 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 NAPA, a native-pulse ansatz generator framework for VQAs. We generate native-pulse ansatz with trainable parameters for amplitudes and frequencies. In our proposed NAPA, we are tuning parametric pulses, which are natively supported on NISQ computers. Given the limited availability of gradient-based optimizers for pulse-level quantum programs, we choose to deploy non-gradient optimizers in our framework. 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 for Variational Quantum Eigensolver (VQE) tasks to evaluate our methods.

翻译：变分量子算法（VQAs）在含噪声中等规模量子（NISQ）时代展现出巨大潜力。在VQA的工作流程中，拟设的参数通过迭代更新以逼近目标量子态。已有诸多研究致力于设计更优拟设以降低门操作数量。部分工作考虑了底层电路的物理意义，另一些则借鉴神经架构搜索（NAS）思想构建拟设生成器。然而，这些设计并未充分发挥VQA的优势。因为多数技术针对门级拟设，参数通常为量子门的旋转角度。在量子计算机中，门级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与欠旋转等误差。在VQA场景中，此过程会引入冗余，而VQA的变分特性天然可通过更新振幅与频率参数处理过旋转与欠旋转问题。为此，我们提出NAPA——一种面向VQA的本征脉冲拟设生成框架。我们生成具有振幅与频率可训练参数的本征脉冲拟设。在NAPA中，我们直接调控NISQ计算机原生支持的参数化脉冲。鉴于脉冲级量子程序梯度优化器的可用性有限，我们选择在框架中部署非梯度优化器。为限制传递给优化器的参数数量，我们采用渐进式方式生成本征脉冲拟设。我们在模拟器与量子设备上针对变分量子特征求解器（VQE）任务开展实验，以评估所提方法。