In the era of noisy intermediate-scale quantum devices, variational quantum algorithms (VQAs) stand as a prominent strategy for constructing quantum machine learning models. These models comprise both a quantum and a classical component. The quantum facet is characterized by a parametrization U, typically derived from the composition of various quantum gates. On the other hand, the classical component involves an optimizer that adjusts the parameters of U to minimize a cost function C. Despite the extensive applications of VQAs, several critical questions persist, such as determining the optimal gate sequence, devising efficient parameter optimization strategies, selecting appropriate cost functions, and understanding the influence of quantum chip architectures on the final results. This article aims to address the last question, emphasizing that, in general, the cost function tends to converge toward an average value as the utilized parameterization approaches a 2-design. Consequently, when the parameterization closely aligns with a 2-design, the quantum neural network model’s outcome becomes less dependent on the specific parametrization. This insight leads to the possibility of leveraging the inherent architecture of quantum chips to define the parametrization for VQAs. By doing so, the need for additional swap gates is mitigated, consequently reducing the depth of VQAs and minimizing associated errors.

在嘈杂的中规模量子设备时代，变分量子算法（VQA）是构建量子机器学习模型的重要策略。这些模型包含量子和经典部分。量子面的特征是参数化U，通常源自各种量子门的组合。另一方面，经典组件涉及一个优化器，该优化器调整U的参数以最小化成本函数C。尽管 VQA 得到了广泛的应用，但仍然存在一些关键问题，例如确定最佳门序列、设计有效的参数优化策略、选择适当的成本函数以及了解量子芯片架构对最终结果的影响。本文旨在解决最后一个问题，强调一般来说，当所使用的参数化接近 2 设计时，成本函数趋向于平均值。因此，当参数化与 2 设计紧密结合时，量子神经网络模型的结果变得更少依赖于特定的参数化。这种见解使得利用量子芯片的固有架构来定义 VQA 的参数化成为可能。通过这样做，可以减少对额外交换门的需求，从而减少 VQA 的深度并最大限度地减少相关错误。