We develop computationally affordable and encoding independent gradient evaluation procedures for unitary coupled-cluster type operators, applicable on quantum computers. We show that, within our framework, the gradient of an expectation value with respect to a parameterized n-fold fermionic excitation can be evaluated by four expectation values of similar form and size, whereas most standard approaches, based on the direct application of the parameter-shift-rule, come with an associated cost of expectation values. For real wavefunctions, this cost can be further reduced to two expectation values. Our strategies are implemented within the open-source package Tequila and allow blackboard style construction of differentiable objective functions. We illustrate initial applications through extended adaptive approaches for electronic ground and excited states.

中文翻译：

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量子计算机上自动可微酉耦合簇的可行方法

我们为酉耦合簇类型算子开发了计算上可负担且编码独立的梯度评估程序，适用于量子计算机。我们表明，在我们的框架内，相对于参数化n倍费米子激励的期望值的梯度可以通过四个相似形式和大小的期望值来评估，而大多数标准方法基于参数的直接应用-shift-rule，伴随着期望值的相关成本。对于真实的波函数，这个成本可以进一步减少到两个期望值。我们的策略在开源包 Tequila 中实现，并允许黑板式构建可微的目标函数。我们通过电子基态和激发态的扩展自适应方法来说明初始应用。