This work presents an optimization-based scalable quantum neural network framework for approximating $n$-qubit unitaries through generic parametric representation of unitaries, which are obtained as product of exponential of basis elements of a new basis that we propose as an alternative to Pauli string basis. We call this basis as the Standard Recursive Block Basis, which is constructed using a recursive method, and its elements are permutation-similar to block Hermitian unitary matrices.

翻译：本文提出了一种基于优化的可扩展量子神经网络框架，通过酉矩阵的通用参数化表示来逼近$n$量子比特酉算子。该参数化表示通过指数化一种新基（作为泡利字符串基的替代方案）的基元素乘积获得。我们将此基称为标准递归块基，其采用递归方法构建，且基元素与块厄米酉矩阵经置换相似。