  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.
