Classical information loading is an essential task for many processing
quantum algorithms, constituting a cornerstone in the field of quantum machine
learning. In particular, the embedding techniques based on Hamiltonian
simulation techniques enable the loading of matrices into quantum computers. A
representative example of these methods is the Lloyd-Mohseni-Rebentrost
protocol, which efficiently implements matrix exponentiation when multiple
copies of a quantum state are available. However, this is a quite ideal set up,
and in a realistic scenario, the copies are limited and the non-cloning theorem
prevents from producing more exact copies in order to increase the accuracy of
the protocol. Here, we propose a method to circumvent this limitation by
introducing imperfect quantum copies that significantly enhance the performance
of previous proposals