Quantum approximated cloning-assisted density matrix exponentiation

doi:10.1103/PhysRevResearch.7.013264

Quantum approximated cloning-assisted density matrix exponentiation

2025-03-12

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P

Pablo Rodriguez-Grasa *

R

Rubén Ibarrondo

J

Javier Gonzalez-Conde

DOI：10.1103/PhysRevResearch.7.013264

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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 (LMR) protocol, which efficiently implements matrix exponentiation when multiple copies of a quantum state are available. However, this is a quite ideal setup, and in a realistic scenario, the copies are limited and the noncloning theorem prevents one 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, which significantly improve the performance of the LMR when the eigenvectors are known.

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Quantum approximated cloning-assisted density matrix exponentiation

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