Input Redundancy for Parameterized Quantum Circuits
Quantum Physics
2020-06-25 v2 Machine Learning
Abstract
The topic area of this paper parameterized quantum circuits (quantum neural networks) which are trained to estimate a given function, specifically the type of circuits proposed by Mitarai et al. (Phys. Rev. A, 2018). The input is encoded into amplitudes of states of qubits. The no-cloning principle of quantum mechanics suggests that there is an advantage in redundantly encoding the input value several times. We follow this suggestion and prove lower bounds on the number of redundant copies for two types of input encoding. We draw conclusions for the architecture design of QNNs.
Cite
@article{arxiv.1901.11434,
title = {Input Redundancy for Parameterized Quantum Circuits},
author = {Javier Gil Vidal and Dirk Oliver Theis},
journal= {arXiv preprint arXiv:1901.11434},
year = {2020}
}
Comments
13p