arXiv subjects

Javier Gil Vidal

Publications and source records attributed to Javier Gil Vidal.

2 recordsLinked to original sources

arXiv2020

Input Redundancy for Parameterized Quantum Circuits

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.

quant-ph↗

arXiv2018

Calculus on parameterized quantum circuits

Mitarai, Negoro, Kitagawa, and Fujii proposed a type of parameterized quantum circuits, for which they gave a way to estimate derivatives wrt the parameters using only changes in the values of the parameters, not in the circuit itself, i.e., no ancillas or controlled operations. Recently, Schuld et al. have extended the results, but they need to revert to ancillas and controlled operations for some cases. In this note, we extend the types of MiNKiF circuits for which derivatives can be computed without ancillas or controlled operations --- at the cost of a larger number of evaluation points. We also propose a "training" (i.e., optimizing the parameters) which takes advantage of our approach.

quant-ph↗