Work

Input Redundancy for Parameterized Quantum Circuits

Year: 2020

Type: article

Abstract: In this paper we deal with a special type of parameterized quantum circuits, the so-called quantum neural networks or QNN's, which are trained to estimate a given function, specifically the type of circuits proposed by Mitarai et al.\ (Phys.\ Rev.\ A, 2018; see below). 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.

Source: Frontiers in Physics

DOI: 10.3389/fphy.2020.00297

Authors: Francisco Javier Gil Vidal, Dirk Oliver Theis

Institution: University of Tartu

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,490

Funders: Eesti Teadusagentuur, European Regional Development Fund