Prospect of using Grover's search in the noisy-intermediate-scale quantum-computer era

TL;DR

This paper investigates the effectiveness of Grover's search algorithm on noisy intermediate-scale quantum computers through simulations, establishing noise thresholds and predicting practical error bounds for large data searches.

Contribution

It provides a comparative analysis of standard and modified Grover's algorithms under noise, and predicts error bounds for large-scale quantum data search applications.

Findings

Identifies noise thresholds depending on circuit depth.

Modified algorithms reduce noise sensitivity.

Predicts gate error bounds for large data sets.

Abstract

In order to understand the bounds of utilization of the Grover's search algorithm for the large unstructured data in presence of the quantum computer noise, we undertake a series of simulations by inflicting various types of noise, modelled by the IBM QISKit. We apply three forms of Grover's algorithms: (1) the standard one, with 4-10 qubits, (2) recently published modified Grover's algorithm, set to reduce the circuit depth, and (3) the algorithms in (1) and (2) with multi-control Toffoli's modified by addition of an ancilla qubit. Based on these simulations, we find the upper bound of noise for these cases, establish its dependence on the quantum depth of the circuit and provide comparison among them. By extrapolation of the fitted thresholds, we predict what would be the typical gate error bounds when apply the Grover's algorithms for the search of a data in a data set as large as thirty two thousands.