Quantum search with prior knowledge

TL;DR

This paper develops a quantum search framework utilizing prior knowledge of network structures, combining amplitude amplification and phase estimation to achieve exponential speed-up in complex networks.

Contribution

It introduces a novel quantum search algorithm that incorporates network-specific phase shifts, enabling more efficient exploration of structured quantum networks.

Findings

Achieves exponential speed-up over traditional quantum search methods.

Demonstrates linear complexity increase in neural network-inspired networks.

Utilizes phase estimation to incorporate network structure into quantum search.

Abstract

The aim of this work is to develop a framework for realising quantum network algorithms with the use of prior knowledge about the structure of the network. We seek to obtain computational methods that allows us to locally determine network properties in a quantum superposition and drive the walk behaviour accordingly. In particular, we consider a network that consists of different types of edges, such that the transitions between nodes result in extra edge-dependent phase shift. We combine amplitude amplification and phase estimation to develop an algorithm for exploring such networks. In the layered neural network inspired case we obtain linear increase of the search complexity with exponential growth of the nodes number. We show that in consequence one is able to perform quantum search algorithms with exponential speed-up compared to quantum search that neglects the extra phase shifts.