SQUWALS: A Szegedy QUantum WALks Simulator
Sergio A. Ortega, Miguel A. Martin-Delgado

TL;DR
This paper introduces SQUWALS, a memory-efficient classical simulator for Szegedy's quantum walk, enabling error-free testing of quantum algorithms like quantum PageRank with scalable performance.
Contribution
It presents a novel memory-saving algorithm for simulating Szegedy's quantum walk and extends it to mixed states and semiclassical variants, implemented in the SQUWALS Python package.
Findings
Scales as O(N^2) in time and memory for graph size N
Supports simulation of mixed states and semiclassical walks
Enables high-level applications like quantum PageRank
Abstract
Szegedy's quantum walk is an algorithm for quantizing a general Markov chain. It has plenty of applications such as many variants of optimizations. In order to check its properties in an error-free environment, it is important to have a classical simulator. However, the current simulation algorithms require a great deal of memory due to the particular formulation of this quantum walk. In this paper we propose a memory-saving algorithm that scales as with the size of the graph. We provide additional procedures for simulating Szegedy's quantum walk over mixed states and also the Semiclassical Szegedy walk. With these techniques we have built a classical simulator in Python called SQUWALS. We show that our simulator scales as in both time and memory resources. This package provides some high-level applications for algorithms based on Szegedy's…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Cloud Computing and Resource Management · Machine Learning and ELM
