QuEST and Intel-QS Quantum Computation Simulation Packages Performance Comparison

Authors

  • Alexey V. Liniov Lobachevsky State University of Nizhny Novgorod
  • Pavel E. Vedrukov Lobachevsky State University of Nizhny Novgorod
  • Denis S. Kulandin Lobachevsky State University of Nizhny Novgorod
  • Iosif B. Meyerov Lobachevsky State University of Nizhny Novgorod
  • Sergey Denisov Lobachevsky State University of Nizhny Novgorod, Oslo Metropolitan University

DOI:

https://doi.org/10.14529/cmse210104

Keywords:

computational quantum physics, quantum algorithms, high-performance computing, GPGPU, QuEST, Intel-QS

Abstract

In the nearest future quantum computers will be suitable for practical use. The development of quantum algorithms can be carried out using classical computers and specialized software that allows simulating of a quantum circuit functioning. Simulation results can be used to analyze the algorithm and also contribute to co-design when developing quantum architectures. However, when planning and performing numerical experiments, it is necessary to understand the capabilities of simulators and the limitations on the parameters of the quantum circuit imposed by the characteristics of the available classical computational resources (computers). This paper presents the results of computational experiments on simulating the operation of quantum circuits on an ideal quantum computer using the QuEST and Intel-QS packages, as well as our own "naïve" implementation. Restrictions on the size of a simulated quantum system N are shown when using computing systems of various classes — a virtual machine, a computing server, a computing server with a graphics accelerator (GPU), a supercomputer (the maximum achieved size is N = 33). The performance and scalability characteristics of the considered implementations on shared and distributed memory are given (the observed scaling efficiency is 30 % and 70 %, respectively). For the QuEST package and our own implementation the performance is presented for systems with graphics accelerator (GPU).

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Published

2021-02-10

Issue

Section

Supercomputer Modeling