Energy Efficiency of Quantum Statevector Simulation at Scale

TL;DR

This study evaluates the performance and energy efficiency of large-scale quantum statevector simulations of the Quantum Fourier Transform on supercomputers, demonstrating significant energy savings and speed improvements through various optimizations.

Contribution

The paper introduces optimization techniques like cache-blocking and parameter tuning that significantly improve energy efficiency and performance of quantum circuit simulations at scale.

Findings

Energy savings of up to 25% by reducing CPU frequency.

40% faster simulations with combined optimizations.

Halving communication requirements with cache-blocking.

Abstract

Classical simulations are essential for the development of quantum computing, and their exponential scaling can easily fill any modern supercomputer. In this paper we consider the performance and energy consumption of large Quantum Fourier Transform (QFT) simulations run on ARCHER2, the UK's National Supercomputing Service, with QuEST toolkit. We take into account CPU clock frequency and node memory size, and use cache-blocking to rearrange the circuit, which minimises communications. We find that using 2.00GHz instead of 2.25GHz can save as much as 25% of energy at 5% increase in runtime. Higher node memory also has the potential to be more efficient, and cost the user fewer CUs, but at higher runtime penalty. Finally, we present a cache-blocking QFT circuit, which halves the required communication. All our optimisations combined result in 40% faster simulations and 35% energy savings in 44 qubit simulations on 4,096 ARCHER2 nodes.