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Quantum Machine Learning

Trie-based ranking of quantum many-body states

DOAJ
Authors: Markus Wallerberger, Karsten Held

Year

2022

Paper ID

35539

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

110

Citations

5

Abstract

Ranking bit patterns—finding the index of a given pattern in an ordered sequence—is a major bottleneck in scaling up numerical quantum many-body calculations, as fermionic and hard-core bosonic states translate naturally to bit patterns. Traditionally, ranking is done by bisectioning search, which has poor cache performance on modern machines. We instead propose to use tries (prefix trees), thereby achieving a two- to tenfold speedup in numerical experiments with only moderate memory overhead. For the important problem of ranking permutations, the corresponding tries can be compressed. These compressed “staggered” lookups allow for a considerable speedup while retaining the memory requirements of prior algorithms based on the combinatorial number system.

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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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  • Ranking bit patterns—finding the index of a given pattern in an ordered sequence—is a major bottleneck in scaling up numerical quantum many-body calculations, as fermionic and...

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References & Citation Signals

[1] DOI https://doi.org/10.1103/PhysRevResearch.4.033238 [2] Publisher http://doi.org/10.1103/PhysRevResearch.4.033238

Local Citation Graph (Related-Paper Links)

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External citation index: OpenAlex citation signal • updated 2026-06-09 22:20:20

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