Tensorized Pauli decomposition algorithm

Fuente: arXiv
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Autores principales: Hantzko, Lukas, Binkowski, Lennart, Gupta, Sabhyata
Formato: Preprint
Publicado: 2023
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author Hantzko, Lukas
Binkowski, Lennart
Gupta, Sabhyata
author_facet Hantzko, Lukas
Binkowski, Lennart
Gupta, Sabhyata
contents This paper introduces a novel general-purpose algorithm for Pauli decomposition that employs matrix slicing and addition rather than expensive matrix multiplication, significantly accelerating the decomposition of multi-qubit matrices. In a detailed complexity analysis, we show that the algorithm admits the best known worst-case scaling and more favorable runtimes for many practical examples. Numerical experiments are provided to validate the asymptotic speed-up already for small instance sizes, underscoring the algorithm's potential significance in the realm of quantum computing and quantum chemistry simulations.
format Preprint
id arxiv_https___arxiv_org_abs_2310_13421
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Tensorized Pauli decomposition algorithm
Hantzko, Lukas
Binkowski, Lennart
Gupta, Sabhyata
Quantum Physics
This paper introduces a novel general-purpose algorithm for Pauli decomposition that employs matrix slicing and addition rather than expensive matrix multiplication, significantly accelerating the decomposition of multi-qubit matrices. In a detailed complexity analysis, we show that the algorithm admits the best known worst-case scaling and more favorable runtimes for many practical examples. Numerical experiments are provided to validate the asymptotic speed-up already for small instance sizes, underscoring the algorithm's potential significance in the realm of quantum computing and quantum chemistry simulations.
title Tensorized Pauli decomposition algorithm
topic Quantum Physics
url https://arxiv.org/abs/2310.13421