Quantum search by measurements assisted by pre-trained tensor network states for Hamiltonian simulations

Fuente: arXiv
Enregistré dans:
Détails bibliographiques
Auteur principal: Javanmard, Younes
Format: Preprint
Publié: 2024
Sujets:
Accès en ligne:
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
_version_ 1866908866253422592
author Javanmard, Younes
author_facet Javanmard, Younes
contents We present a quantum algorithm for simulating complex many-body systems and finding their ground states, combining the use of tensor networks and density matrix renormalization group (DMRG) techniques. The algorithm is based on von Neumann's measurement prescription, which serves as a conceptual building block for quantum phase estimation. We describe the implementation and simulation of the algorithm, including the estimation of resources required and the use of matrix product operators (MPOs) to represent the Hamiltonian. We highlight the potential applications of the algorithm in simulating quantum spin systems and electronic structure problems.
format Preprint
id arxiv_https___arxiv_org_abs_2407_19348
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Quantum search by measurements assisted by pre-trained tensor network states for Hamiltonian simulations
Javanmard, Younes
Quantum Physics
We present a quantum algorithm for simulating complex many-body systems and finding their ground states, combining the use of tensor networks and density matrix renormalization group (DMRG) techniques. The algorithm is based on von Neumann's measurement prescription, which serves as a conceptual building block for quantum phase estimation. We describe the implementation and simulation of the algorithm, including the estimation of resources required and the use of matrix product operators (MPOs) to represent the Hamiltonian. We highlight the potential applications of the algorithm in simulating quantum spin systems and electronic structure problems.
title Quantum search by measurements assisted by pre-trained tensor network states for Hamiltonian simulations
topic Quantum Physics
url https://arxiv.org/abs/2407.19348