Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2110.15153 · Quantum Information Processing

The effect of quantum noise on algorithmic perfect quantum state transfer on NISQ processors

Бабухин Данила Валерьевич, W. V. Pogosov

Published Dec 23, 2021• 18 citations
arXivHTMLPDF
DOI
10.1007/s11128-021-03346-z

Related papers

Characterization of State-dependent Noise in NISQ Processors

Ronald J. Sadlier, Travis S. Humble

Bulletin of the American Physical Society•Mar 2, 2020•1 citation

Hybrid Grover Search for AUD on a NISQ Device

Romain Piron, Claire Goursaud

This paper focuses on the AUD in a NOMA network. To do so with a reduced complexity, we propose a new strategy based on Grover's quantum algorithm to perform the minimum searching so as to implement the ML receiver. As current quantum processors still suffer from noise in the so-called NISQ era, we propose to use Grover's routine with a reduced number of iterations but with several trials. We show that this approach presents a complexity advantage and allows to reach higher success probabilities than Grover's. This strategy may permit to timely deploy such AUD receiver even without perfect quantum (devices.

European Signal Processing Conference (EUSIPCO)•Aug 26, 2024•2 citations

QUANTUM-ASSISTED ALGORITHMS FOR DIGITAL QUANTUM COMPUTERS IN THE NISQ ERA

LAU WEI ZHONG JONATHAN

•Mar 5, 2024•0 citations

Noise-Induced Transition in Optimal Solutions of Variational Quantum Algorithms

Cheong Li, Imanol Hernandez

Variational quantum algorithms are promising candidates for delivering practical quantum advantage on noisy intermediate-scale quantum (NISQ) hardware. However, optimizing the noisy cost functions associated with these algorithms is challenging for system sizes relevant to quantum advantage. In this work, we investigate the effect of noise on optimization by studying a variational quantum eigensolver (VQE) algorithm calculating the ground state of a spin chain model, and we observe an abrupt transition induced by noise to the optimal solutions. We will present numerical simulations, a demonstration using an IBM quantum processor unit (QPU), and a theoretical analysis indicating the origin of this transition. Our findings suggest that careful analysis is crucial to avoid misinterpreting the noise-induced features as genuine algorithm results.

arXiv:2403.02762•Mar 6, 2024•0 citations

Benchmarking NISQ-era quantum processors

Jay Gambetta

Bulletin of the American Physical Society•Mar 8, 2019•1 citation

Noisy intermediate scale quantum NISQ computing

Charles R. Giardina

Elsevier eBooks•Jan 1, 2025•1 citation
This paper focuses on the AUD in a NOMA network. To do so with a reduced complexity, we propose a new strategy based on Grover's quantum algorithm to perform the minimum searching so as to implement the ML receiver. As current quantum processors still suffer from noise in the so-called NISQ era, we propose to use Grover's routine with a reduced number of iterations but with several trials. We show that this approach presents a complexity advantage and allows to reach higher success probabilities than Grover's. This strategy may permit to timely deploy such AUD receiver even without perfect quantum (devices.
Variational quantum algorithms are promising candidates for delivering practical quantum advantage on noisy intermediate-scale quantum (NISQ) hardware. However, optimizing the noisy cost functions associated with these algorithms is challenging for system sizes relevant to quantum advantage. In this work, we investigate the effect of noise on optimization by studying a variational quantum eigensolver (VQE) algorithm calculating the ground state of a spin chain model, and we observe an abrupt transition induced by noise to the optimal solutions. We will present numerical simulations, a demonstration using an IBM quantum processor unit (QPU), and a theoretical analysis indicating the origin of this transition. Our findings suggest that careful analysis is crucial to avoid misinterpreting the noise-induced features as genuine algorithm results.