Solving optimization problems with Rydberg analog quantum computers
post-template-default,single,single-post,postid-7974,single-format-standard,mkd-core-1.0.3,highrise child-child-ver-1.0.0,highrise-ver-1.5,,mkd-smooth-page-transitions,mkd-ajax,mkd-grid-1300,mkd-blog-installed,mkd-header-standard,mkd-sticky-header-on-scroll-down-up,mkd-default-mobile-header,mkd-sticky-up-mobile-header,mkd-dropdown-slide-from-bottom,mkd-dark-header,mkd-full-width-wide-menu,mkd-header-standard-in-grid-shadow-disable,mkd-search-covers-header,wpb-js-composer js-comp-ver-6.9.0,vc_responsive


Solving optimization problems with Rydberg analog quantum computers

In a recent article published by the American Physical Society’s Physical Review A, the Atos Quantum Lab (Michel Fabrice Serret, Bertrand Marchand, Thomas Ayral) investigates the potential of Rydberg analog quantum computers (a promising quantum platform constructed for instance at IOGS or at Pasqal) for solving NP-hard optimization problems better than state-of-the-art classical approximation algorithms. Using the Atos Quantum Learning Machine to perform realistic noisy simulations of Rydberg systems with an unprecedented number of atoms, they estimate that with the currently implementable algorithms, thousands of atoms, or substantially improved coherence properties, are required to reach quantum advantage within a reasonable time budget… unless new algorithms emerge that are both implementable on current or near-term hardware and yield higher success rates.


Michel Fabrice Serret, Bertrand Marchand, and Thomas Ayral: “Solving optimization problems with Rydberg analog quantum computers: Realistic requirements for quantum advantage using noisy simulation and classical benchmarks”. Phys.Rev.A 102, 052617 – Published 23 November 2020