Literature DB >> 24500027

Error-corrected quantum annealing with hundreds of qubits.

Kristen L Pudenz1, Tameem Albash2, Daniel A Lidar3.   

Abstract

Quantum information processing offers dramatic speedups, yet is susceptible to decoherence, whereby quantum superpositions decay into mutually exclusive classical alternatives, thus robbing quantum computers of their power. This makes the development of quantum error correction an essential aspect of quantum computing. So far, little is known about protection against decoherence for quantum annealing, a computational paradigm aiming to exploit ground-state quantum dynamics to solve optimization problems more rapidly than is possible classically. Here we develop error correction for quantum annealing and experimentally demonstrate it using antiferromagnetic chains with up to 344 superconducting flux qubits in processors that have recently been shown to physically implement programmable quantum annealing. We demonstrate a substantial improvement over the performance of the processors in the absence of error correction. These results pave the way towards large-scale noise-protected adiabatic quantum optimization devices, although a threshold theorem such as has been established in the circuit model of quantum computing remains elusive.

Year:  2014        PMID: 24500027     DOI: 10.1038/ncomms4243

Source DB:  PubMed          Journal:  Nat Commun        ISSN: 2041-1723            Impact factor:   14.919


  4 in total

1.  Unraveling Quantum Annealers using Classical Hardness.

Authors:  Victor Martin-Mayor; Itay Hen
Journal:  Sci Rep       Date:  2015-10-20       Impact factor: 4.379

2.  Determination and correction of persistent biases in quantum annealers.

Authors:  Alejandro Perdomo-Ortiz; Bryan O'Gorman; Joseph Fluegemann; Rupak Biswas; Vadim N Smelyanskiy
Journal:  Sci Rep       Date:  2016-01-19       Impact factor: 4.379

3.  Adiabatic quantum simulation of quantum chemistry.

Authors:  Ryan Babbush; Peter J Love; Alán Aspuru-Guzik
Journal:  Sci Rep       Date:  2014-10-13       Impact factor: 4.379

4.  Maximum-Entropy Inference with a Programmable Annealer.

Authors:  Nicholas Chancellor; Szilard Szoke; Walter Vinci; Gabriel Aeppli; Paul A Warburton
Journal:  Sci Rep       Date:  2016-03-03       Impact factor: 4.379

  4 in total

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