Literature DB >> 23811779

Experimental signature of programmable quantum annealing.

Sergio Boixo1, Tameem Albash, Federico M Spedalieri, Nicholas Chancellor, Daniel A Lidar.   

Abstract

Quantum annealing is a general strategy for solving difficult optimization problems with the aid of quantum adiabatic evolution. Both analytical and numerical evidence suggests that under idealized, closed system conditions, quantum annealing can outperform classical thermalization-based algorithms such as simulated annealing. Current engineered quantum annealing devices have a decoherence timescale which is orders of magnitude shorter than the adiabatic evolution time. Do they effectively perform classical thermalization when coupled to a decohering thermal environment? Here we present an experimental signature which is consistent with quantum annealing, and at the same time inconsistent with classical thermalization. Our experiment uses groups of eight superconducting flux qubits with programmable spin-spin couplings, embedded on a commercially available chip with >100 functional qubits. This suggests that programmable quantum devices, scalable with current superconducting technology, implement quantum annealing with a surprising robustness against noise and imperfections.

Year:  2013        PMID: 23811779     DOI: 10.1038/ncomms3067

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


  19 in total

1.  Experimental quantum annealing: case study involving the graph isomorphism problem.

Authors:  Kenneth M Zick; Omar Shehab; Matthew French
Journal:  Sci Rep       Date:  2015-06-08       Impact factor: 4.379

2.  Quantum Simulation of Dissipative Processes without Reservoir Engineering.

Authors:  R Di Candia; J S Pedernales; A del Campo; E Solano; J Casanova
Journal:  Sci Rep       Date:  2015-05-29       Impact factor: 4.379

3.  Hearing the shape of the Ising model with a programmable superconducting-flux annealer.

Authors:  Walter Vinci; Klas Markström; Sergio Boixo; Aidan Roy; Federico M Spedalieri; Paul A Warburton; Simone Severini
Journal:  Sci Rep       Date:  2014-07-16       Impact factor: 4.379

4.  Solving Set Cover with Pairs Problem using Quantum Annealing.

Authors:  Yudong Cao; Shuxian Jiang; Debbie Perouli; Sabre Kais
Journal:  Sci Rep       Date:  2016-09-27       Impact factor: 4.379

5.  Superadiabatic Controlled Evolutions and Universal Quantum Computation.

Authors:  Alan C Santos; Marcelo S Sarandy
Journal:  Sci Rep       Date:  2015-10-29       Impact factor: 4.379

6.  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

7.  A quantum annealing architecture with all-to-all connectivity from local interactions.

Authors:  Wolfgang Lechner; Philipp Hauke; Peter Zoller
Journal:  Sci Adv       Date:  2015-10-23       Impact factor: 14.136

8.  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

9.  A 16-bit Coherent Ising Machine for One-Dimensional Ring and Cubic Graph Problems.

Authors:  Kenta Takata; Alireza Marandi; Ryan Hamerly; Yoshitaka Haribara; Daiki Maruo; Shuhei Tamate; Hiromasa Sakaguchi; Shoko Utsunomiya; Yoshihisa Yamamoto
Journal:  Sci Rep       Date:  2016-09-23       Impact factor: 4.379

10.  Computational multiqubit tunnelling in programmable quantum annealers.

Authors:  Sergio Boixo; Vadim N Smelyanskiy; Alireza Shabani; Sergei V Isakov; Mark Dykman; Vasil S Denchev; Mohammad H Amin; Anatoly Yu Smirnov; Masoud Mohseni; Hartmut Neven
Journal:  Nat Commun       Date:  2016-01-07       Impact factor: 14.919

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