Multi-qubit correction for quantum annealers View Full Text


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Article Info

DATE

2021-08-09

AUTHORS

Ramin Ayanzadeh, John Dorband, Milton Halem, Tim Finin

ABSTRACT

We present multi-qubit correction (MQC) as a novel postprocessing method for quantum annealers that views the evolution in an open-system as a Gibbs sampler and reduces a set of excited states to a new synthetic state with lower energy value. After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels-i.e., a group of qubits that changing their states simultaneously can result in a new state with lower energy value-and successively converges to the ground state. Experimental results using D-Wave 2000Q quantum annealers demonstrate that MQC finds samples with notably lower energy values and improves the reproducibility of results when compared to recent hardware/software advances in the realm of quantum annealing, such as spin-reversal transforms, classical postprocessing techniques, and increased inter-sample delay between successive measurements. More... »

PAGES

16119

References to SciGraph publications

  • 2011-05-11. Quantum annealing with manufactured spins in NATURE
  • 2016-08-02. Nested quantum annealing correction in NPJ QUANTUM INFORMATION
  • 2018-11-21. Quantum fluctuation theorem for error diagnostics in quantum annealers in SCIENTIFIC REPORTS
  • 2011. Adaptive Imitation Scheme for Memetic Algorithms in TECHNOLOGICAL INNOVATION FOR SUSTAINABILITY
  • 2018-03-14. Defects in Quantum Computers in SCIENTIFIC REPORTS
  • 2020-05-14. Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata in SCIENTIFIC REPORTS
  • 2013-03-26. Decoherence induced deformation of the ground state in adiabatic quantum computation in SCIENTIFIC REPORTS
  • 2015-12-14. Performance of two different quantum annealing correction codes in QUANTUM INFORMATION PROCESSING
  • 2014-02-06. Error-corrected quantum annealing with hundreds of qubits in NATURE COMMUNICATIONS
  • 2017-05-16. Boosting quantum annealer performance via sample persistence in QUANTUM INFORMATION PROCESSING
  • 2019-11-22. On Post-processing the Results of Quantum Optimizers in THEORY AND PRACTICE OF NATURAL COMPUTING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-021-95482-w

    DOI

    http://dx.doi.org/10.1038/s41598-021-95482-w

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1140287125

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/34373515


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