Corpus ID: 219530622

An Ensemble Approach for Compressive Sensing with Quantum

@article{Ayanzadeh2020AnEA,
  title={An Ensemble Approach for Compressive Sensing with Quantum},
  author={Ramin Ayanzadeh and M. Halem and Tim Finin},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.04682}
}
  • Ramin Ayanzadeh, M. Halem, Tim Finin
  • Published 2020
  • Physics, Computer Science, Engineering, Mathematics
  • ArXiv
  • We leverage the idea of a statistical ensemble to improve the quality of quantum annealing based binary compressive sensing. Since executing quantum machine instructions on a quantum annealer can result in an excited state, rather than the ground state of the given Hamiltonian, we use different penalty parameters to generate multiple distinct quadratic unconstrained binary optimization (QUBO) functions whose ground state(s) represent a potential solution of the original problem. We then employ… CONTINUE READING
    A Survey on Compressive Sensing: Classical Results and Recent Advancements
    • 11
    • PDF
    Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata
    • 4
    • PDF
    Post-Quantum Error-Correction for Quantum Annealers
    • 1
    • PDF

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 22 REFERENCES
    Compressed Sensing
    • 5,295
    • PDF
    Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
    • 6,044
    • PDF
    Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
    • 13,378
    • PDF
    A Simple Proof of the Restricted Isometry Property for Random Matrices
    • 2,162
    • PDF
    Quantum annealing in the transverse Ising model
    • 577
    • PDF
    The Dantzig selector: Statistical estimation when P is much larger than n
    • 2,971
    • PDF
    SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR
    • 1,914
    • PDF
    On the “degrees of freedom” of the lasso
    • 806
    • PDF
    A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications
    • 122