An Ensemble Approach for Compressive Sensing with Quantum
Abstract
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 the attained samples from minimizing all corresponding (different) QUBOs to estimate the solution of the problem of binary compressive sensing. Our experiments, on a DWave 2000Q quantum processor, demonstrated that the proposed ensemble scheme is notably less sensitive to the calibration of the penalty parameter that controls the tradeoff between the feasibility and sparsity of recoveries.
 Publication:

arXiv eprints
 Pub Date:
 June 2020
 arXiv:
 arXiv:2006.04682
 Bibcode:
 2020arXiv200604682A
 Keywords:

 Quantum Physics;
 Computer Science  Information Theory;
 Computer Science  Machine Learning;
 Electrical Engineering and Systems Science  Signal Processing