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We leverage the idea of a statistical ensemble to improve the quality ofquantum annealing based binary compressive sensing. Since executing quantummachine instructions on a quantum annealer can result in an excited state,rather than the ground state of the given Hamiltonian, we use different penaltyparameters to generate multiple distinct quadratic unconstrained binaryoptimization (QUBO) functions whose ground state(s) represent a potentialsolution of the original problem. We then employ the attained samples fromminimizing all corresponding (different) QUBOs to estimate the solution of theproblem of binary compressive sensing. Our experiments, on a D-Wave 2000Qquantum processor, demonstrated that the proposed ensemble scheme is notablyless sensitive to the calibration of the penalty parameter that controls thetrade-off between the feasibility and sparsity of recoveries.

An Ensemble Approach for Compressive Sensing with Quantum