“Students Maya Hanna, MPH, and Juan-David Rueda, MD, received the Department of Pharmaceutical Health Services Research’s (PHSR) Student Travel Scholarship to present their research at the 2018 International Society for Pharmacoeconomics and Outcomes Research meeting in Europe. Other PHSR awards to graduate students included the Harris Zuckerman Scholarship to Yoon Hong, PharmD, the Donald O. Fedder Memorial Fellowship to Jacquelyn McRae, PharmD, and the Dr. Arthur Schwartz Memorial Scholarship to Chengchen Zhang, MPH.” (pages 8-9)

Please see page 16 regarding our STAR-PREP Fellows. This is outstanding news!

More about this message from the Presiden in the December 2018 edition at the following link: https://www.umaryland.edu/media/umb/president/the-presidents-message/2018-19/December-2018.pdf

Quantum Annealing Based Binary Compressive Sensing with Matrix Uncertainty

Compressive sensing is a novel approach that linearly samples sparse or compressible signals at a rate much below the Nyquist-Shannon sampling rate and outperforms traditional signal processing techniques in acquiring and reconstructing such signals. Compressive sensing with matrix uncertainty is an extension of the standard compressive sensing problem that appears in various applications including but not limited to cognitive radio sensing, calibration of the antenna, and deconvolution. The original problem of compressive sensing is NP-hard so the traditional techniques, such as convex and nonconvex relaxations and greedy algorithms, apply stringent constraints on the measurement matrix to indirectly handle this problem in the realm of classical computing.

We propose well-posed approaches for both binary compressive sensing and binary compressive sensing with matrix uncertainty problems that are tractable by quantum annealers. Our approach formulates an Ising model whose ground state represents a sparse solution for the binary compressive sensing problem and then employs an alternating minimization scheme to tackle the binary compressive sensing with matrix uncertainty problem. This setting only requires the solution uniqueness of the considered problem to have a successful recovery process, and therefore the required conditions on the measurement matrix are notably looser. As a proof of concept, we can demonstrate the applicability of the proposed approach on the D-Wave quantum annealers; however, we can adapt our method to employ other modern computing phenomena–like adiabatic quantum computers (in general), CMOS annealers, optical parametric oscillators, and neuromorphic computing.

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