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Tomography via Compressed Sensing

Steve Flammia, Caltech

(Session 4 : Friday from 3:00pm-3:30pm)

Abstract. I will review past results and present current progress on using methods from the theory of compressed sensing to drastically reduce the number of measurements required for quantum tomography. These methods achieve a square root improvement in the number of measurement settings when the state in question is pure. Our methods have several features that make them amenable to experimental implementation: they require only simple Pauli measurements, use fast convex optimization, are stable against noise, can be applied to states that are only approximately pure, and can be extended to process tomography of nearly unitary channels. The acquired data can be used to certify that the state is indeed close to pure, so no a priori assumptions are needed.