Probability Of Successful Recovery Research Articles

Two-Step Phase Retrieval Algorithm Using Single-Intensity Measurement

Aiming at the problem that the single-intensity phase retrieval method has poor reconstruction quality and low probability of successful recovery, an improved method is proposed in this paper. Our method divides the phase retrieval into two steps: firstly, the GS algorithm is used to recover the amplitude in the spatial domain from the single-spread Fourier spectrum, and then the classical GS algorithm using two intensity measurements (one is recorded and the other is estimated from the first step) measurements is used to recover the phase. Finally, the effectiveness of the proposed method is verified by numerical experiments.

Unlearning quantum information

Quantum dynamics can be driven by measurement. By constructing measurements that gain no information, effective unitary evolution can be induced on a quantum system, for example in ancilla driven quantum computation. In the non-ideal case where a measurement does reveal some information about the system, it may be possible to "unlearn" this information and restore unitary evolution through subsequent measurements. Here we analyse two methods of quantum "unlearning" and present a simplified proof of the bound on the probability of successfully applying the required correction operators. We find that the probability of successful recovery is inversely related to the ability of the initial measurement to exclude the possibility of a state.

Wideband Spectrum Sensing With Sub-Nyquist Sampling in Cognitive Radios

Multi-rate asynchronous sub-Nyquist sampling (MASS) is proposed for wideband spectrum sensing. Corresponding spectral recovery conditions are derived and the probability of successful recovery is given. Compared to previous approaches, MASS offers lower sampling rate, and is an attractive approach for cognitive radio networks.