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A deep learning approach to estimating permanents

Chang, B.L. (2018) A deep learning approach to estimating permanents.

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Abstract:The permanent of a matrix is a value that can be computed from a square matrix. It is calculated in almost the same way as the determinant, but all of the terms in the permanent are summed. The permanent has an application in a quantum optical experiment known as boson sampling. The probability of the outcome of this experiment can be calculated by taking the permanent of a matrix. It is believed that the complexity of approximating the outcome of this experiment is related to the photon indistinguishability. At full distinguishability, it is expected to follow polynomial complexity as the matrix size increases. At full indistinguishability, it is expected to follow exponential complexity. For partial indistinguishability, the complexity is expected to be somewhere in between. The aim of this research is to use deep learning networks to estimate the permanents for varying levels of photon indistinguishability and to investigate whether the complexity of the networks reflects the expected complexity of estimating the outcome of the boson sampling experiment
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:33 physics, 54 computer science
Programme:Electrical Engineering BSc (56953)
Link to this item:http://purl.utwente.nl/essays/76139
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