The scaling of boson sampling experiments
classification
🪐 quant-ph
keywords
bosonsamplingmatrixaveragecountexperimentslossesoutput
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Boson sampling is the problem of generating a quantum bit stream whose average is the permanent of a $n\times n$ matrix. The bitstream is created as the output of a prototype quantum computing device with $n$ input photons. It is a fundamental challenge to verify boson sampling, and the question of how output count rates scale with matrix size $n$ is crucial. Here we apply results from random matrix theory to establish scaling laws for average count rates in boson sampling experiments with arbitrary inputs and losses. The results show that, even with losses included, verification of nonclassical behaviour at large $n$ values is indeed possible.
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