Programs in Physics & Physical Chemistry
|[Licence| Download | New Version Template] aemr_v1_0.tar.gz(12 Kbytes)|
|Manuscript Title: Numerical model for macroscopic quantum superpositions based on phase-covariant quantum cloning.|
|Authors: A. Buraczewski, M. Stobińska|
|Program title: MQSVIS|
|Catalogue identifier: AEMR_v1_0|
Distribution format: tar.gz
|Journal reference: Comput. Phys. Commun. 183(2012)2245|
|Programming language: C with OpenMP extensions (main numerical program), Python (helper scripts).|
|Computer: Modern PC (tested on AMD and Intel processors), HP BL2x220.|
|Operating system: Unix/Linux.|
|Has the code been vectorised or parallelized?: Yes (OpenMP).|
|RAM: 200 MB for single run for 1000 × 1000 tile|
|Keywords: Macroscopic quantum superpositions, Macroscopic entanglement, Optimal quantum cloning, Gaussian hypergeometric function, Quantum optics.|
|PACS: 03.65.Ud, 42.50.-p, 02.70.-c, 02.60.-x.|
|Classification: 4.15, 18.|
External routines: OpenMP
Nature of problem:
Recently macroscopically populated quantum superpositions for light, generated by an optimal quantum cloner, were demonstrated. They are of fundamental and technological interest. Their properties are governed by Gaussian hypergeometric function 2F1 of half-integer parameters, which cannot be reduced to either elementary nor easily tractable functions. Computation of photon number distribution, visibility and mean number of photons, necessary for characterization of these states, requires evaluation of infinite sums involving this function performed over its parameters.
The MQSVIS program suite computes various quantum indicators, such as photon number distribution, visibility, mean number of photons, variance for macroscopic quantum superpositions of light. It takes losses (modeled with a beamsplitter) and imperfect photodetection (modeled with a Weierstrass transform applied to the photon number distribution) into account. Cutoffs of the infinite hypergeometric sums are estimated dynamically, and precision is enhanced with computation of expressions in logarithmic form. Depending on the experimental parameters, the program chooses one of several ways of summation to achieve the best efficiency. The program is parallelized using OpenMP standard, which ensures best utilization of multicore processors, and splits the work into tiles computed with different nodes of a computer cluster. This allows computation of the required indicators for realistic values of the parameters.
1-2h for 1000×1000 tile, depending on the values of parameters.
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