scholarly journals Comparison of a quantum random number generator with pseudorandom number generators for their use in molecular Monte Carlo simulations

2017 ◽  
Vol 38 (31) ◽  
pp. 2713-2720 ◽  
Author(s):  
Dario Ghersi ◽  
Abhishek Parakh ◽  
Mihaly Mezei
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Random Number ◽  
Random Number Generator ◽  
Pseudorandom Number ◽  
Number Generator ◽  
Pseudorandom Number Generators

scholarly journals ERRORS IN MONTE CARLO SIMULATIONS USING SHIFT REGISTER RANDOM NUMBER GENERATORS

1996 ◽  
Vol 06 (06) ◽  
pp. 781-787 ◽  
Author(s):  
F. SCHMID ◽  
N. B. WILDING
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Random Number ◽  
Random Number Generator ◽  
Shift Register ◽  
Magnetization Distribution ◽  
Number Generator ◽  
Random Number Generators ◽  
Single Spin ◽  
System Volume

We report large systematic errors in Monte Carlo simulations of the tricritical Blume–Capel model using single spin Metropolis updating. The error, manifest as a 20% asymmetry in the magnetization distribution, is traced to the interplay between strong triplet correlations in the shift register random number generator and the large tricritical clusters. The effect of these correlations is visible only when the system volume is a multiple of the random number generator lag parameter. No such effects are observed in related models.

scholarly journals Multi-criteria method for evaluation of the pseudorandom number generators using thermodynamic systems behavior

2019 ◽  
Vol XXII (2) ◽  
pp. 305-312
Author(s):  
Cornaciu V.
Keyword(s):  
Optimization Problem ◽  
Random Number ◽  
Random Number Generator ◽  
Mathematical Optimization ◽  
Pseudorandom Number ◽  
Number Generator ◽  
Pseudorandom Number Generators ◽  
Pseudo Random Number ◽  
High Level ◽  
Pseudo Random Number Generator

The choice of pseudo random number generators is a major problem in many areas of activity, one of the areas that uses them intensively it is the field of cryptography. Although they are applicable to any programming paradigm, these generators can be used successfully in the statistical analysis of thermodynamic behaviors, so, they must be designed individually to meet the requirements of each type of client. The research for new pseudorandom number generators with higher level of security is a field in great expansion, but the factors that influence us to use a product are not always this high level of security, but features such as ease of implementation or rapidity of generation. The design of a pseudo-random number generator needs to consider various characteristics simultaneously, which can be regarded as a optimization problem. The purpose of the article is to give a overview of the characteristics that a pseudo-random generator must meet in different fields, to define an objective function that encompasses these features, and create a mathematical optimization problem in order to achieve the maximum of properties that a pseudo-random number generator can give.

A Modification to the Monte Carlo Method—The Exodus Method

1968 ◽  
Vol 90 (3) ◽  
pp. 328-332 ◽  
Author(s):  
A. F. Emery ◽  
W. W. Carson
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Calculation Time ◽  
The Monte Carlo Method

A modification to the Monte Carlo method is described which reduces calculation time and improves the accuracy. This method—termed “Exodus”—is not dependent upon a random number generator and may be applied to any problem which admits of a nodal network.

A new random-number generator for multispin Monte Carlo algorithms

1987 ◽  
Vol 48 (1-2) ◽  
pp. 135-149 ◽  
Author(s):  
L. Pierre ◽  
T. Giamarchi ◽  
H. J. Schulz
Keyword(s):  
Monte Carlo ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Monte Carlo Algorithms

scholarly journals FULL QCD ON APE100 MACHINES

1995 ◽  
Vol 06 (01) ◽  
pp. 25-45
Author(s):  
STEFANO ANTONELLI ◽  
MARCO BELLACCI ◽  
ANDREA DONINI ◽  
RENATA SARNO
Keyword(s):  
Monte Carlo ◽  
Random Number ◽  
Random Number Generator ◽  
Memory Capacity ◽  
Monte Carlo Algorithm ◽  
Number Generator ◽  
Inversion Algorithm ◽  
Hybrid Monte Carlo ◽  
Pure Gauge ◽  
Wilson Fermions

We present the first tests and results from a study of QCD with two flavours of dynamical Wilson fermions using the Hybrid Monte Carlo Algorithm (HMCA) on APE100 machines. The simulations have been performed on 64 lattice for the pure gauge HMCA and on 84, 123×32 lattices for full QCD configurations. We discuss the inversion algorithm for the fermionic operator, the methods used to overcome the problems arising using a 32 bit machine and the implementation of a new random number generator for APE100 machines. We propose different scenarios for the simulation of physical observables, with respect to the memory capacity and speed of different APE100 configurations.

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