Cover Page September-2019-Advanced Materials Letters

Advanced Materials Letters

Volume 10, Issue 9, Pages 622-626, September 2019
About Cover

Materials modelling has established itself as an essential analysis not only to study the insight of complex physical phenomena appearing in the soft or condensed matter but also to realize the emerging trend of 'reverse engineering' as a keystone for technological innovations. The cover photo of this September 2019 issue describes the bio interaction between graphene and enzyme protein for bioelectronics applicable in battery, fuel cell and biosensing applications and dedicated to celebrating the 6th anniversary of Nobel Prize in Chemistry on “Multiscale models for complex chemical systems”.


Matrix and Variant Transformations Simulate Statistical Canonical Ensembles from Fock to Poissonian States of Random Sequences

Jeffrey Zheng1, 2*, Yamin Luo1, 2, Xin Zhang1, 2, Chris Zheng3 

1Key Lab of Quantum Information, Yunnan University, Kunming Yunnan 650091, China

2Key Lab of Software Engineering, Yunnan University, Kunming Yunnan 650091, China

3Tahto, Sydney, Australia 

Adv. Mater. Lett., 2019, 10 (9), pp 622-626

DOI: 10.5185/amlett.2019.9911

Publication Date (Web): Mar 02, 2019

E-mail: conjugatelogic@yahoo.com

Abstract

From a quantum statistical viewpoint, four typical quantum states are Fock, Sub-Poissonian, Poissonian and Super-Poissonian states. Quantum interactions are among Fock and Poissonian states. Using quantum statistics, model and simulation, this paper propose two models: matrix and variant transformations: 1. MT Matrix Transformation – eigenvalue states; 2. VT Variant Transformation – invariant states to analyze three random sequences: 1) random; 2) conditional random in a constant; 3) periodic pattern.  Four procedures are proposed. Fast Fourier Transformation FFT is applied as one of MT schemes and two invariant scheme of VT schemes are applied, three random sequences are used in M segments, and each segment has a length m to generate a measuring sequence. Shifting operations are applied on each random sequence to create m+1 spectrum distributions. Better than FFT, VT can identify Fock, Sub-Poissonian, Poissonian states in random analysis to distinguish three random sequences as three levels of statistical ensembles: Micro-canonical, Canonical, and Grand-Canonical ensembles. Applying two transformations, quantum statistics, model and simulation of modern quantum theory and applications can be explored.  © VBRI Press.

Keywords

Fock, sub-poissonian, poissonian, super-poissonian, matrix transformation, variant transformation, canonical ensembles.

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