Quantum Mechanics in Simple Matrix Form

1986 ◽  
Vol 54 (12) ◽  
pp. 1154-1155 ◽  
Author(s):  
Thomas F. Jordan ◽  
Kannan Jagannathan
1986 ◽  
Vol 37 (11) ◽  
pp. 465-465
Author(s):  
E Copeland

2021 ◽  
Author(s):  
Marek Gazdzicki ◽  
Mark Gorenstein ◽  
Ivan Pidhurskyi ◽  
Oleh Savchuk ◽  
Leonardo Tinti

Abstract Quantum statistics and non-locality are deeply rooted in quantum mechanics and go beyond our intuition reflected in classical physics. Quantum statistics can be derived using statistical methods for indistinguishable particles - particles of quantum mechanics. Violation of strong locality - colloquially called the ghostly action at a distance - is one of the most amazing properties of nature derived from quantum mechanics. An intriguing question is whether the non-local evolution of indistinguishable particles is needed to reach the equilibrium state given by quantum statistics. Motivated by the above and similar questions, we developed a simple framework that allows us to follow space-time evolution of assembly of particles. It is based on a discrete-time Markov chain on countable space for indistinguishable particles. We summarise well-known and introduced new constraints on the transition matrix that grant space-time symmetries, locality of particle-transport, strong locality, and equilibrium state. Then, within the framework, several important cases are considered. First, we show that the simplest transition matrix leads to equilibrium but violates particle transport and strong localities. Furthermore, we construct a simple matrix that leads to equilibrium obeying particle-transport locality and violating strong locality. This resembles the properties of quantum mechanics. Finally, we demonstrate that it is also possible to reach equilibrium by obeying both particle-transport and strong localities. Thus, within this framework, the violation of a strong locality is not needed to reach the equilibrium of indistinguishable particles. However, to obey strong locality, a complex structure of the transition matrix is needed. In addition, we comment on distinguishable particles and, in particular, show that their evolution seen by an observer blind to particle differences may look like the evolution of indistinguishable particles with the properties of quantum mechanics. We hope that this work may help to study the relation between symmetries, localities and the evolution to equilibrium for indistinguishable and distinguishable particles.


2021 ◽  
pp. 1-16
Author(s):  
Zhiwei Qiao

PURPOSE: The adaptive steepest descent projection onto convex set (ASD-POCS) algorithm is a promising algorithm for constrained total variation (TV) type norm minimization models in computed tomography (CT) image reconstruction using sparse and/or noisy data. However, in ASD-POCS algorithm, the existing gradient expression of the TV-type norm appears too complicated in the implementation code and reduces image reconstruction speed. To address this issue, this work aims to develop and test a simple and fast ASD-POCS algorithm. METHODS: Since the original algorithm is not derived thoroughly, we first obtain a simple matrix-form expression by thorough derivation via matrix representations. Next, we derive the simple matrix expressions of the gradients of TV, adaptive weighted TV (awTV), total p-variation (TpV), high order TV (HOTV) norms by term combinations and matrix representations. The deep analysis is then performed to identify the hidden relations of these terms. RESULTS: The TV reconstruction experiments by use of sparse-view projections via the Shepp-Logan, FORBILD and a real CT image phantoms show that the simplified ASD-POCS (S-ASD-POCS) using the simple matrix-form expression of TV gradient achieve the same reconstruction accuracy relative to ASD-POCS, whereas it enables to speed up the whole ASD process 1.8–2.7 time fast. CONCLUSIONS: The derived simple matrix expressions of the gradients of these TV-type norms may simplify the implementation of the ASD-POCS algorithm and speed up the ASD process. Additionally, a general gradient expression suitable to all the sparse transform-based optimization models is demonstrated so that the ASD-POCS algorithm may be tailored to extended image reconstruction fields with accelerated computational speed.


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