If real is what you can feel, smell, taste and see, then 'real' is simply electrical signals interpreted by your brain.

Main /
## AboutTensor Networks provide a theoretical framework that captures the important properties of quantum systems (such as entanglement) in a way that also allows use of algebraic properties such as geometric and internal symmetries. The result is a framework for computational methods that combines powerful algebraic methods with efficient numerical techniques that can make good use of modern computational architectures (such as GPU devices) to give tools for modelling many-body quantum systems at a microscopic level. These tools have many applications, from fundamental physics such as the classification of topological states of matter, to applications in real materials and devices such as ultra-cold atomic gases and quantum-engineered devices. In one-dimension, tensor networks have a quite long history, via the density-matrix renormalization-group (DMRG) algorithm developed by Steven R White in 1992. While the method was originally formulated slightly differently, it quickly became apparent that DMRG is built around a one-dimensional tensor network, which is known as a Matrix Product State (MPS). The Matrix Product Toolkit is a project which started life around 2002, originally envisaged as a ‘next generation’ DMRG code, incorporating non-abelian symmetries[1] and with an emphasis on a flexible and generic way to construct Hamiltonian operators and measure observables. In 2003/2004 there were a lot of developments connecting DMRG and Matrix Product States, many of which were discussed at the workshop “Recent Progress and Prospects in DMRG”, held in Leiden in 2004. Especially with the advent of TEBD / t-DMRG it made sense to construct a software toolkit using the MPS representation, and covering a wide range of tools (groundstates, real-time evolution, frequency space methods, etc). Around this time, I realized that the ‘generic’ way to construct a Hamiltonian that I was working on for DMRG was exactly the form of a Matrix Product Operator, and thus the Matrix Product Toolkit was born. Since then the toolkit has grown to around 100,000 lines of C++. It is currently split into two versions, the ‘old’ toolkit for finite-size calculations (accessed via svn), and a ‘new’ version, which started out as a branch for infinite-DMRG, but ultimately there were enough changes to the underlying code that merging the two branches was impractical. Instead, individual tools from the ‘old’ toolkit are getting ported to the new version as the need arises. The aim is to combine, in a single toolkit, finite, infinite and IBC[2] tools, with as much code reuse as possible. The toolkit has been used in approx 90 (as of late 2017) research papers since its development. It isn’t formally described in a research publication. So far, most uses of the toolkit have been collaborative projects with Ian McCulloch as a coauthor, however a simple acknowledgement and citation of the Toolkit website (and, when it appears, the Toolkit publication) is sufficient. Ian McCulloch [1] http://iopscience.iop.org/article/10.1209/epl/i2002-00393-0 |

Page last modified on November 28, 2018, at 05:29 AM