Classical Chaos Described by a Density Matrix

In this paper, a reference to the semiclassical model, in which quantum degrees of freedom interact with classical ones, is considered. The classical limit of a maximum-entropy density matrix that describes the temporal evolution of such a system is analyzed. Here, it is analytically shown that, in the classical limit, it is possible to reproduce classical results. An example is classical chaos. This is done by means a pure-state density matrix, a rather unexpected result. It is shown that this is possible only if the quantum part of the system is in a special class of states.

Classical Chaos Described by a Density Matrix

We work with reference to a well-known semiclassical model, in which quantum degrees of freedom interact with classical ones. We show that, in the classical limit, it is possible to represent classical results (e.g., classical chaos) by means a pure-state density matrix.

Hardware-neutral tools for the exploration of optical phenomena in near-resonant atomic systems

In the last three decades, a lot of research has been devoted to the optical response of an atomic media in near-to-resonant conditions and to how nonlinear optical properties are enhanced in these systems. However, as current research turns its attention towards multi-level and multidimensional systems interacting with several electromagnetic fields, the ever-increasing complexity of these problems makes it difficult to treat the semiclassical model of the Maxwell–Bloch equations analytically without any strongly-limiting approximations. Thus, numerical methods and particularly robust and fast computational tools, capable of addressing such class of modern and future problems in photonics, are mandatory. In this paper, we describe the development and implementation of a Maxwell–Bloch numerical solver that exploits the massive parallelism of the GPUs to tackle efficiently problems in multidimensional settings or featuring Doppler broadening effects. This constitutes a simulation tool that is capable of addressing a vast class of problems with considerable reduction of simulation time, featuring speedups up to 15 compared with the same codes running on a CPU.