Oral: MPρ: Efficient data structure to represent coherent and mixture correlations

Understanding the properties of open quantum systems are central to a broad spectrum of themes ranging from the dynamics of many-body systems to that of quantifying simulable limits of quantum systems on powerful classical supercomputers. Recently, tensor network based methods have emerged as effective tools to efficiently capture the properties of quantum systems at scale. However, most of the progress has been limited to the understanding of closed quantum systems with extensions to open quantum systems being largely under explored. To this extent, by decorating the MPS with an additional index, we study the one-dimensional mixed state dynamics at scale by fixing the gauge freedom of the unitary sum representation. Further, we present various algorithms that allow a handle on alternative representations of the underlying density matrix representing the mixed state. To illustrate the computing potential of our methods, we efficiently evaluate the dynamics of a 50-qubit mixed state under the action of different noise channels.