Quantum state tomography is a key technique for quantum information processing but challenging due to the exponential growth of its complexity with system size. In this work we propose an algorithm which iteratively finds best non-negative matrix product approximation based on set measurement outcomes whose size does not necessarily grow exponentially. Compared method neural network states, our...

We propose an accelerated density matrix purification scheme with error control. The method makes use of the scale-and-fold acceleration technique and screening submatrix products in block-sparse matrix-matrix multiplies to reduce computational cost. An bound a parameter sweep are combined select threshold value for screening, such that can be controlled. evaluate performance comparison without...

Automotive and aerospace industries are required to light materials which have high properties. This causes to reduce production cost and fuel consumption. One of these advanced materials that have been attracted at these decades is particulate metal matrix nanocomposites. These materials have a combination of ductile matrix alloy properties and ceramic reinforcement nanoparticles. Powder metal...

We propose for the spin density matrix two parametrizations which automatically fulfil non-negativity conditions, without setting any bound on parameters. The first one relies a theorem, that we prove, and it is rather simple easily adaptable to some specific reactions, where, example, parity conserved or angular momentum conservation entails selection rules. Moreover, in case when rank less th...