Parallel acceleration of density matrix renormalization group calculations with TensorFlow

We parallelize singular value decomposition in a matrix product state formulation of the density matrix renormalization group using the TensorFlow library to find use cases in which consumer-grade GPU hardware can reduce run times. Specifically, we tested the performance of the implementation on a 20-site spin chain for a variable number of kept states. We were able to acquire a speedup of up to 6.4% when using TensorFlow GPU libraries and a speedup of up to 5.4% with TensorFlow multicore CPU libraries.