Tensor Network algorithms in Finance and engineering

We plan to further develop tensor network algorithms for solving partial differential equations for problems in Finance and engineering. These include Fokker-Planck and Langevin equations in Finance, Navier Stokes equations in Fluid dynamics, Maxwell’s equations for chip design, and many others.
Tensor networks can in principle provide exponential improvements in accuracy and especially storage over existing methods and provide a highly promising avenue of research. I have already published early results on the topic, but there is a great deal of important results still to be obtained.
These algorithms can be ported to quantum computes when sufficiently large machines are available.