Fachbereich Informatik

Félix Givois

(Fraunhofer ITWM, Kaiserslautern)
hosted by Seminar Series on Scientific Computing

"Quantum Computing for Material Characterization"

( MPI-SWS talk in Kooperation mit dem Fachbereich Informatik)

The description of material laws of a complex microstructure is a problem really complex to solve. As there is not any analytical description of it,the best method is to approximate material effective behaviour by simulating material unit cell on the microscopic scale, based on a tomography image. To do so, the approximation of the solution of an homogenization problem described by an elliptic PDE is needed. As the computation domain can be very wide, a memory efficient algorithm has been developed by Moulinec and Suquet. This algorithm solves the PDE iteratively by a matrix-free gradient-descent method.But despite the fact that this algorithm is matrix free, it can be very costly as it involves 3D Fourier transforms of large data domain at each iteration to invert preconditioner. During the last decades,the computation power needed for material characterization skyrockets with the improvement of material tomography imaging. This improvement in the accuracy of material images turned the computation domains to be very wide(more than a terabyte of data), this leads to way longer computation time due to Fourier transform complexity,and to memory bottleneck. Moreover, as the 3D Fourier transform is not a problem well scalable on distributed memory clusters due to data domain transpositions, this algorithm will not really benefit from parallelization. Nevertheless,recent leaps in the development of Quantum computers by industrials such as IBM or Google seem to let the door open for practical application of quantum algorithms and we could take advantage of this new kind of methods to improve our solver. In this talk we will take a look in a way of replacing classical Fast Fourier transform by its Quantum equivalent: The Quantum Fourier Transform. More especially we will focus on the different difficulties of Quantum algorithms: encoding real or complex data into qubits and reading out the results after the calculation. Moreover,we will discuss about Quantum noise and the limitation of current real quantum devices for practical solutions.

Bio:


Time: Thursday, 29.04.2021, 11:30
Place:
Video:

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