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08050061

Solving linear systems of equations is a common problem that arises both on its own and as a subroutine in more complex problems: given a matrix A and a vector b, find a vector x such that Ax=b. Often, one does not need to know the solution x itself, but rather an approximation of the expectation value of some operator associated with x, e.g., x'Mx for some matrix M. In this case, when A is sparse and well-conditioned, with largest dimension N, the best known classical algorithms can find x and estimate x'Mx in O(N * poly(log(N))) time.

In this talk I'll describe a quantum algorithm for solving linear sets of equations that runs in poly(log N) time, an exponential improvement over the best classical algorithm.

This talk is based on my paper arXiv:0811.3171v2, which was written with Avinatan Hassidim and Seth Lloyd.

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