Abstract
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 and a vector , find a vector such that . We consider the case where one does not need to know the solution itself, but rather an approximation of the expectation value of some operator associated with , e.g., for some matrix . In this case, when is sparse, and has condition number , the fastest known classical algorithms can find and estimate in time scaling roughly as . Here, we exhibit a quantum algorithm for estimating whose runtime is a polynomial of and . Indeed, for small values of [i.e., ], we prove (using some common complexity-theoretic assumptions) that any classical algorithm for this problem generically requires exponentially more time than our quantum algorithm.
- Received 5 July 2009
DOI:https://doi.org/10.1103/PhysRevLett.103.150502
©2009 American Physical Society
Synopsis
The quantum shortcut to a solution
Published 19 October 2009
A quantum algorithm that uses the solution to a set of linear equations provides an exponential speedup by comparison with classical alternatives.
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