Generalizing and Derandomizing Gurvits's Approximation Algorithm for the Permanent
| Autor: | Travis Hance, Scott Aaronson |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
Discrete mathematics
FOS: Computer and information sciences Nuclear and High Energy Physics Quantum Physics Conjecture Generalization General Physics and Astronomy Approximation algorithm FOS: Physical sciences Statistical and Nonlinear Physics Computational Complexity (cs.CC) Upper and lower bounds Outcome (probability) Theoretical Computer Science Randomized algorithm Combinatorics Matrix (mathematics) Computer Science - Computational Complexity Computational Theory and Mathematics Quantum Physics (quant-ph) Complex number Mathematical Physics Mathematics |
| Popis: | Around 2002, Leonid Gurvits gave a striking randomized algorithm to approximate the permanent of an n*n matrix A. The algorithm runs in O(n^2/eps^2) time, and approximates Per(A) to within eps*||A||^n additive error. A major advantage of Gurvits's algorithm is that it works for arbitrary matrices, not just for nonnegative matrices. This makes it highly relevant to quantum optics, where the permanents of bounded-norm complex matrices play a central role. Indeed, the existence of Gurvits's algorithm is why, in their recent work on the hardness of quantum optics, Aaronson and Arkhipov (AA) had to talk about sampling problems rather than estimation problems. In this paper, we improve Gurvits's algorithm in two ways. First, using an idea from quantum optics, we generalize the algorithm so that it yields a better approximation when the matrix A has either repeated rows or repeated columns. Translating back to quantum optics, this lets us classically estimate the probability of any outcome of an AA-type experiment---even an outcome involving multiple photons "bunched" in the same mode---at least as well as that probability can be estimated by the experiment itself. (This does not, of course, let us solve the AA sampling problem.) It also yields a general upper bound on the probabilities of "bunched" outcomes, which resolves a conjecture of Gurvits and might be of independent physical interest. Second, we use eps-biased sets to derandomize Gurvits's algorithm, in the special case where the matrix A is nonnegative. More interestingly, we generalize the notion of eps-biased sets to the complex numbers, construct "complex eps-biased sets," then use those sets to derandomize even our generalization of Gurvits's algorithm to the multirow/multicolumn case (again for nonnegative A). Whether Gurvits's algorithm can be derandomized for general A remains an outstanding problem. 19 pages, 1 figure, minor additions |
| Databáze: | OpenAIRE |
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