365 research outputs found
What can quantum optics say about computational complexity theory?
Considering the problem of sampling from the output photon-counting
probability distribution of a linear-optical network for input Gaussian states,
we obtain results that are of interest from both quantum theory and the
computational complexity theory point of view. We derive a general formula for
calculating the output probabilities, and by considering input thermal states,
we show that the output probabilities are proportional to permanents of
positive-semidefinite Hermitian matrices. It is believed that approximating
permanents of complex matrices in general is a #P-hard problem. However, we
show that these permanents can be approximated with an algorithm in BPP^NP
complexity class, as there exists an efficient classical algorithm for sampling
from the output probability distribution. We further consider input
squeezed-vacuum states and discuss the complexity of sampling from the
probability distribution at the output.Comment: 5 pages, 1 figur
Computing the permanent of (some) complex matrices
We present a deterministic algorithm, which, for any given 0< epsilon < 1 and
an nxn real or complex matrix A=(a_{ij}) such that | a_{ij}-1| < 0.19 for all
i, j computes the permanent of A within relative error epsilon in n^{O(ln n -ln
epsilon)} time. The method can be extended to computing hafnians and
multidimensional permanents.Comment: 12 pages, results extended to hafnians and multidimensional
permanents, minor improvement
Approximating the Permanent with Fractional Belief Propagation
We discuss schemes for exact and approximate computations of permanents, and
compare them with each other. Specifically, we analyze the Belief Propagation
(BP) approach and its Fractional Belief Propagation (FBP) generalization for
computing the permanent of a non-negative matrix. Known bounds and conjectures
are verified in experiments, and some new theoretical relations, bounds and
conjectures are proposed. The Fractional Free Energy (FFE) functional is
parameterized by a scalar parameter , where
corresponds to the BP limit and corresponds to the exclusion
principle (but ignoring perfect matching constraints) Mean-Field (MF) limit.
FFE shows monotonicity and continuity with respect to . For every
non-negative matrix, we define its special value to be the
for which the minimum of the -parameterized FFE functional is
equal to the permanent of the matrix, where the lower and upper bounds of the
-interval corresponds to respective bounds for the permanent. Our
experimental analysis suggests that the distribution of varies for
different ensembles but always lies within the interval.
Moreover, for all ensembles considered the behavior of is highly
distinctive, offering an emprirical practical guidance for estimating
permanents of non-negative matrices via the FFE approach.Comment: 42 pages, 14 figure
Approximating the Permanent of a Random Matrix with Vanishing Mean
We show an algorithm for computing the permanent of a random matrix with
vanishing mean in quasi-polynomial time. Among special cases are the Gaussian,
and biased-Bernoulli random matrices with mean 1/lnln(n)^{1/8}. In addition, we
can compute the permanent of a random matrix with mean 1/poly(ln(n)) in time
2^{O(n^{\eps})} for any small constant \eps>0. Our algorithm counters the
intuition that the permanent is hard because of the "sign problem" - namely the
interference between entries of a matrix with different signs. A major open
question then remains whether one can provide an efficient algorithm for random
matrices of mean 1/poly(n), whose conjectured #P-hardness is one of the
baseline assumptions of the BosonSampling paradigm
A quantum-inspired algorithm for estimating the permanent of positive semidefinite matrices
We construct a quantum-inspired classical algorithm for computing the
permanent of Hermitian positive semidefinite matrices, by exploiting a
connection between these mathematical structures and the boson sampling model.
Specifically, the permanent of a Hermitian positive semidefinite matrix can be
expressed in terms of the expected value of a random variable, which stands for
a specific photon-counting probability when measuring a linear-optically
evolved random multimode coherent state. Our algorithm then approximates the
matrix permanent from the corresponding sample mean and is shown to run in
polynomial time for various sets of Hermitian positive semidefinite matrices,
achieving a precision that improves over known techniques. This work
illustrates how quantum optics may benefit algorithms development.Comment: 9 pages, 1 figure. Updated version for publicatio
Matrix permanent and quantum entanglement of permutation invariant states
We point out that a geometric measure of quantum entanglement is related to
the matrix permanent when restricted to permutation invariant states. This
connection allows us to interpret the permanent as an angle between vectors. By
employing a recently introduced permanent inequality by Carlen, Loss and Lieb,
we can prove explicit formulas of the geometric measure for permutation
invariant basis states in a simple way.Comment: 10 page
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