35,825 research outputs found
A Linear-Optical Proof that the Permanent is #P-Hard
One of the crown jewels of complexity theory is Valiant's 1979 theorem that
computing the permanent of an n*n matrix is #P-hard. Here we show that, by
using the model of linear-optical quantum computing---and in particular, a
universality theorem due to Knill, Laflamme, and Milburn---one can give a
different and arguably more intuitive proof of this theorem.Comment: 12 pages, 2 figures, to appear in Proceedings of the Royal Society A.
doi: 10.1098/rspa.2011.023
New Hardness Results for the Permanent Using Linear Optics
In 2011, Aaronson gave a striking proof, based on quantum linear optics, that the problem of computing the permanent of a matrix is #P-hard. Aaronson\u27s proof led naturally to hardness of approximation results for the permanent, and it was arguably simpler than Valiant\u27s seminal proof of the same fact in 1979. Nevertheless, it did not show #P-hardness of the permanent for any class of matrices which was not previously known. In this paper, we present a collection of new results about matrix permanents that are derived primarily via these linear optical techniques.
First, we show that the problem of computing the permanent of a real orthogonal matrix is #P-hard. Much like Aaronson\u27s original proof, this implies that even a multiplicative approximation remains #P-hard to compute. The hardness result even translates to permanents of orthogonal matrices over the finite field F_{p^4} for p != 2, 3. Interestingly, this characterization is tight: in fields of characteristic 2, the permanent coincides with the determinant; in fields of characteristic 3, one can efficiently compute the permanent of an orthogonal matrix by a nontrivial result of Kogan.
Finally, we use more elementary arguments to prove #P-hardness for the permanent of a positive semidefinite matrix. This result shows that certain probabilities of boson sampling experiments with thermal states are hard to compute exactly, despite the fact that they can be efficiently sampled by a classical computer
The Computational Complexity of Linear Optics
We give new evidence that quantum computers -- moreover, rudimentary quantum
computers built entirely out of linear-optical elements -- cannot be
efficiently simulated by classical computers. In particular, we define a model
of computation in which identical photons are generated, sent through a
linear-optical network, then nonadaptively measured to count the number of
photons in each mode. This model is not known or believed to be universal for
quantum computation, and indeed, we discuss the prospects for realizing the
model using current technology. On the other hand, we prove that the model is
able to solve sampling problems and search problems that are classically
intractable under plausible assumptions. Our first result says that, if there
exists a polynomial-time classical algorithm that samples from the same
probability distribution as a linear-optical network, then P^#P=BPP^NP, and
hence the polynomial hierarchy collapses to the third level. Unfortunately,
this result assumes an extremely accurate simulation. Our main result suggests
that even an approximate or noisy classical simulation would already imply a
collapse of the polynomial hierarchy. For this, we need two unproven
conjectures: the "Permanent-of-Gaussians Conjecture", which says that it is
#P-hard to approximate the permanent of a matrix A of independent N(0,1)
Gaussian entries, with high probability over A; and the "Permanent
Anti-Concentration Conjecture", which says that |Per(A)|>=sqrt(n!)/poly(n) with
high probability over A. We present evidence for these conjectures, both of
which seem interesting even apart from our application. This paper does not
assume knowledge of quantum optics. Indeed, part of its goal is to develop the
beautiful theory of noninteracting bosons underlying our model, and its
connection to the permanent function, in a self-contained way accessible to
theoretical computer scientists.Comment: 94 pages, 4 figure
Boson Sampling from Gaussian States
We pose a generalized Boson Sampling problem. Strong evidence exists that
such a problem becomes intractable on a classical computer as a function of the
number of Bosons. We describe a quantum optical processor that can solve this
problem efficiently based on Gaussian input states, a linear optical network
and non-adaptive photon counting measurements. All the elements required to
build such a processor currently exist. The demonstration of such a device
would provide the first empirical evidence that quantum computers can indeed
outperform classical computers and could lead to applications
BosonSampling with Lost Photons
BosonSampling is an intermediate model of quantum computation where
linear-optical networks are used to solve sampling problems expected to be hard
for classical computers. Since these devices are not expected to be universal
for quantum computation, it remains an open question of whether any
error-correction techniques can be applied to them, and thus it is important to
investigate how robust the model is under natural experimental imperfections,
such as losses and imperfect control of parameters. Here we investigate the
complexity of BosonSampling under photon losses---more specifically, the case
where an unknown subset of the photons are randomly lost at the sources. We
show that, if out of photons are lost, then we cannot sample
classically from a distribution that is -close (in total
variation distance) to the ideal distribution, unless a
machine can estimate the permanents of Gaussian
matrices in time. In particular, if is constant, this implies
that simulating lossy BosonSampling is hard for a classical computer, under
exactly the same complexity assumption used for the original lossless case.Comment: 12 pages. v2: extended concluding sectio
Quantum Sampling Problems, BosonSampling and Quantum Supremacy
There is a large body of evidence for the potential of greater computational
power using information carriers that are quantum mechanical over those
governed by the laws of classical mechanics. But the question of the exact
nature of the power contributed by quantum mechanics remains only partially
answered. Furthermore, there exists doubt over the practicality of achieving a
large enough quantum computation that definitively demonstrates quantum
supremacy. Recently the study of computational problems that produce samples
from probability distributions has added to both our understanding of the power
of quantum algorithms and lowered the requirements for demonstration of fast
quantum algorithms. The proposed quantum sampling problems do not require a
quantum computer capable of universal operations and also permit physically
realistic errors in their operation. This is an encouraging step towards an
experimental demonstration of quantum algorithmic supremacy. In this paper, we
will review sampling problems and the arguments that have been used to deduce
when sampling problems are hard for classical computers to simulate. Two
classes of quantum sampling problems that demonstrate the supremacy of quantum
algorithms are BosonSampling and IQP Sampling. We will present the details of
these classes and recent experimental progress towards demonstrating quantum
supremacy in BosonSampling.Comment: Survey paper first submitted for publication in October 2016. 10
pages, 4 figures, 1 tabl
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
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