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The Computational Complexity of Linear Optics Scott Aaronson # Alex Arkhipov +

Summary: The Computational Complexity of Linear Optics
Scott Aaronson # Alex Arkhipov +
We give new evidence that quantum computers---moreover, rudimentary quantum computers
built entirely out of linear­optical elements---cannot be e#ciently simulated by classical comput­
ers. In particular, we define a model of computation in which identical photons are generated,
sent through a linear­optical 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 com­
putation, 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 polynomial­time classical algorithm that samples
from the same probability distribution as a linear­optical 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 al­
ready imply a collapse of the polynomial hierarchy. For this, we need two unproven conjectures:
the Permanent­of­Gaussians Conjecture, which says that it is #P­hard to approximate the per­
manent of a matrix A of independent N (0, 1) Gaussian entries, with high probability over A;
and the Permanent Anti­Concentration Conjecture, which says that |Per (A)| # # n!/ poly (n)


Source: Aaronson, Scott - Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT)


Collections: Physics; Computer Technologies and Information Sciences