In 2011, Aaronson gave a striking proof, based on quantum linear optics, showing that the problem of computing the permanent of a matrix is #P-hard. Aaronson's proof led naturally to hardness of approximation results for the permanent, and it was arguably simpler than Valiant's seminal proof of the same fact in 1979. Nevertheless, it did not prove that computing the permanent was #P-hard 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's original proof, this will show that even a multiplicative approximation remains #P-hard to compute. The hardness result even translates to permanents over finite fields, where the problem of computing the permanent of an orthogonal matrix is ModpP-hard in the finite field F_{p^4} for all primes p not equal to 2 or 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, which 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.