New Permanent Estimators via Non-Commutative Determinants
  We introduce a new notion of the determinant, called symmetrized determinant, for a square matrix with the entries in an associative algebra $\AA$. The monomial expansion of the symmetrized determinant is obtained from the standard expansion of the commutative determinant by averaging the products of entries of the matrix in all possible orders. We show that for any fixed finite-dimensional associative algebra $\AA$, the symmetrized determinant of an $n\times n$ matrix with the entries in $\AA$ can be computed in polynomial in $n$ time (the degree of the polynomial is linear in the dimension of $\AA$). Then, for every associative algebra $\AA$ endowed with a scalar product and unbiased probability measure, we construct a randomized polynomial time algorithm to estimate the permanent of non-negative matrices. We conjecture that if $\AA=\Mat(d, {\Bbb R})$ is the algebra of $d\times d$ real matrices endowed with the standard scalar product and Gaussian measure, the algorithm approximates the permanent of a non-negative $n \times n$ matrix within $O(\gamma_d^n)$ factor, where $\lim_{d \longrightarrow +\infty} \gamma_d=1$. Finally, we provide some informal arguments why the conjecture might be true. 
Alexander Barvinok