The problem reduces to estimating |Per(Y)|2, for a matrix YCnn of (essentially) independent N(0,1) Gaussians
{"Image DocumentName": "The problem reduces to estimating |Per(Y)|2, for a matrix Y\uf0ceCn\uf0b4n of (essentially) independent N(0,1) Gaussians", "Image ImageDescription": "To do this, generate a random m\uf0b4n column-orthonormal matrix A that contains Y/m as an n\uf0b4n submatrix (i.e., such that AS=Y/m for some random configuration S) Let M be our BPP algorithm for approximate BosonSampling, and let r be M\u2019s randomness. Use approximate counting (in BPPNP) to estimate. Intuition: M has no way to determine which configuration S we care about. So if it\u2019s right about most configurations, then w.h.p. we must have.", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}