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The Systematic Normal Form (SysNF) is a canonical form of lattices introduced in [Eldar,Shor 16], in which the basis entries satisfy a certain co-primality condition. Using a smooth analysis of lattices by SysNF lattices we design a quantum algorithm that can efficiently solve the following variant of the bounded-distance-decoding problem: given a lattice L, a vector v, and numbers b = {lambda}_1(L)/n^{17}, a = {lambda}_1(L)/n^{13} decide if vs distance from L is in the range [a/2, a] or at most b, where {lambda}_1(L) is the length of Ls shortest non-zero vector. Improving these parameters to a = b = {lambda}_1(L)/sqrt{n} would invalidate one of the security assumptions of the Learning-with-Errors (LWE) cryptosystem against quantum attacks.
We develop an efficient quantum implementation of an important signal processing algorithm for line spectral estimation: the matrix pencil method, which determines the frequencies and damping factors of signals consisting of finite sums of exponentia
We consider the problem of finding the closest lattice point to a vector in n-dimensional Euclidean space when each component of the vector is available at a distinct node in a network. Our objectives are (i) minimize the communication cost and (ii)
We present a quasipolynomial-time algorithm for solving the weak membership problem for the convex set of separable, i.e. non-entangled, bipartite density matrices. The algorithm decides whether a density matrix is separable or whether it is eps-away
We give an approximation algorithm for MaxCut and provide guarantees on the average fraction of edges cut on $d$-regular graphs of girth $geq 2k$. For every $d geq 3$ and $k geq 4$, our approximation guarantees are better than those of all other clas
Privacy amplification (PA) is an essential part in a quantum key distribution (QKD) system, distilling a highly secure key from a partially secure string by public negotiation between two parties. The optimization objectives of privacy amplification