Anand Natarajan

Learn More
Monitoring threats to information security is increasingly becoming important to protecting secured organizational documents. There is increasing number of threats to information security, which originates from the internal users of the system. Insider is defined as a trusted person and has access to classified documents. Our focus here is on understanding(More)
We give a quantum multiprover interactive proof system for the local Hamiltonian problem in which there is a constant number of provers, questions are classical of length polynomial in the number of qubits, and answers are of constant length. The main novelty of our protocol is that the gap between completeness and soundness is directly proportional to the(More)
We present a stronger version of the Doherty-Parrilo-Spedalieri (DPS) hierarchy of approximations for the set of separable states. Unlike DPS, our hierarchy converges exactly at a finite number of rounds for any fixed input dimension. This yields an algorithm for sep-arability testing which is singly exponential in dimension and polylogarithmic in accuracy.(More)
We introduce a method for using reductions to prove limitations on the ability of semidefinite programs (SDPs) to approximately solve optimization problems. We use this to show specifically that SDPs have limited ability to approximate two particularly important sets in quantum information theory: 1. The set of separable (i.e. unentangled) states. 2. The(More)
We introduce a simple two-player test which certifies that the players apply tensor products of Pauli σ<sub><i>X</i></sub> and σ<sub><i>Z</i></sub> observables on the tensor product of <i>n</i> EPR pairs. The test has constant robustness: any strategy achieving success probability within an additive of the optimal must be <i>poly</i>(&#206;&#181;)-close,(More)
Semidefinite programs (SDPs) are a framework for exact or approximate optimization with widespread application in quantum information theory. We introduce a new method for using reductions to construct integrality gaps for SDPs, meaning instances where the SDP value is far from the true optimum. These are based on new limitations on the sum-of-squares (SoS)(More)