Siddhartha Das

Siddhartha Dasdas.seed

Nov 10 2017 02:00 UTC
We discuss quantum position verification (QPV) protocols in which the verifiers create and send single-qubit states to the prover. QPV protocols using single-qubit states are known to be insecure against adversaries that share a small number of entangled qubits. We introduce QPV protocols that are practically secure: they only require single-qubit states from each of the verifiers, yet their security is broken if the adversaries share an impractically large number of shared entangled qubits. These protocols are a modification of known QPV protocols in which we include a classical random oracle without altering the amount of quantum resources needed by the verifiers. We present a cheating strategy that requires a number of entangled qubits shared among the adversaries that grows exponentially with the size of the classical input of the random oracle.
Oct 30 2017 04:40 UTC
Oct 18 2017 15:16 UTC
Oct 18 2017 15:15 UTC
Oct 06 2017 03:18 UTC

Here is a work in related direction: "Unification of Bell, Leggett-Garg and Kochen-Specker inequalities: Hybrid spatio-temporal inequalities", Europhysics Letters 104, 60006 (2013), which may be relevant to the discussions in your paper. [https://arxiv.org/abs/1308.0270]

Sep 22 2017 02:00 UTC
Entanglement distribution is a prerequisite for several important quantum information processing and computing tasks, such as quantum teleportation, quantum key distribution, and distributed quantum computing. In this work, we focus on two-dimensional quantum networks based on optical quantum technologies using dual-rail photonic qubits. We lay out a quantum network architecture for entanglement distribution between distant parties, with the technological constraint that quantum repeaters equipped with quantum memories are not currently widely available. We also discuss several quantum network topologies for the building of a fail-safe quantum internet. We use percolation theory to provide figures of merit on the loss parameter of the optical medium for networks with bow-tie lattice and Archimedean lattice topologies. These figures of merit allow for comparisons of the robustness of different networks against intermittent failures of its nodes and against intermittent photon loss, which is an important consideration in the realization of the quantum internet.
Sep 07 2017 02:04 UTC
Sep 06 2017 02:24 UTC
Aug 22 2017 20:57 UTC
Siddhartha Das scited Quantum state certification
Aug 17 2017 02:40 UTC
Aug 11 2017 10:03 UTC
Siddhartha Das scited Quantum deconvolution
Jul 24 2017 23:23 UTC
Jul 21 2017 02:00 UTC
It is well known in the realm of quantum mechanics and information theory that the entropy is non-decreasing for the class of unital physical processes. However, in general, the entropy does not exhibit monotonic behavior. This has restricted the use of entropy change in characterizing evolution processes. Recently, a lower bound on the entropy change was provided in [Buscemi, Das, & Wilde, Phys. Rev. A 93(6), 062314 (2016)]. We explore the limit that this bound places on the physical evolution of a quantum system and discuss how these limits can be used as witnesses to characterize quantum dynamics. In particular, we derive a lower limit on the rate of entropy change for memoryless quantum dynamics, and we argue that it provides a witness of non-unitality. This limit on the rate of entropy change leads to definitions of several witnesses for testing memory effects in quantum dynamics. Furthermore, from the aforementioned lower bound on entropy change, we obtain a measure of non-unitarity for unital evolutions.
Jul 04 2017 02:00 UTC
With the significant advancement in quantum computation in the past couple of decades, the exploration of machine-learning subroutines using quantum strategies has become increasingly popular. Gaussian process regression is a widely used technique in supervised classical machine learning. Here we introduce an algorithm for Gaussian process regression using continuous-variable quantum systems that can be realized with technology based on photonic quantum computers. Our algorithm shows that by using a continuous-variable quantum computer a dramatic speed-up in computing Gaussian process regression can be achieved, i.e., the possibility of exponentially reducing the time to compute. Furthermore, our results also include a continuous-variable quantum-assisted singular value decomposition method of non-sparse low rank matrices and forms an important subroutine in our Gaussian process regression algorithm.
May 08 2017 04:30 UTC