1,093 research outputs found

    Quantum memories based on engineered dissipation

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    Storing quantum information for long times without disruptions is a major requirement for most quantum information technologies. A very appealing approach is to use self-correcting Hamiltonians, i.e. tailoring local interactions among the qubits such that when the system is weakly coupled to a cold bath the thermalization process takes a long time. Here we propose an alternative but more powerful approach in which the coupling to a bath is engineered, so that dissipation protects the encoded qubit against more general kinds of errors. We show that the method can be implemented locally in four dimensional lattice geometries by means of a toric code, and propose a simple 2D set-up for proof of principle experiments.Comment: 6 +8 pages, 4 figures, Includes minor corrections updated references and aknowledgement

    Side Channel Leakage Analysis - Detection, Exploitation and Quantification

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    Nearly twenty years ago the discovery of side channel attacks has warned the world that security is more than just a mathematical problem. Serious considerations need to be placed on the implementation and its physical media. Nowadays the ever-growing ubiquitous computing calls for in-pace development of security solutions. Although the physical security has attracted increasing public attention, side channel security remains as a problem that is far from being completely solved. An important problem is how much expertise is required by a side channel adversary. The essential interest is to explore whether detailed knowledge about implementation and leakage model are indispensable for a successful side channel attack. If such knowledge is not a prerequisite, attacks can be mounted by even inexperienced adversaries. Hence the threat from physical observables may be underestimated. Another urgent problem is how to secure a cryptographic system in the exposure of unavoidable leakage. Although many countermeasures have been developed, their effectiveness pends empirical verification and the side channel security needs to be evaluated systematically. The research in this dissertation focuses on two topics, leakage-model independent side channel analysis and security evaluation, which are described from three perspectives: leakage detection, exploitation and quantification. To free side channel analysis from the complicated procedure of leakage modeling, an observation to observation comparison approach is proposed. Several attacks presented in this work follow this approach. They exhibit efficient leakage detection and exploitation under various leakage models and implementations. More importantly, this achievement no longer relies on or even requires precise leakage modeling. For the security evaluation, a weak maximum likelihood approach is proposed. It provides a quantification of the loss of full key security due to the presence of side channel leakage. A constructive algorithm is developed following this approach. The algorithm can be used by security lab to measure the leakage resilience. It can also be used by a side channel adversary to determine whether limited side channel information suffices the full key recovery at affordable expense

    Quantum information and statistical mechanics: an introduction to frontier

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    This is a short review on an interdisciplinary field of quantum information science and statistical mechanics. We first give a pedagogical introduction to the stabilizer formalism, which is an efficient way to describe an important class of quantum states, the so-called stabilizer states, and quantum operations on them. Furthermore, graph states, which are a class of stabilizer states associated with graphs, and their applications for measurement-based quantum computation are also mentioned. Based on the stabilizer formalism, we review two interdisciplinary topics. One is the relation between quantum error correction codes and spin glass models, which allows us to analyze the performances of quantum error correction codes by using the knowledge about phases in statistical models. The other is the relation between the stabilizer formalism and partition functions of classical spin models, which provides new quantum and classical algorithms to evaluate partition functions of classical spin models.Comment: 15pages, 4 figures, to appear in Proceedings of 4th YSM-SPIP (Sendai, 14-16 December 2012

    Model based fault diagnosis and prognosis of class of linear and nonlinear distributed parameter systems modeled by partial differential equations

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    With the rapid development of modern control systems, a significant number of industrial systems may suffer from component failures. An accurate yet faster fault prognosis and resilience can improve system availability and reduce unscheduled downtime. Therefore, in this dissertation, model-based prognosis and resilience control schemes have been developed for online prediction and accommodation of faults for distributed parameter systems (DPS). First, a novel fault detection, estimation and prediction framework is introduced utilizing a novel observer for a class of linear DPS with bounded disturbance by modeling the DPS as a set of partial differential equations. To relax the state measurability in DPS, filters are introduced to redesign the detection observer. Upon detecting a fault, an adaptive term is activated to estimate the multiplicative fault and a tuning law is derived to tune the fault parameter magnitude. Then based on this estimated fault parameter together with its failure limit, time-to-failure (TTF) is derived for prognosis. A novel fault accommodation scheme is developed to handle actuator and sensor faults with boundary measurements. Next, a fault isolation scheme is presented to differentiate actuator, sensor and state faults with a limited number of measurements for a class of linear and nonlinear DPS. Subsequently, actuator and sensor fault detection and prediction for a class of nonlinear DPS are considered with bounded disturbance by using a Luenberger observer. Finally, a novel resilient control scheme is proposed for nonlinear DPS once an actuator fault is detected by using an additional boundary measurement. In all the above methods, Lyapunov analysis is utilized to show the boundedness of the closed-loop signals during fault detection, prediction and resilience under mild assumptions --Abstract, page iv

    MDS Variable Generation and Secure Summation with User Selection

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    A collection of KK random variables are called (K,n)(K,n)-MDS if any nn of the KK variables are independent and determine all remaining variables. In the MDS variable generation problem, KK users wish to generate variables that are (K,n)(K,n)-MDS using a randomness variable owned by each user. We show that to generate 11 bit of (K,n)(K,n)-MDS variables for each n{1,2,,K}n \in \{1,2,\cdots, K\}, the minimum size of the randomness variable at each user is 1+1/2++1/K1 + 1/2 + \cdots + 1/K bits. An intimately related problem is secure summation with user selection, where a server may select an arbitrary subset of KK users and securely compute the sum of the inputs of the selected users. We show that to compute 11 bit of an arbitrarily chosen sum securely, the minimum size of the key held by each user is 1+1/2++1/(K1)1 + 1/2 + \cdots + 1/(K-1) bits, whose achievability uses the generation of (K,n)(K,n)-MDS variables for n{1,2,,K1}n \in \{1,2,\cdots,K-1\}

    Design of a compliant wheel for a miniature rover to be used on Mars

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    The Jet Propulsion Laboratory has identified the need for a compliant wheel for a miniature martian rover vehicle. This wheel must meet requirements of minimum mass, linear radial deflection, and reliability in cryogenic conditions over a five year lifespan. Additionally, axial and tangential deflections must be no more than 10 percent of the radial value. The team designed a wheel by use of finite element and dimensionless parameter analysis. Due to the complex geometry of the wheel, a finite element model describing its behavior was constructed to investigate different wheel configurations. Axial and tangential deflections were greatly reduced but did not meet design criteria. A composite material was selected for its high strength, toughness, fatigue resistance, and damping characteristics. The team chose a Kevlar fiber filled thermoplastic composite. This report is divided into four primary sections. First, the introduction section gives background information, defines the task, and discusses the scope and limitations of the project. Second, the alternative designs section introduces alternative design solutions, addresses advantages and disadvantages of each, and identifies the parameters used to determine the best design. Third, the design solution section introduces the methods used to evaluate the alternates, and gives a description of the design process used. Finally, the conclusion and recommendations section evaluates the wheel design, and offers recommendations pertaining to improvement of the design solution

    Coding against stragglers in distributed computation scenarios

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    Data and analytics capabilities have made a leap forward in recent years. The volume of available data has grown exponentially. The huge amount of data needs to be transferred and stored with extremely high reliability. The concept of coded computing , or a distributed computing paradigm that utilizes coding theory to smartly inject and leverage data/computation redundancy into distributed computing systems, mitigates the fundamental performance bottlenecks for running large-scale data analytics. In this dissertation, a distributed computing framework, first for input files distributedly stored on the uplink of a cloud radio access network architecture, is studied. It focuses on that decoding at the cloud takes place via network function virtualization on commercial off-the-shelf servers. In order to mitigate the impact of straggling decoders in this platform, a novel coding strategy is proposed, whereby the cloud re-encodes the received frames via a linear code before distributing them to the decoding processors. Transmission of a single frame is considered first, and upper bounds on the resulting frame unavailability probability as a function of the decoding latency are derived by assuming a binary symmetric channel for uplink communications. Then, the analysis is extended to account for random frame arrival times. In this case, the trade-off between an average decoding latency and the frame error rate is studied for two different queuing policies, whereby the servers carry out per-frame decoding or continuous decoding, respectively. Numerical examples demonstrate that the bounds are useful tools for code design and that coding is instrumental in obtaining a desirable compromise between decoding latency and reliability. In the second part of this dissertation large matrix multiplications are considered which are central to large-scale machine learning applications. These operations are often carried out on a distributed computing platform with a master server and multiple workers in the cloud operating in parallel. For such distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a trade-off between recovery threshold, i.e., the number of workers required to recover the matrix product, and communication load, and the total amount of data to be downloaded from the workers. In addition to exact recovery requirements, security and privacy constraints on the data matrices are imposed, and the recovery threshold as a function of the communication load is studied. First, it is assumed that both matrices contain private information and that workers can collude to eavesdrop on the content of these data matrices. For this problem, a novel class of secure codes is introduced, referred to as secure generalized PolyDot codes, that generalize state-of-the-art non-secure codes for matrix multiplication. Secure generalized PolyDot codes allow a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers while providing perfect secrecy for the two data matrices. Then, a connection between secure matrix multiplication and private information retrieval is studied. It is assumed that one of the data matrices is taken from a public set known to all the workers. In this setup, the identity of the matrix of interest should be kept private from the workers. For this model, a variant of generalized PolyDot codes is presented that can guarantee both secrecy of one matrix and privacy for the identity of the other matrix for the case of no colluding servers
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