1,272 research outputs found

    Classical and quantum algorithms for scaling problems

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    This thesis is concerned with scaling problems, which have a plethora of connections to different areas of mathematics, physics and computer science. Although many structural aspects of these problems are understood by now, we only know how to solve them efficiently in special cases.We give new algorithms for non-commutative scaling problems with complexity guarantees that match the prior state of the art. To this end, we extend the well-known (self-concordance based) interior-point method (IPM) framework to Riemannian manifolds, motivated by its success in the commutative setting. Moreover, the IPM framework does not obviously suffer from the same obstructions to efficiency as previous methods. It also yields the first high-precision algorithms for other natural geometric problems in non-positive curvature.For the (commutative) problems of matrix scaling and balancing, we show that quantum algorithms can outperform the (already very efficient) state-of-the-art classical algorithms. Their time complexity can be sublinear in the input size; in certain parameter regimes they are also optimal, whereas in others we show no quantum speedup over the classical methods is possible. Along the way, we provide improvements over the long-standing state of the art for searching for all marked elements in a list, and computing the sum of a list of numbers.We identify a new application in the context of tensor networks for quantum many-body physics. We define a computable canonical form for uniform projected entangled pair states (as the solution to a scaling problem), circumventing previously known undecidability results. We also show, by characterizing the invariant polynomials, that the canonical form is determined by evaluating the tensor network contractions on networks of bounded size

    Vertex Sparsification for Edge Connectivity in Polynomial Time

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    Nonlocal games and their device-independent quantum applications

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    Device-independence is a property of certain protocols that allows one to ensure their proper execution given only classical interaction with devices and assuming the correctness of the laws of physics. This scenario describes the most general form of cryptographic security, in which no trust is placed in the hardware involved; indeed, one may even take it to have been prepared by an adversary. Many quantum tasks have been shown to admit device-independent protocols by augmentation with "nonlocal games". These are games in which noncommunicating parties jointly attempt to fulfil some conditions imposed by a referee. We introduce examples of such games and examine the optimal strategies of players who are allowed access to different possible shared resources, such as entangled quantum states. We then study their role in self-testing, private random number generation, and secure delegated quantum computation. Hardware imperfections are naturally incorporated in the device-independent scenario as adversarial, and we thus also perform noise robustness analysis where feasible. We first study a generalization of the Mermin–Peres magic square game to arbitrary rectangular dimensions. After exhibiting some general properties, these "magic rectangle" games are fully characterized in terms of their optimal win probabilities for quantum strategies. We find that for m×n magic rectangle games with dimensions m,n≄3, there are quantum strategies that win with certainty, while for dimensions 1×n quantum strategies do not outperform classical strategies. The final case of dimensions 2×n is richer, and we give upper and lower bounds that both outperform the classical strategies. As an initial usage scenario, we apply our findings to quantum certified randomness expansion to find noise tolerances and rates for all magic rectangle games. To do this, we use our previous results to obtain the winning probabilities of games with a distinguished input for which the devices give a deterministic outcome and follow the analysis of C. A. Miller and Y. Shi [SIAM J. Comput. 46, 1304 (2017)]. Self-testing is a method to verify that one has a particular quantum state from purely classical statistics. For practical applications, such as device-independent delegated verifiable quantum computation, it is crucial that one self-tests multiple Bell states in parallel while keeping the quantum capabilities required of one side to a minimum. We use our 3×n magic rectangle games to obtain a self-test for n Bell states where one side needs only to measure single-qubit Pauli observables. The protocol requires small input sizes [constant for Alice and O(log n) bits for Bob] and is robust with robustness O(n⁔/ÂČ√Δ), where Δ is the closeness of the ideal (perfect) correlations to those observed. To achieve the desired self-test, we introduce a one-side-local quantum strategy for the magic square game that wins with certainty, we generalize this strategy to the family of 3×n magic rectangle games, and we supplement these nonlocal games with extra check rounds (of single and pairs of observables). Finally, we introduce a device-independent two-prover scheme in which a classical verifier can use a simple untrusted quantum measurement device (the client device) to securely delegate a quantum computation to an untrusted quantum server. To do this, we construct a parallel self-testing protocol to perform device-independent remote state preparation of n qubits and compose this with the unconditionally secure universal verifiable blind quantum computation (VBQC) scheme of J. F. Fitzsimons and E. Kashefi [Phys. Rev. A 96, 012303 (2017)]. Our self-test achieves a multitude of desirable properties for the application we consider, giving rise to practical and fully device-independent VBQC. It certifies parallel measurements of all cardinal and intercardinal directions in the XY-plane as well as the computational basis, uses few input questions (of size logarithmic in n for the client and a constant number communicated to the server), and requires only single-qubit measurements to be performed by the client device

    Distributed energy efficient channel allocation in underlay multicast D2D communications

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    In this paper, we address the optimization of the energy efficiency of underlay multicast device-to-device (D2MD) communications on cellular networks. In particular, we maximize the energy efficiency of both the global network and the individual users considering various fairness factors such as maximum power and minimum rate constraints. For this, we employ a canonical mixed-integer non-linear formulation of the joint power control and resource allocation problem. To cope with its NP-hard nature, we propose a two-stage semi-distributed solution. In the first stage, we find a stable, yet sub-optimal, channel allocation for D2MD groups using a cooperative coalitional game framework that allows co-channel transmission over a set of shared resource blocks and/or transmission over several different channels per D2MD group. In the second stage, a central entity determines the optimal transmission power for each user in the system via fractional programming. We performed extensive simulations to analyze the resulting energy efficiency and attainable transmission rates. The results show that the performance of our semi-distributed approach is very close to that obtained with a pure optimal centralized one.Ministerio de Ciencia, InnovaciĂłn y Universidades | Ref. GO2EDGERED2018-102563-TAgencia Estatal de InvestigaciĂłn | Ref. TEC2017-85587-RAgencia Estatal de InvestigaciĂłn | Ref. RED2018-102563-

    Dynamic (1+\epsilon) : approximate matching size in truly sublinear update time

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    Providing Private and Fast Data Access for Cloud Systems

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    Cloud storage and computing systems have become the backbone of many applications such as streaming (Netflix, YouTube), storage (Dropbox, Google Drive), and computing (Amazon Elastic Computing, Microsoft Azure). To address the ever growing demand for storage and computing requirements of these applications, cloud services are typically im-plemented over a large-scale distributed data storage system. Cloud systems are expected to provide the following two pivotal services for the users: 1) private content access and 2) fast content access. The goal of this thesis is to understand and address some of the challenges that need to be overcome to provide these two services. The first part of this thesis focuses on private data access in distributed systems. In particular, we contribute to the areas of Private Information Retrieval (PIR) and Private Computation (PC). In the PIR problem, there is a user who wishes to privately retrieve a subset of files belonging to a database stored on a single or multiple remote server(s). In the PC problem, the user wants to privately compute functions of a subset of files in the database. The PIR and PC problems seek the most efficient solutions with the minimum download cost that enable the user to retrieve or compute what it wants privately. We establish fundamental bounds on the minimum download cost required for guaran-teeing the privacy requirement in some practical and realistic settings of the PIR and PC problems and develop novel and efficient privacy-preserving algorithms for these settings. In particular, we study the single-server and multi-server settings of PIR in which the user initially has a random linear combination of a subset of files in the database as side in-formation, referred to as PIR with coded side information. We also study the multi-server setting of the PC in which the user wants to privately compute multiple linear combinations of a subset of files in the database, referred to as Private Linear Transformation. The second part of this thesis focuses on fast content access in distributed systems. In particular, we study the use of erasure coding to handle data access requests in distributed storage and computing systems. Service rate region is an important performance metric for coded distributed systems, which expresses the set of all data access request rates that can be simultaneously served by the system. In this context, two classes of problems arise: 1) characterizing the service rate region of a given storage scheme and finding the optimal request allocation, and 2) designing the underlying erasure code to handle a given desired service rate region. As contributions along the first class of problems, we characterize the service rate region of systems with some common coding schemes such as Simplex codes and Reed-Muller codes by introducing two novel techniques: 1) fractional matching and vertex cover on graph representation of codes, and 2) geometric representations of codes. Moreover, along the second class of code design, we establish some lower bounds on the minimum storage required to handle a desired service rate region for a coded distributed system and in some regimes, we design efficient storage schemes that provide the desired service rate region while minimizing the storage requirements

    Algorithmic and Coding-theoretic Methods for Group Testing and Private Information Retrieval

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    In the first part of this dissertation, we consider the Group Testing (GT) problem and its two variants, the Quantitative GT (QGT) problem and the Coin Weighing (CW) problem. An instance of the GT problem includes a ground set of items that includes a small subset of defective items. The GT procedure consists of a number of tests, such that each test indicates whether or not a given subset of items includes one or more defective items. The goal of the GT procedure is to identify the subset of defective items with the minimum number of tests. Motivated by practical scenarios where the outcome of the tests can be affected by noise, we focus on the noisy GT setting, in which the outcome of a test can be flipped with some probability. In the noisy GT setting, the goal is to identify the set of defective items with high probability. We investigate the performance of two variants of the Belief Propagation (BP) algorithm for decoding of noisy non-adaptive GT under the combinatorial model for defective items. Through extensive simulations, we show that the proposed algorithms achieve higher success probability and lower false-negative and false-positive rates when compared to the traditional BP algorithm. We also consider a variation of the probabilistic GT model in which the prior probability of each item to be defective is not uniform and in which there is a certain amount of side information on the distribution of the defective items available to the GT algorithm. This dissertation focuses on leveraging the side information for improving the performance of decoding algorithms for noisy GT. First, we propose a probabilistic model, referred to as an interaction model, that captures the side information about the probability distribution of the defective items. Next, we present a decoding scheme, based on BP, that leverages the interaction model to improve the decoding accuracy. Our results indicate that the proposed algorithm achieves higher success probability and lower false-negative and false-positive rates when compared to the traditional BP, especially in the high noise regime. In the QGT problem, the result of a test reveals the number of defective items in the tested group. This is in contrast to the standard GT where the result of each test is either 1 or 0 depending on whether the tested group contains any defective items or not. In this dissertation, we study the QGT problem for the combinatorial and probabilistic models of defective items. We propose non-adaptive QGT algorithms using sparse graph codes over bi-regular and irregular bipartite graphs, and binary t-error-correcting BCH codes. The proposed schemes provide exact recovery with a probabilistic guarantee, i.e. recover all the defective items with high probability. The proposed schemes outperform existing non-adaptive QGT schemes for the sub-linear regime in terms of the number of tests required to identify all defective items with high probability. The CW problem lies at the intersection of GT and compressed sensing problems. Given a collection of coins and the total weight of the coins, where the weight of each coin is an unknown integer, the problem is to determine the weight of each coin by weighing subsets of coins on a spring scale. The goal is to minimize the average number of weighings over all possible weight configurations. Toward this goal, we propose and analyze a simple and effective adaptive weighing strategy. This is the first non-trivial achievable upper bound on the minimum expected required number of weighings. In the second part of this dissertation, we focus on the private information retrieval problem. In many practical settings, the user needs to retrieve information messages from a server in a periodic manner, over multiple rounds of communication. The messages are retrieved one at a time and the identity of future requests is not known to the server. We study the private information retrieval protocols that ensure that the identities of all the messages retrieved from the server are protected. This scenario can occur in practical settings such as periodic content download from text and multimedia repositories. We refer to this problem of minimizing the rate of data download as online private information retrieval problem. Following the previous line of work by Kadhe et al., we assume that the user knows a subset of messages in the database as side information. The identities of these messages are initially unknown to the server. Focusing on scalar-linear settings, we characterize the per-round capacity, i.e., the maximum achievable download rate at each round. The key idea of our achievability scheme is to combine the data downloaded during the current round and the previous rounds with the original side information messages and use the resulting data as side information for the subsequent rounds

    Dynamic (1+Ï”)(1+\epsilon)-Approximate Matching Size in Truly Sublinear Update Time

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    We show a fully dynamic algorithm for maintaining (1+Ï”)(1+\epsilon)-approximate \emph{size} of maximum matching of the graph with nn vertices and mm edges using m0.5−Ωϔ(1)m^{0.5-\Omega_{\epsilon}(1)} update time. This is the first polynomial improvement over the long-standing O(n)O(n) update time, which can be trivially obtained by periodic recomputation. Thus, we resolve the value version of a major open question of the dynamic graph algorithms literature (see, e.g., [Gupta and Peng FOCS'13], [Bernstein and Stein SODA'16],[Behnezhad and Khanna SODA'22]). Our key technical component is the first sublinear algorithm for (1,Ï”n)(1,\epsilon n)-approximate maximum matching with sublinear running time on dense graphs. All previous algorithms suffered a multiplicative approximation factor of at least 1.4991.499 or assumed that the graph has a very small maximum degree

    Exotic Ground States and Dynamics in Constrained Systems

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    The overarching theme of this thesis is the question of how constraints influence collective behavior. Constraints are crucial in shaping both static and dynamic properties of systems across diverse areas within condensed matter physics and beyond. For example, the simple geometric constraint that hard particles cannot overlap at high density leads to slow dynamics and jamming in glass formers. Constraints also arise effectively at low temperature as a consequence of strong competing interactions in magnetic materials, where they give rise to emergent gauge theories and unconventional magnetic order. Enforcing constraints artificially in turn can be used to protect otherwise fragile quantum information from external noise. This thesis in particular contains progress on the realization of different unconventional phases of matter in constrained systems. The presentation of individual results is organized by the stage of realization of the respective phase. Novel physical phenomena after conceptualization are often exemplified in simple, heuristic models bearing little resemblance of actual matter, but which are interesting enough to motivate efforts with the final goal of realizing them in some way in the lab. One form of progress is then to devise refined models, which retain a degree of simplification while still realizing the same physics and improving the degree of realism in some direction. Finally, direct efforts in realizing either the original models or some refined version in experiment today are mostly two-fold. One route, having grown in importance rapidly during the last two decades, is via the engineering of artificial systems realizing suitable models. The other, more conventional way is to search for realizations of novel phases in materials. The thesis is divided into three parts, where Part I is devoted to the study of two simple models, while artificial systems and real materials are the subject of Part II and Part III respectively. Below, the content of each part is summarized in more detail. After a general introduction to entropic ordering and slow dynamics we present a family of models devised as a lattice analog of hard spheres. These are often studied to explore whether low-dimensional analogues of mean-field glass- and jamming transitions exist, but also serve as the canonical model systems for slow dynamics in granular materials more generally. Arguably the models in this family do not offer a close resemblance of actual granular materials. However, by studying their behavior far from equilibrium, we observe the onset of slow dynamics and a kinetic arrest for which, importantly, we obtain an essentially complete analytical and numerical understanding. Particularly interesting is the fact that this understanding hinges on the (in-)ability to anneal topological defects in the presence of a hardcore constraints, which resonates with some previous proposals for an understanding of the glass transition. As another example of anomalous dynamics arising in a magnetic system, we also present a detailed study of a two-dimensional fracton spin liquid. The model is an Ising system with an energy function designed to give rise to an emergent higher-rank gauge theory at low energy. We show explicitly that the number of zero-energy states in the model scales exponentially with the system size, establishing a finite residual entropy. A purpose-built cluster Monte-Carlo algorithm makes it possible to study the behavior of the model as a function of temperature. We show evidence for a first order transition from a high-temperature paramagnet to a low-temperature phase where correlations match predictions of a higher-rank coulomb phase. Turning away from heuristic models, the second part of the thesis begins with an introduction to quantum error correction, a scheme where constraints are artificially imposed in a quantum system through measurement and feedback. This is done in order to preserve quantum information in the presence of external noise, and is widely believed to be necessary in order to one day harness the full power of quantum computers. Given a certain error-correcting code as well as a noise model, a particularly interesting quantity is the threshold of the code, that is the critical amount of external noise below which quantum error correction becomes possible. For the toric code under independent bit- and phase-flip noise for example, the threshold is well known to map to the paramagnet to ferromagnet transition of the two-dimensional random-bond Ising model along the Nishimori line. Here, we present the first generalization of this mapping to a family of codes with finite rate, that is a family where the number of encoded logical qubits grows linearly with the number of physical qubits. In particular, we show that the threshold of hyperbolic surface codes maps to a paramagnet to ferromagnet transition in what we call the 'dual'' random-bond Ising model on regular tessellations of compact hyperbolic manifolds. This model is related to the usual random-bond Ising model by the Kramers-Wannier duality but distinct from it even on self-dual tessellations. As a corollary, we clarify long-standing issues regarding self-duality of the Ising model in hyperbolic space. The final part of the thesis is devoted to the study of material candidates of quantum spin ice, a three-dimensional quantum spin liquid. The work presented here was done in close collaboration with experiment and focuses on a particular family of materials called dipolar-octupolar pyrochlores. This family of materials is particularly interesting because they might realize novel exotic quantum states such as octupolar spin liquids, while at the same time being described by a relatively simple model Hamiltonian. This thesis contains a detailed study of ground state selection in dipolar-octupolar pyrochlore magnets and its signatures as observable in neutron scattering. First, we present evidence that the two compounds Ce2Zr2O7 and Ce2Sn2O7 despite their similar chemical composition realize an exotic quantum spin liquid state and an ordered state respectively. Then, we also study the ground-state selection in dipolar-octupolar pyrochlores in a magnetic field. Most importantly, we show that the well-known effective one-dimensional physics -- arising when the field is applied along a certain crystallographic axis -- is expected to be stable at experimentally relevant temperatures. Finally, we make predictions for neutron scattering in the large-field phase and compare these to measurements on Ce2Zr2O7
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