Towards Hybrid Classical-Quantum Computation Structures in Wirelessly-Networked Systems

With unprecedented increases in traffic load in today's wireless networks, design challenges shift from the wireless network itself to the computational support behind the wireless network. In this vein, there is new interest in quantum-compute approaches because of their potential to substantially speed up processing, and so improve network throughput. However, quantum hardware that actually exists today is much more susceptible to computational errors than silicon-based hardware, due to the physical phenomena of decoherence and noise. This paper explores the boundary between the two types of computation---classical-quantum hybrid processing for optimization problems in wireless systems---envisioning how wireless can simultaneously leverage the benefit of both approaches. We explore the feasibility of a hybrid system with a real hardware prototype using one of the most advanced experimentally available techniques today, reverse quantum annealing. Preliminary results on a low-latency, large MIMO system envisioned in the 5G New Radio roadmap are encouraging, showing approximately 2--10X better performance in terms of processing time than prior published results.Comment: HotNets 2020: Nineteenth ACM Workshop on Hot Topics in Networks (https://doi.org/10.1145/3422604.3425924

Design of of model-based controllers via parametric programming

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Exponential Qubit Reduction in Optimization for Financial Transaction Settlement

We extend the qubit-efficient encoding presented in [Tan et al., Quantum 5, 454 (2021)] and apply it to instances of the financial transaction settlement problem constructed from data provided by a regulated financial exchange. Our methods are directly applicable to any QUBO problem with linear inequality constraints. Our extension of previously proposed methods consists of a simplification in varying the number of qubits used to encode correlations as well as a new class of variational circuits which incorporate symmetries, thereby reducing sampling overhead, improving numerical stability and recovering the expression of the cost objective as a Hermitian observable. We also propose optimality-preserving methods to reduce variance in real-world data and substitute continuous slack variables. We benchmark our methods against standard QAOA for problems consisting of 16 transactions and obtain competitive results. Our newly proposed variational ansatz performs best overall. We demonstrate tackling problems with 128 transactions on real quantum hardware, exceeding previous results bounded by NISQ hardware by almost two orders of magnitude.Comment: 16 pages, 8 figure

Solving combinatorial optimization problems using quantum computing: a case study for the QAP

Quantum computing is one of the most researched areas in computer science and withone of the greatest future prospects thanks to the new discoveries and methodologies thatcan provide approaches of a different kind to tackle problems and find solutions. One ofsuch methodologies is none other than quantum annealing, a metaheuristic focused on thequalities of quantum mechanics. Combinatorial Optimization (CO) problems have always been a hassle to find solutionsin large scale problems owing to their complexity and resource consumption. However,these can be transformed into an equivalent Quadratic Unconstrained Binary Optimization(QUBO) model that is constraint-free and its variables are (binary) decision variables.What is best, QUBO models can be efficiently solve with quantum annealing in a quantumcomputer. The objective of the project will be the study of all the particular functionalities andproperties of each aspect of all the transformation chain for the specific case study withthe QAP problem. The implementation of this problem, its transformation into a QUBOmodel and the solution obtained through quantum annealing are the key points that leadto harness the potential of quantum computing and set its current limits

Xqx Based Modeling For General Integer Programming Problems

We present a new way to model general integer programming (IP) problems with in- equality and equality constraints using XQX. We begin with the definition of IP problems folloby their practical applications, and then present the existing XQX based models to handle such problems. We then present our XQX model for general IP problems (including binary IP) with equality and inequality constraints, and also show how this model can be applied to problems with just inequality constraints. We then present the local optima based solution procedure for our XQX model. We also present new theorems and their proofs for our XQX model. Next, we present a detailed literature survey on the 0-1 multidimensional knapsack problem (MDKP) and apply our XQX model using our simple heuristic procedure to solve benchmark problems. The 0-1 MDKP is a binary IP problem with inequality con- straints and variables with binary values. We apply our XQX model using a heuristics we developed on 0-1 MDKP problems of various sizes and found that our model can handle any problem sizes and can provide reasonable quality results in reasonable time. Finally, we apply our XQX model developed for general integer programming problems on the general multi-dimensional knapsack problems. The general MDKP is a general IP problem with inequality constraints where the variables are positive integers. We apply our XQX model on GMDKP problems of various sizes and find that it can provide reasonable quality results in reasonable time. We also find that it can handle problems of any size and provide fea- sible and good quality solutions irrespective of the starting solutions. We conclude with a discussion of some issues related with our XQX model

Solving combinatorial optimization problems using quantum computing: a case study for the QAP

Quantum computing is one of the most researched areas in computer science and withone of the greatest future prospects thanks to the new discoveries and methodologies thatcan provide approaches of a different kind to tackle problems and find solutions. One ofsuch methodologies is none other than quantum annealing, a metaheuristic focused on thequalities of quantum mechanics. Combinatorial Optimization (CO) problems have always been a hassle to find solutionsin large scale problems owing to their complexity and resource consumption. However,these can be transformed into an equivalent Quadratic Unconstrained Binary Optimization(QUBO) model that is constraint-free and its variables are (binary) decision variables.What is best, QUBO models can be efficiently solve with quantum annealing in a quantumcomputer. The objective of the project will be the study of all the particular functionalities andproperties of each aspect of all the transformation chain for the specific case study withthe QAP problem. The implementation of this problem, its transformation into a QUBOmodel and the solution obtained through quantum annealing are the key points that leadto harness the potential of quantum computing and set its current limits

Uma nova relaxação quadrática para variáveis binárias com aplicações a confiabilidade de redes de energia elétrica, a segmentação de imagens médicas de nervos e a problemas de geometria de distâncias

Orientador: Christiano Lyra FilhoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Como o título sugere, o foco desta pesquisa é o desenvolvimento de uma nova relaxação quadrática para problemas binários, sua formalização em resultados teóricos, e a aplicação dos novos conceitos em aplicações à confiabilidade de redes de energia elétrica, à segmentação de imagens médicas de nervos e à problemas de geometria de distâncias. Modelos matemáticos contendo va-riáveis de decisões binárias podem ser usados para encontrar as melhores soluções em processos de tomada de decisões, normalmente caracterizando problemas de otimização combinatória difíceis. A solução desses problemas em aplicações de interesse prático requer um grande esforço computacional; por isso, ao longo dos últimos anos, têm sido objeto de pesquisas na área de metaheurísticas. As ideias aqui desenvolvidas abrem novas perspectivas para a abordagem desses problemas apoiando-se em métodos de otimização não-lineares, área que vem sendo povoada por "solvers" muito eficientes. Inicialmente, explorando aspectos formais, a relaxação desenvolvida é parti-cularizada para um problema de otimização quadrática binária irrestrita. O relaxamento permite o desenvolvimento de três estruturas para abordar esta classe de problemas, e explora a convexidade da função objetivo para obter melhorias computacionais. Estudos de casos compararam o relaxamento proposto com os relaxamentos similares apresentados na literatura. Foram desenvolvidas três aplicações para os desenvolvimentos teóricos da pesquisa. A primeira aplicação envolve a melhoria da confiabilidade de redes de energia elétrica. Especificamente, aborda o problema de definir a melhor alternativa para a alocação de sensores na rede, o que permite reduzir os efeitos de ocorrências indesejáveis e ampliar a resiliência das redes. A segunda aplicação envolve o problema de segmentação de imagens médicas associadas a estruturas de nervos. A abordagem proposta interpreta o problema de segmentação como um problema de otimização binária, onde medir cada axônio significa encontrar um ciclo Hamiltoniano, um caso do problema do caixeiro viajante; a solução desses problemas fornece a estatística descritiva para um conjunto de axônios, incluindo o número (de axônios), os diâmetros e as áreas ocupadas. A última aplicação elabora um modelo matemático para o problema de geometria de distâncias sem designação, área ainda pouco estudada e com muitos aspectos em aberto. A relaxação desenvolvida na pesquisa permitiu resolver instâncias com mais de vinte mil variáveis binárias. Esses resultados são bons indicadores dos benefícios alcançáveis com os aspectos teóricos da pesquisa, e abrem novas perspectivas para as aplicações, que incluem inovações em nanotecnologia e bioengenhariaAbstract: As the title suggests, the focus this research is the development of a new quadratic relaxation for binary problems, its formalization in theoretical results, and the application of the new concepts in applications to the reliability of electric power networks, segmentation of nerve root images, and distance geometry problems. Mathematical models with binary decision variables can be used to find the best solutions for decision-making process, usually leading to difficult combinatorial optimization problems. The solution to these problems in practical applications requires a high computational effort; therefore, over the past years it has been the subject of research in the area of metaheuristics. The ideas developed in this thesis open new perspectives for addressing these problems using nonlinear optimization approaches, an area that has been populated by very efficient solvers. The initial developments explore the formal aspects of the relaxation in the context of a quadratic unconstrained binary optimization problem. The use of the proposed relaxation allows to create three structures to deal with this class of problems, and explores the objective function convexity to improve the computational performance. Case studies compare the proposed relaxation with the previous relaxations proposed in the literature. Three new applications were developed to explore the theoretical developments of this research. The first application concerns the improvement of the reliability of electric power distribution networks. Specifically, it deals with the problem of defining the best allocation for remote fault sensor, allowing to reduce the consequence of the faults and to improve the resilience of the networks. The second application explores the segmentation of medical images related to nerve root structures. The proposed approach regards the segmentation problem as a binary optimization problem, where measuring each axon is equivalent to finding a Hamiltonian cycle for a variant of the traveling salesman problem; the solution to these problems provides the descriptive statistics of the axon set, including the number of axons, their diameters, and the area used by each axon. The last application designs a mathematical model for the unassigned distance geometry problem, an incipient research area with many open problems. The relaxation developed in this research allowed to solve instances with more than twenty thousand binary variables. These results can be seen as good indicators of the benefits attainable with the theoretical aspects of the research, and opens new perspectives for applications, which include innovations in nanotechnology and bio-engineeringDoutoradoAutomaçãoDoutora em Engenharia Elétrica148400/2016-7CNP

Continuous Biochemical Processing: Investigating Novel Strategies to Produce Sustainable Fuels and Pharmaceuticals

Biochemical processing methods have been targeted as one of the potential renewable strategies for producing commodities currently dominated by the petrochemical industry. To design biochemical systems with the ability to compete with petrochemical facilities, inroads are needed to transition from traditional batch methods to continuous methods. Recent advancements in the areas of process systems and biochemical engineering have provided the tools necessary to study and design these continuous biochemical systems to maximize productivity and substrate utilization while reducing capital and operating costs. The first goal of this thesis is to propose a novel strategy for the continuous biochemical production of pharmaceuticals. The structural complexity of most pharmaceutical compounds makes chemical synthesis a difficult option, facilitating the need for their biological production. To this end, a continuous, multi-feed bioreactor system composed of multiple independently controlled feeds for substrate(s) and media is proposed to freely manipulate the bioreactor dilution rate and substrate concentrations. The optimal feed flow rates are determined through the solution to an optimal control problem where the kinetic models describing the time-variant system states are used as constraints. This new bioreactor paradigm is exemplified through the batch and continuous cultivation of β-carotene, a representative product of the mevalonate pathway, using Saccharomyces cerevisiae strain mutant SM14. The second goal of this thesis is to design continuous, biochemical processes capable of economically producing alternative liquid fuels. The large-scale, continuous production of ethanol via consolidated bioprocessing (CBP) is examined. Optimal process topologies for the CBP technology selected from a superstructure considering multiple biomass feeds, chosen from those available across the United States, and multiple prospective pretreatment technologies. Similarly, the production of butanol via acetone-butanol-ethanol (ABE) fermentation is explored using process intensification to improve process productivity and profitability. To overcome the inhibitory nature of the butanol product, the multi-feed bioreactor paradigm developed for pharmaceutical production is utilized with in situ gas stripping to simultaneously provide dilution effects and selectively remove the volatile ABE components. Optimal control and process synthesis techniques are utilized to determine the benefits of gas stripping and design a butanol production process guaranteed to be profitable