Evolution of whole genomes through inversions:models and algorithms for duplicates, ancestors, and edit scenarios

Advances in sequencing technology are yielding DNA sequence data at an alarming rate – a rate reminiscent of Moore's law. Biologists' abilities to analyze this data, however, have not kept pace. On the other hand, the discrete and mechanical nature of the cell life-cycle has been tantalizing to computer scientists. Thus in the 1980s, pioneers of the field now called Computational Biology began to uncover a wealth of computer science problems, some confronting modern Biologists and some hidden in the annals of the biological literature. In particular, many interesting twists were introduced to classical string matching, sorting, and graph problems. One such problem, first posed in 1941 but rediscovered in the early 1980s, is that of sorting by inversions (also called reversals): given two permutations, find the minimum number of inversions required to transform one into the other, where an inversion inverts the order of a subpermutation. Indeed, many genomes have evolved mostly or only through inversions. Thus it becomes possible to trace evolutionary histories by inferring sequences of such inversions that led to today's genomes from a distant common ancestor. But unlike the classic edit distance problem where string editing was relatively simple, editing permutation in this way has proved to be more complex. In this dissertation, we extend the theory so as to make these edit distances more broadly applicable and faster to compute, and work towards more powerful tools that can accurately infer evolutionary histories. In particular, we present work that for the first time considers genomic distances between any pair of genomes, with no limitation on the number of occurrences of a gene. Next we show that there are conditions under which an ancestral genome (or one close to the true ancestor) can be reliably reconstructed. Finally we present new methodology that computes a minimum-length sequence of inversions to transform one permutation into another in, on average, O(n log n) steps, whereas the best worst-case algorithm to compute such a sequence uses O(n√n log n) steps

Quantum information outside quantum information

Quantum theory, as counter-intuitive as a theory can get, has turned out to make predictions of the physical world that match observations so precisely that it has been described as the most accurate physical theory ever devised. Viewing quantum entanglement, superposition and interference not as undesirable necessities but as interesting resources paved the way to the development of quantum information science. This area studies the processing, transmission and storage of information when one accounts that information is physical and subjected to the laws of nature that govern the systems it is encoded in. The development of the consequences of this idea, along with the great advances experienced in the control of individual quantum systems, has led to what is now known as the second quantum revolution, in which quantum information science has emerged as a fully-grown field. As such, ideas and tools developed within the framework of quantum information theory begin to permeate to other fields of research. This Ph.D. dissertation is devoted to the use of concepts and methods akin to the field of quantum information science in other areas of research. In the same way, it also considers how encoding information in quantum degrees of freedom may allow further development of well-established research fields and industries. This is, this thesis aims to the study of quantum information outside the field of quantum information. Four different areas are visited. A first question posed is that of the role of quantum information in quantum field theory, with a focus in the quantum vacuum. It is known that the quantum vacuum contains entanglement, but it remains unknown whether it can be accessed and exploited in experiments. We give crucial steps in this direction by studying the extraction of vacuum entanglement in realistic models of light-matter interaction, and by giving strict mathematical conditions of general applicability that must be fulfilled for extraction to be possible at all. Another field where quantum information methods can offer great insight is in that of quantum thermodynamics, where the idealizations made in macroscopic thermodynamics break down. Making use of a quintessential framework of quantum information and quantum optics, we study the cyclic operation of a microscopic heat engine composed by a single particle reciprocating between two finite-size baths, focusing on the consequences of the removal of the macroscopic idealizations. One more step down the stairs to applications in society, we analyze the impact that encoding information in quantum systems and processing it in quantum computers may have in the field of machine learning. A great desideratum in this area, largely obstructed by computational power, is that of explainable models which not only make predictions but also provide information about the decision process that triggers them. We develop an algorithm to train neural networks using explainable techniques that exploits entanglement and superposition to execute efficiently in quantum computers, in contrast with classical counterparts. Furthermore, we run it in state-of-the-art quantum computers with the aim of assessing the viability of realistic implementations. Lastly, and encompassing all the above, we explore the notion of causality in quantum mechanics from an information-theoretic point of view. While it is known since the work of John S. Bell in 1964 that, for a same causal pattern, quantum systems can generate correlations between variables that are impossible to obtain employing only classical systems, there is an important lack of tools to study complex causal effects whenever a quantum behavior is expected. We fill this gap by providing general methods for the characterization of the quantum correlations achievable in complex causal patterns. Closing the circle, we make use of these tools to find phenomena of fundamental and experimental relevance back in quantum information.La teoría cuántica, la más extraña y antiintuitiva de las teorías físicas, es también considerada como la teoría más precisa jamás desarrollada. La interpretación del entrelazamiento, la superposición y la interferencia como interesantes recursos aprovechables cimentó el desarrollo de la teoría cuántica de la información (QIT), que estudia el procesado, transmisión y almacenamiento de información teniendo en cuenta que ésta es física, en tanto a que está sujeta a las leyes de la naturaleza que gobiernan los sistemas en que se codifica. El desarrollo de esta idea, en conjunción con los recientes avances en el control de sistemas cuánticos individuales, ha dado lugar a la conocida como segunda revolución cuántica, en la cual la QIT ha emergido como un área de estudio con denominación propia. A consecuencia de su desarrollo actual, ideas y herramientas creadas en su seno comienzan a permear a otros ámbitos de investigación. Esta tesis doctoral está dedicada a la utilización de conceptos y métodos originales del campo de información cuántica en otras áreas. También considera cómo la codificación de información en grados de libertad cuánticos puede afectar el futuro desarrollo de áreas de investigación e industrias bien establecidas. Es decir, esta tesis tiene como objetivo el estudio de la información cuántica fuera de la información cuántica, haciendo hincapié en cuatro ámbitos diferentes. Una primera cuestión propuesta es la del papel de la información cuántica en la teoría cuántica de campos, con especial énfasis en el vacío cuántico. Es conocido que el vacío cuántico contiene entrelazamiento, pero aún se desconoce éste es accesible para su uso en realizaciones experimentales. En esta tesis se dan pasos cruciales en esta dirección mediante el estudio de la extracción de entrelazamiento en modelos realistas de la interacción materia-radiación, y dando condiciones matemáticas estrictas que deben ser satisfechas para que dicha extracción sea posible. Otro campo en el cual métodos propios de QIT pueden ofrecer nuevos puntos de vista es en termodinámica cuántica. A través del uso de un marco de trabajo ampliamente utilizado en información y óptica cuánticas, estudiamos la operación cíclica de un motor térmico microscópico que alterna entre dos baños térmicos de tamaño finito, prestando especial atención a las consecuencias de la eliminación de las idealizaciones macroscópicas utilizadas en termodinámica macroscópica. Acercándonos a aplicaciones industriales, analizamos el potencial impacto de codificar y procesar información en sistemas cuánticos en el ámbito del aprendizaje automático. Un fin codiciado en esta área, inaccesible debido a su coste computacional, es el de modelos explicativos que realicen predicciones, y además ofrezcan información acerca del proceso de decisión que las genera. Presentamos un algoritmo de entrenamiento de redes neuronales con técnicas explicativas que hace uso del entrelazamiento y la superposición para tener una ejecución eficiente en ordenadores cuánticos, en comparación con homólogos clásicos. Además, ejecutamos el algoritmo en ordenadores cuánticos contemporáneos con el objetivo de evaluar la viabilidad de implementaciones realistas. Finalmente, y englobando todo lo anterior, exploramos la noción de causalidad en mecánica cuántica desde el punto de vista de la teoría de la información. A pesar de que es conocido que para un mismo patrón causal existen sistemas cuánticos que dan lugar a correlaciones imposibles de generar por mediación de sistemas clásicos, existe una notable falta de herramientas para estudiar efectos causales cuánticos complejos. Cubrimos esta falta mediante métodos generales para la caracterización de las correlaciones cuánticas que pueden ser generadas en estructuras causales complejas. Cerrando el círculo, usamos estas herramientas para encontrar fenómenos de relevancia fundamental y experimental en la información cuántic

The effects of bias on sampling algorithms and combinatorial objects

Markov chains are algorithms that can provide critical information from exponentially large sets efficiently through random sampling. These algorithms are ubiquitous across numerous scientific and engineering disciplines, including statistical physics, biology and operations research. In this thesis we solve sampling problems at the interface of theoretical computer science with applied computer science, discrete mathematics, statistical physics, chemistry and economics. A common theme throughout each of these problems is the use of bias. The first problem we study is biased permutations which arise in the context of self-organizing lists. Here we are interested in the mixing time of a Markov chain that performs nearest neighbor transpositions in the non-uniform setting. We are given "positively biased'' probabilities $\{p_{i,j} \geq 1/2 \}$ for all $i < j$ and let $p_{j,i} = 1-p_{i,j}$. In each step, the chain chooses two adjacent elements~$k,$ and~$\ell$ and exchanges their positions with probability $p_{ \ell, k}$. We define two general classes of bias and give the first proofs that the chain is rapidly mixing for both. We also demonstrate that the chain is not always rapidly mixing by constructing an example requiring exponential time to converge to equilibrium. Next we study rectangular dissections of an $n \times n$ lattice region into rectangles of area $n$, where $n=2^k$ for an even integer $k.$ We consider a weighted version of a natural edge flipping Markov chain where, given a parameter $\lambda > 0,$ we would like to generate each rectangular dissection (or dyadic tiling)~$\sigma$ with probability proportional to $\lambda^{|\sigma|},$ where $|\sigma|$ is the total edge length. First we look at the restricted case of dyadic tilings, where each rectangle is required to have the form $R = [s2^{u},(s+1)2^{u}]\times [t2^{v},(t+1)2^{v}],$ where $s, t, u$ and~$v$ are nonnegative integers. Here we show there is a phase transition: when $\lambda 1,$ the mixing time is $\exp(\Omega({n^2}))$. The behavior for general rectangular dissections is more subtle, and we show the chain requires exponential time when $\lambda >1$ and when $\lambda <1.$ The last two problems we study arise directly from applications in chemistry and economics. Colloids are binary mixtures of molecules with one type of molecule suspended in another. It is believed that at low density typical configurations will be well-mixed throughout, while at high density they will separate into clusters. We characterize the high and low density phases for a general family of discrete interfering colloid models by showing that they exhibit a "clustering property" at high density and not at low density. The clustering property states that there will be a region that has very high area to perimeter ratio and very high density of one type of molecule. A special case is mixtures of squares and diamonds on $\Z^2$ which correspond to the Ising model at fixed magnetization. Subsequently, we expanded techniques developed in the context of colloids to give a new rigorous underpinning to the Schelling model, which was proposed in 1971 by economist Thomas Schelling to understand the causes of racial segregation. Schelling considered residents of two types, where everyone prefers that the majority of his or her neighbors are of the same type. He showed through simulations that even mild preferences of this type can lead to segregation if residents move whenever they are not happy with their local environments. We generalize the Schelling model to include a broad class of bias functions determining individuals happiness or desire to move. We show that for any influence function in this class, the dynamics will be rapidly mixing and cities will be integrated if the racial bias is sufficiently low. However when the bias is sufficiently high, we show the dynamics take exponential time to mix and a large cluster of one type will form.Ph.D

Quantum information outside quantum information

Premi Extraordinari de Doctorat, promoció 2018-2019. Àmbit de CiènciesQuantum theory, as counter-intuitive as a theory can get, has turned out to make predictions of the physical world that match observations so precisely that it has been described as the most accurate physical theory ever devised. Viewing quantum entanglement, superposition and interference not as undesirable necessities but as interesting resources paved the way to the development of quantum information science. This area studies the processing, transmission and storage of information when one accounts that information is physical and subjected to the laws of nature that govern the systems it is encoded in. The development of the consequences of this idea, along with the great advances experienced in the control of individual quantum systems, has led to what is now known as the second quantum revolution, in which quantum information science has emerged as a fully-grown field. As such, ideas and tools developed within the framework of quantum information theory begin to permeate to other fields of research. This Ph.D. dissertation is devoted to the use of concepts and methods akin to the field of quantum information science in other areas of research. In the same way, it also considers how encoding information in quantum degrees of freedom may allow further development of well-established research fields and industries. This is, this thesis aims to the study of quantum information outside the field of quantum information. Four different areas are visited. A first question posed is that of the role of quantum information in quantum field theory, with a focus in the quantum vacuum. It is known that the quantum vacuum contains entanglement, but it remains unknown whether it can be accessed and exploited in experiments. We give crucial steps in this direction by studying the extraction of vacuum entanglement in realistic models of light-matter interaction, and by giving strict mathematical conditions of general applicability that must be fulfilled for extraction to be possible at all. Another field where quantum information methods can offer great insight is in that of quantum thermodynamics, where the idealizations made in macroscopic thermodynamics break down. Making use of a quintessential framework of quantum information and quantum optics, we study the cyclic operation of a microscopic heat engine composed by a single particle reciprocating between two finite-size baths, focusing on the consequences of the removal of the macroscopic idealizations. One more step down the stairs to applications in society, we analyze the impact that encoding information in quantum systems and processing it in quantum computers may have in the field of machine learning. A great desideratum in this area, largely obstructed by computational power, is that of explainable models which not only make predictions but also provide information about the decision process that triggers them. We develop an algorithm to train neural networks using explainable techniques that exploits entanglement and superposition to execute efficiently in quantum computers, in contrast with classical counterparts. Furthermore, we run it in state-of-the-art quantum computers with the aim of assessing the viability of realistic implementations. Lastly, and encompassing all the above, we explore the notion of causality in quantum mechanics from an information-theoretic point of view. While it is known since the work of John S. Bell in 1964 that, for a same causal pattern, quantum systems can generate correlations between variables that are impossible to obtain employing only classical systems, there is an important lack of tools to study complex causal effects whenever a quantum behavior is expected. We fill this gap by providing general methods for the characterization of the quantum correlations achievable in complex causal patterns. Closing the circle, we make use of these tools to find phenomena of fundamental and experimental relevance back in quantum information.La teoría cuántica, la más extraña y antiintuitiva de las teorías físicas, es también considerada como la teoría más precisa jamás desarrollada. La interpretación del entrelazamiento, la superposición y la interferencia como interesantes recursos aprovechables cimentó el desarrollo de la teoría cuántica de la información (QIT), que estudia el procesado, transmisión y almacenamiento de información teniendo en cuenta que ésta es física, en tanto a que está sujeta a las leyes de la naturaleza que gobiernan los sistemas en que se codifica. El desarrollo de esta idea, en conjunción con los recientes avances en el control de sistemas cuánticos individuales, ha dado lugar a la conocida como segunda revolución cuántica, en la cual la QIT ha emergido como un área de estudio con denominación propia. A consecuencia de su desarrollo actual, ideas y herramientas creadas en su seno comienzan a permear a otros ámbitos de investigación. Esta tesis doctoral está dedicada a la utilización de conceptos y métodos originales del campo de información cuántica en otras áreas. También considera cómo la codificación de información en grados de libertad cuánticos puede afectar el futuro desarrollo de áreas de investigación e industrias bien establecidas. Es decir, esta tesis tiene como objetivo el estudio de la información cuántica fuera de la información cuántica, haciendo hincapié en cuatro ámbitos diferentes. Una primera cuestión propuesta es la del papel de la información cuántica en la teoría cuántica de campos, con especial énfasis en el vacío cuántico. Es conocido que el vacío cuántico contiene entrelazamiento, pero aún se desconoce éste es accesible para su uso en realizaciones experimentales. En esta tesis se dan pasos cruciales en esta dirección mediante el estudio de la extracción de entrelazamiento en modelos realistas de la interacción materia-radiación, y dando condiciones matemáticas estrictas que deben ser satisfechas para que dicha extracción sea posible. Otro campo en el cual métodos propios de QIT pueden ofrecer nuevos puntos de vista es en termodinámica cuántica. A través del uso de un marco de trabajo ampliamente utilizado en información y óptica cuánticas, estudiamos la operación cíclica de un motor térmico microscópico que alterna entre dos baños térmicos de tamaño finito, prestando especial atención a las consecuencias de la eliminación de las idealizaciones macroscópicas utilizadas en termodinámica macroscópica. Acercándonos a aplicaciones industriales, analizamos el potencial impacto de codificar y procesar información en sistemas cuánticos en el ámbito del aprendizaje automático. Un fin codiciado en esta área, inaccesible debido a su coste computacional, es el de modelos explicativos que realicen predicciones, y además ofrezcan información acerca del proceso de decisión que las genera. Presentamos un algoritmo de entrenamiento de redes neuronales con técnicas explicativas que hace uso del entrelazamiento y la superposición para tener una ejecución eficiente en ordenadores cuánticos, en comparación con homólogos clásicos. Además, ejecutamos el algoritmo en ordenadores cuánticos contemporáneos con el objetivo de evaluar la viabilidad de implementaciones realistas. Finalmente, y englobando todo lo anterior, exploramos la noción de causalidad en mecánica cuántica desde el punto de vista de la teoría de la información. A pesar de que es conocido que para un mismo patrón causal existen sistemas cuánticos que dan lugar a correlaciones imposibles de generar por mediación de sistemas clásicos, existe una notable falta de herramientas para estudiar efectos causales cuánticos complejos. Cubrimos esta falta mediante métodos generales para la caracterización de las correlaciones cuánticas que pueden ser generadas en estructuras causales complejas. Cerrando el círculo, usamos estas herramientas para encontrar fenómenos de relevancia fundamental y experimental en la información cuánticaPostprint (published version