Accessible information in a two-qubit system through the quantum steering ellipsoids formalism

Capire come ottenere l'informazione accessibile, cioè quanta informazione classica si può estrarre da un processo quantistico, è una delle questioni più intricate e affascinanti nell'ambito della teoria dell'informazione quantistica. Nonostante l'importanza della nozione di informazione accessibile non esistono metodi generali per poterla calcolare, esistono soltanto dei limiti, i più famosi dei quali sono il limite superiore di Holevo e il limite inferiore di Josza-Robb-Wootters. La seguente tesi fa riferimento a un processo che coinvolge due parti, Alice e Bob, che condividono due qubits. Si considera il caso in cui Bob effettua misure binarie sul suo qubit e quindi indirizza lo stato del qubit di Alice in due possibili stati. L'obiettivo di Alice è effettuare la misura ottimale nell'ottica di decretare in quale dei due stati si trova il suo qubit. Lo strumento scelto per studiare questo processo va sotto il nome di 'quantum steering ellipsoids formalism'. Esso afferma che lo stato di un sistema di due qubit può essere descritto dai vettori di Bloch di Alice e Bob e da un ellissoide nella sfera di Bloch di Alice generato da tutte le possibili misure di Bob. Tra tutti gli stati descritti da ellissoidi ce ne sono alcuni che manifestano particolari proprietà, per esempio gli stati di massimo volume. Considerando stati di massimo volume e misure binarie si è riuscito a trovare un limite inferiore all'informazione accessibile per un sistema di due qubit migliore del limite inferiore di Josza-Robb-Wootters. Un altro risultato notevole e inaspettato è che l'intuitiva e giustificata relazione 'distanza tra i punti nell'ellissoide - mutua informazione' non vale quando si confrontano coppie di punti ''vicine'' tra loro e lontane dai più distanti.

Query rewriting in information retrieval: automatic context extraction from local user documents to improve query results

The central objective of research in Information Retrieval (IR) is to discover new techniques to retrieve relevant information in order to satisfy an Information Need. The Information Need is satisfied when relevant information can be provided to the user. In IR, relevance is a fundamental concept which has changed over time, from popular to personal, i.e., what was considered relevant before was information for the whole population, but what is considered relevant now is specific information for each user. Hence, there is a need to connect the behavior of the system to the condition of a particular person and his social context; thereby an interdisciplinary sector called Human-Centered Computing was born. For the modern search engine, the information extracted for the individual user is crucial. According to the Personalized Search (PS), two different techniques are necessary to personalize a search: contextualization (interconnected conditions that occur in an activity), and individualization (characteristics that distinguish an individual). This movement of focus to the individual's need undermines the rigid linearity of the classical model overtaken the ``berry picking'' model which explains that the terms change thanks to the informational feedback received from the search activity introducing the concept of evolution of search terms. The development of Information Foraging theory, which observed the correlations between animal foraging and human information foraging, also contributed to this transformation through attempts to optimize the cost-benefit ratio. This thesis arose from the need to satisfy human individuality when searching for information, and it develops a synergistic collaboration between the frontiers of technological innovation and the recent advances in IR. The search method developed exploits what is relevant for the user by changing radically the way in which an Information Need is expressed, because now it is expressed through the generation of the query and its own context. As a matter of fact the method was born under the pretense to improve the quality of search by rewriting the query based on the contexts automatically generated from a local knowledge base. Furthermore, the idea of optimizing each IR system has led to develop it as a middleware of interaction between the user and the IR system. Thereby the system has just two possible actions: rewriting the query, and reordering the result. Equivalent actions to the approach was described from the PS that generally exploits information derived from analysis of user behavior, while the proposed approach exploits knowledge provided by the user. The thesis went further to generate a novel method for an assessment procedure, according to the "Cranfield paradigm", in order to evaluate this type of IR systems. The results achieved are interesting considering both the effectiveness achieved and the innovative approach undertaken together with the several applications inspired using a local knowledge base.