Doctoral Theses

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Analysing Multiqubit Entanglement, Nonlocality and Quantum Information Processing Protocols
(Indian Institute of Technology Jodhpur, 2018-10) Kumar, Atul
Quantum entanglement and nonlocal correlations are essential feature of quantum information and computation with no classical analogues. Apart from being central to the fundamentals of quantum mechanics and quantum information, entanglement is also used as an efficient resource for quantum information processing protocols such as dense coding, teleportation, secret sharing, entanglement swapping, and cryptography. The use of entangled resources to achieve efficient and optimal success in quantum information, in comparison to classical resources, is based on nonlocal correlations existing between the particles. The existence of such long-range correlations in quantum systems thus distinguishes the quantum world from its classical counterpart. Although entanglement and nonlocality are well studied in pure two-qubit systems in terms of entanglement of formation (or some physically equivalent quantity) and Bell Inequality, the description and characterization of multiqubit entanglement and nonlocality is much more complex. In fact the entanglement properties of bipartite mixed entangled states itself require a much better physical interpretation. What makes the problem extremely challenging is probably the characteristic trait of complexity. For example, the Bell-type inequalities for three-qubit pure entangled systems itself either fail to distinguish between bipartite and tripartite inequality or fail to identify the presence of nonlocal correlations in a large set of states. The intricacy of the problem increases further considering the real conditions, i.e., by considering interaction between the principal system and environment, leading to decoherence, which adversely impacts the efficiency of quantum systems; in general. Initially, the description of quantum correlations was mainly associated with entanglement and nonlocality- separable states were thought to be classical systems not useful for quantum information and computation. This perception has been questioned recently with the identification of few separable systems exhibiting quantum correlations and showing potential to be used as resources for quantum information processing. Quantum discord, for example, is a measure of genuine nonlocal correlations and captures the nonlocality in entangled as well as separable bipartite systems. Although quantum discord describes all types of nonlocal correlations in the system, it is difficult to obtain analytical expressions to evaluate discord due to the optimization procedure involved. A simple description that can be obtained analytically to distinguish the quantum and classical boundary would therefore be of great value. The Thesis readdresses the question of analysing the entanglement and nonlocality in bipartite and multiqubit entangled systems under real scenarios. For this, we establish analytical relations between nonlocality, state parameters, noise parameters and entanglement measures of several entangled classes in two, three and four-qubit systems under real noisy conditions. We further extend our analysis to study the applications of weak measurement and its reversal operations on the nature of correlations existing between the qubits in noisy environment. Interestingly, we find that more nonlocal correlations in the initially prepared state not always guarantee higher efficiency in quantum communication protocols. The analytical results obtained in the Thesis are in complete agreement with the numerical optimization. Moreover, we also propose a class of two-qubit mixed states which is demonstrated to be much more efficient in quantum information processing, than several other two-qubit pure and mixed states. We further emphasize on the usefulness of mixed entangled resources for quantum information and computation in terms of fully entangled fraction, nonlinear entanglement witness and Horodecki’s measure. In multiqubit systems, we consider to characterize nonlocal properties of three- and four-qubit Greenberger-Horne-Zeilinger states, three-qubit Slice states, three-qubitW andWn-type states under real conditions. We believe that our results will be of significant importance as all the multiqubit states considered for analysing entanglement and nonlocal correlations are experimentally viable. In addition, we also study the usefulness of quantum correlations in an arbitrary two-qubit state in a biased scenario, i.e., a situation where Alice and Bob perform measurements on their respective qubits with a certain bias. Our results show that quantum theory offers advantages over classical theory for the whole range of biasing parameters. The analysis is further extended using fine-grained uncertainty relations to distinguish between classical and quantum correlations. For a generic description to characterize nonlocal correlations between qubits, we propose to use statistical correlation coefficients and modify the two-qubit Bell-CHSH and the three-qubit Svetlichny inequality to analyse nonlocality in two and three-qubit entangled systems, respectively. For two-qubit systems, we demonstrate a necessary and sufficient condition for the violation of modified inequality. Further, we describe a simple way to evaluate geometric discord in terms of correlation coefficients and establish a relation between geometric discord and modified Bell-CHSH inequality to understand and interpret nonlocality in terms of correlation coefficients. For three qubit pure Greenberger-Horne-Zeilinger states, unlike the original inequality which fails to detect nonlocality in a large set of states, our modified inequality identifies the presence of nonlocal correlations in all the Greenberger-Horne-Zeilinger states. In addition, we also study the properties of a special class of non-maximally entangled four-qubit W-type states. Our results show that the class of states are highly useful in quantum information and computation. We generalize our analysis for deterministic transfer of information using N-qubit W-type states as resources. The importance of the proposed class, however, is limited by the realization of experimental set-ups to perform and distinguish multiqubit joint measurements. We, therefore, propose another efficient protocol to use non-maximally entangled W-type states for sharing an optimal bipartite entanglement. Interestingly, our analysis also suggests that four-qubit W-type states can be preferred as resources in comparison to three-qubit W-type states for certain ranges of state parameters. For practical implementation, we further propose a method to experimentally prepare W-type states using standard single and two-qubit measurements.
Content Based Analysis and Retrieval of Architectural Floor Plans
(Indian Institute of Technology Jodhpur, 2018-08) Chattopadhyay, Chiranjoy
Architects often refer to existing layouts, while designing new projects. This process aids in providing insight into how such similar architectural situations were solved in the past. By studying one or several previous reference projects the architect tries to derive a solution for the current problem. The manual look-up process for such similar projects through the layouts can be rather cumbersome. With several architectural projects archived in digital form, the research and development of fast automatic retrieval techniques in floor plans is the need of the hour. Floor plan analysis is a special case of document image understanding. It aims at extracting semantic and structural details of an architectural layout by analysis of the 2D image of the floor plan. Symbol spotting and retrieval in architectural layouts have been solved as individual problems in the past. Moreover, both image and sketch has been used as modalities for the symbol spotting task. Thus, upon performing a keen analysis related to the existing work in the area of architectural floor plans, it was concluded that retrieval in this particular domain is a challenging yet less researched area and has a variety of applications in today’s digital scenario. Specific requirements by buyers during property rent/sale can be met using a composite, automated framework that takes into account semantics, as well as the content inside a floor plan for retrieval. Existing floor plan datasets like Systems Evaluation SYnthetic Documents (SESYD) and Computer Vision Center- Floor Plan (CVC-FP) dataset are not fit for a retrieval task as they don’t suffice in number and variation in the floor plan samples for the task of floor plan retrieval. Keeping in mind the current status of floor plan analysis research, two publicly available benchmark datasets Repository Of BuildIng plaNs (ROBIN) and Sketched-Repository Of BuildIng plaNs (S-ROBIN) are proposed in this thesis that will aid research for the community in the area of floor plan analysis and retrieval. In this thesis, techniques to analyse architectural floor plans and extraction of different features to aid in content based retrieval in floor plans is proposed. In one such attempt, retrieval in floor plans under the query by example paradigm, based on similar overall layout designs as well as the arrangement of the room decor present in the layouts, is proposed. Here query is taken as a floor plan image. A room layout segmentation and adjacent room detection algorithm is presented to represent layouts as an undirected graph. The vertices of the graph represent the rooms, while the edges represent the connectivity between them. Also a novel graph spectral embedding feature is proposed to uniquely represent the layout of the architectural floor plan. This helps in effective and efficient matching of the room layouts. To match the semantic similarity between a pair of floor plans, a two stage matching technique is proposed and high retrieval accuracy is obtained. An interactive graphical user interface to aid users to select, analyse and retrieve similar floor plans is also proposed in this thesis. In another attempt a Convolutional Neural Network (CNN) framework to extract both low and high level semantic features is proposed for floor plan retrieval. Experiments were conducted on publicly available datasets as well as ROBIN. The key contributions in the proposed approach are, a novel deep learning framework to retrieve similar floor plan layouts from repository. Also, in this part of the work the effect of the individual deep convolutional neural network layers for floor plan retrieval task is analysed. Experiments have shown that the deep learning frameworks work very well when the target image itself has a lot of features. Examples include natural images, textual documents, etc. On the other hand for images, which are not that feature rich, the off-the-shelf CNNs are not that effective. Moreover, deep features mostly capture the global similarity in an image. It was envisaged that effective combination of domain specific features may give superior results. The effect of combining various extracted features in a weighted manner to aid in giving preference to a certain feature while retrieval is also proposed. A novel end-to-end framework for extracting high level semantic features like area and room-wise decor arrangement for the task of fine grained retrieval is proposed. Further, a technique to perform feature fusion to aggregate high-level semantic features extracted is also proposed. Weighted feature fusion helps in setting preferences to particular characteristics of the floor plan while retrieval and satisfying specific user demands. In an attempt to explore other query modes than query by example in the form of floor plan image, sketch based retrieval is proposed. Sketch based retrieval comes with its own set of challenges in terms of both representation and recognition. However, this mode of query can aid in better correspondence while capturing the user’s intent in a query. A composite network comprising of Cyclic Generative Adversarial Networks (Cycle-GAN) along with CNN is proposed to bridge the gap between sketch and image domains while retrieval. An improved approach using autencoders in conjunction with Cyclic Generative Adversarial Networks is proposed, which outperforms all other state-of-the-art techniques for sketch based floor plan retrieval by using an efficient domain mapping approach.
Solar Radiation Data Quality Analysis and Gap Filling Approaches
(Indian Institute of Technology Jodhpur, 2018-02) Ravindra, Brahmajosyula
Reliability of solar radiation databases is a matter of concern for engineers and professionals working in solar energy. Variability of solar radiation and its forecasting is of great importance to solar power plant developers and operators. But variation due to climatic conditions needs to be distinguished from various operational and instrumental errors. Proper identification of these errors is required in determining the radiation potential of selected location. The procedures used for solar radiation data quality analysis are critically looked at in this research work in the context of a hot and dry climactic zone. The database selected for analysis pertains to Jodhpur city in Western Rajasthan, India. Solar radiation data quality analysis includes data-step checking, transmittance plot analysis, detailed quality control analysis based on coherence test and gap filling analysis. Data quality tests proposed by various international agencies such as NREL, BSRN, and CWET-MNRE-GIZ are compiled and applied to Jodhpur ground station data. After this procedure, the finalized radiation database was formed to have uniform time intervals. This thesis takes into account various cloud conditions and the efficiency of available gap filling algorithms are compared for these days. To check the impact of these research findings, artificial gaps are introduced in the Jodhpur IMD weather station data (one-year radiation database, 10-minute interval). Several gap filling algorithms are then used to create a corrected database. Comparison is done between available raw radiation database with corrected database and results are discussed in terms of the error metrics and their relevance for solar resource assessment.
Technical Analysis for Short-Term Forecasting of Financial Data and Turn of the Year Effect
(Indian Institute of Technology Jodhpur, 2017-05) Vijay, Vivek
Stock market always attact investors to invest money according to their choice form which large profits can be earned. The fundamental drive behind maximizing this profit is strategy of buying and selling of stocks. Prediction of buying and selling patterns of stocks, or the whole market has always been a challenging task. It is due to the complexity,high volatility and non-inearity in the data. The rate of variation of financial time seres depends on several factors, such as fluctuations, interest rates and volume of transactions. Several statistical and machine learning techniques have been developed to forecast the movement of financial time series. Here we first discuss the trading band approach tob predict buy or sell patterns of a particular stock. These bands suggest buy or sell signals based on historical movements. Originally developed by J.H Hurst, these bands became more popular when a trading band was defined by using Moving Average (MA). The most popular trading band is the Bollinger Band, develoed by John Bollinger in 1980. These are volatility bands placed below and above the moving average of given financial time series. Al-though, the dynamic nature of these band makes them useful for different secrities with standard settings but due to the low decision time they arev unable to capture sudden peaks. We develop a new trading band ( Optimal Band ) which is based on absolute extrema (maxima and minima) and local extrema. We also develop an approach of predicting the buy / sell pattern using Hidden Markov Models. On the other hand, if trading bands and technical indicators exhibit similar partterns for two or more stocks, the decision is made on the basis of return and association with parttern. We first classify the historic data as per their pattern by using the Optimal Band. For each of the categories of pattern, we further divide the whole data into different categories of returns. If the interest lies in the interested in forecasting the returns then the historic value of pattern are used to predict the same but if one is interested in forecasting the returns then the historic value of pattern becomes more useful. Therefore, it becomes important to analyze the strength of dependence between the two variable, returns and patterns. We use historic data to see buying and selling pattern by using the Optimal Band. The pattern data is tham divided into there categories, namely, sell, neutral and buy. This is further used to estimate the future category of returns, high, moderate and low. The whole data is then presented in the form of a 2-dimensional contingency table by using the variables, returns and pattern. In techincal analysis, one of the fundamental drivers is volmue of transactios. We include volume as the third variable with its two categories, namely up and down. This division of volume is parimarily based on the range of historic returns. This creates a 3-dimensional contingency table. there are two possible sets of partial tables corresponding to the variable volume. We test different hypotheses for these tables. turn of the year effect, also known January effect, refers to a phenomenon of changing behaviour of stocks during some trading days of the January month. The presence of this effect is well investigated on high returns of the January month for small capital companies. We provide an evidence of the effect by using buying selling ratio and logistic regression. We predict the probability of next pattern from the given state of pattern ( buy, sell, neutral). The reslts are demonstrated for the data od Cipla Pharmaceutical Pvt. Ltd., Tata Motors and Maruti Suzuki.

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Browsing Doctoral Theses by Subject "Analysis"

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