Macrocoherence is the concept of quantum mechanics being scaled up to the macroscopic level where everyday physical systems should inhibit quantum mechanical properties, however this is not what is observed. Through the use of Leggett-Garg inequalities, one can infer if there is a fundamental quantum mechanical behavior of the system being observed. Then, using violations of these inequalities, this paper discusses the possibility of extracting useful measures of how macroscopic a system can be. Utilizing an analogy with the measures of locality through Bell's inequalities, the scope of what a measure should consist of is discussed. A measure should be proper in the sense that a baseline of 0 should be obtained from system that never violates an LGI. Further, it is proposed that a measure should extend naturally to all orders of LGIs without ranking quantum systems differently. With these in mind two measures are proposed, one utilizing the integral over the violated area of a LGI over time whereas the other uses inner products over a matrix defined elementwise as a specified LGI. The measures scopes are discussed and their applications are showcased for some analytical quantum systems. Though functional, the measures are found to lack a resource tied to its value complicating the conceptualization of what is being measured. It is concluded that a new effort to find a true measure of macrocoherence should start from the concept of a resource.

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This work explores the possibilities to use quantum computers and quantum based language models for machine translation. Precise translation requires vast expertise and knowledge of various languages, thus machine translationis still far from superseding humans. Quantum computers could improve machine translation due to their high computational power, as they benefit from properties such as superposition and entanglement to process data faster and in parallel. We focused our work on the DIStributional COmpositional CATegorical (DisCoCat) semantics and its python toolbox DisCoPy developed by [1]. We built and transformed simple, complex, and negative English and Spanish sentences to DisCoCat diagrams. Those diagrams are then used as input to quantum circuits, allowing us to perform calculations in NISQ devices providedby IBMQ. The calculations show that a quantum computer can understand the meaning of simple and complex sentences in different languages, and this is the first step to perform translation with Quantum Computers. In addition, we worked on preserving sentence meaning by measuring the cosine similarity between two vectorised sentences and obtained sentence similarities scores of 95%.

Dark matter is required in the universe to explain certain phenomenona, like the dynamics of stars and temperature fluctuations of the cosmic microwave background. This thesis focuses on the production of dark matter in the ATLAS experiment at the Large Hadron Collider, where the dark matter is detected through momentum conservation. The models of interest are simplified dark matter models consisting of a pair of top quarks produced together with a mediator. It is the mediator, scalar or pseudoscalar having a mass of 20 GeV  that decays to dark matter particles each having a mass of 1 GeV. The final state considered in this thesis consists of jets, one lepton and missing transverse momentum.  The coupling constants between the mediator and the top quarks and the mediator and the dark matter particles is set to 1.  The goal of the work presented in this thesis is  to improve the searches for dark matter of dark matter, by studying the distributions of variables that can separate the signal from various background processes. This is done by loosening the requirement on missing transverse momentum in the dark matter searches from 230 GeV to 70 GeV and study the low missing transverse momenta samples. This thesis then finds that the low missing transverse momentum scalar sample has the highest yield of all samples studied. Moreover, the transverse mass selection efficiency for the low missing transverse momentum sample is found to be low and needs to be improved. The last conclusion presented is that the azimuthal angle between the lepton and the missing transverse momentum can be used to differentiate the mediator types in both the low and high missing transverse momentum samples.

Equivariant localization is a technique can be used to reduce the dimensionality of integral for the manifold with a Lie group action on. Such technique can beapplied to simply the calculation of path integral with certain type of symmetries.In this thesis, we will first introduce the language of super algebra and supergeometry, then discuss the localization formula and Mathai-Quillen formalismaccording to supergeometry.In the last chapter, we will equivariantize the formalism we made and discuss how this being manifested in odd tangent bundleand holomorphic line bundle. Such methodology is very powerful in providing areasonable regularization to eradicate divergence and such regularization is basedon the intrinsic property of the system, in other word the symmetry.

Grasping and dexterous manipulation is a huge area in the current robotic research field. Traditionally in industrial environments, robots are customized for a certain task and work well with repetitive movements where the entire process is predetermined and deterministic. The possibility for a robot to adapt to its surroundings and manipulate objects with unknown properties is very limited and requires new models and methods. It is advantageous to explore and test new algorithms and models in a simulated environment before building physical systems. This thesis focuses on making the development, design and control of dexterous robot hands easier by exploring the benefits and challenges to use simulation in Algoryx's physics engine AGX Dynamics for experimentation and evaluation of design, motion planning, contact models, geometry, etc. The goal is to simulate complex grasping situations in AGX Dynamics and to build knowledge about contact mechanics to fully capture the dynamics of grasping in simulation. The work consists of validating the simulation library with regards to fundamental physics characteristics involved in grasping by a series of benchmark tests. The validation process aims to verify the models and numerical methods in AGX Dynamics to ensure sim2real transfer. The work has exposed friction as one of the biggest challenges in simulation. Developments of the current friction models that better represent the reality have been implemented and tested in AGX. They both show promising results for further development.

Early detection of cancer is necessary to minimize mental and physical distress. Therefore, this report investigated the possibilities of using machine learning methods to detect glioma in an early stage. This by looking at genetic data from real patients. This data consists of more than 14 million genetic features called SNP:s, and is therefore considered highly dimensional. However, the question is if these genetic data can be used for prediction of glioma?   The approach used was to first reduce the dimension by methods such as weighted cosine similarities, PCA, undercomplete autoencoder, t-SNE and sum pooling. To make prediction, k-means, naive bayes, kNN and neural networks were used.  The results of this study show that the methods mentioned above can be difficult to use in determining the risk of cancer. However, this may depend on the hyperparameters used in the models as these play a major role in performance. Some important hyperparameters were the number of nodes and layers in the undercomplete autoencoder and the neural network. Using too many nodes or layers may cause these models to overfit. Contrary, using too few nodes or layers may instead cause them to underfit. The perplexity in the t-SNE and the number of blocks in the sum pooling were also key parameters, these two hyperparameters were hard to tune well since the grid search was very costly time-wise.

The intense increase in energy consumption around the world has prompted a great deal of research on alternative and sustainable energy storage systems such as organic batteries. The fundamental understanding of the physics of organic salts and the ion insertion mechanism plays a key role in the development of electrode materials used in such sustainable batteries. The system studied in this project is of Lithium (2,5-dilithium-oxy)-terephthalate where a previous project studied this system from a different angle.  The electronic structure generation of the system is based on Density Functional Theory (DFT) along with an evolutionary algorithm to find the structures with minimum energy. The effects of varying the description of the exchange-correlation interaction were studied while introducing lithium ions to the system. This was done while also monitoring the repercussions of crystal structure optimization on the voltages, charge redistribution, and bonds of the system. The geometrical optimization of the hybrid functional resulted in the potential of the 2-electron step between Li2-p-DHT/ Li4-p-DHT of 2.6 V being closer to the experimental value recorded at 2.7 V.

Searches for the Vector-Like Quarks usually look for their signatures assuming they decay purely into Standard Model particles. In this paper, we explore one possible exotic decay of the Vector-Like Quark with electric charge +5/3, X5/3. We adopt a simplified model which allows X5/3 to decay into a doubly-charged scalar S++ which further decays into a pair of same-sign leptons. We implement, validate and recast a recent CMS search for top quark partners with charge +5/3 in the same-sign dilepton final state. Our recast analysis provides a stringent bound of about 1.54 TeV on the mass of X5/3 for a wide range of masses of S++, independent of the chirality of the relevant couplings.

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