Theseshttp://hdl.handle.net/10012/62023-01-28T03:25:12Z2023-01-28T03:25:12ZA Stabilizer Formalism for Infinitely Many QubitsKong, Xiangzhouhttp://hdl.handle.net/10012/191402023-01-27T21:18:05Z2023-01-27T00:00:00ZA Stabilizer Formalism for Infinitely Many Qubits
Kong, Xiangzhou
The study of infinite dimensional quantum systems has been an active area of discussion in quantum information theory, particularly in settings where certain properties are shown to be not attainable by any finite dimensional system (such as nonlocal correlations).
Similarly, the notion of stabilizer states has yielded interesting developments in areas like error correction, efficient simulation of quantum systems and its relation to graph states.
However, the commonly used model of tensor products of finite dimensional Hilbert spaces is not sufficiently general to capture infinite dimensional stabilizer states.
A more general framework quantum mechanical systems using C*-algebras has been instrumental in studying systems with an infinite number of discrete systems in quantum statistical mechanics and quantum field theory.
We propose a framework in the C*-algebra model (specifically, the CAR algebra) for the stabilizer formalism that extends to infinitely many qubits.
Importantly, the stabilizer states on the CAR algebra form a class of states that can attain unbounded entanglement and yet has a simple characterization through the group structure of its stabilizer.
In this framework, we develop a theory for the states, operations and measurements needed to study open questions in quantum information.
2023-01-27T00:00:00ZData-Driven Methods for System Identification and Lyapunov StabilityQuartz, Thaninhttp://hdl.handle.net/10012/191392023-01-27T19:27:32Z2023-01-27T00:00:00ZData-Driven Methods for System Identification and Lyapunov Stability
Quartz, Thanin
This thesis focuses on data-driven methods applied to system identification and stability analysis of dynamical systems. In the first major contribution of the theorem we propose a learning framework to simultaneously stabilize an unknown nonlinear system with a neural controller and learn a neural Lyapunov function to certify a region of attraction (ROA) for the closed-loop system. The algorithmic structure consists of two neural networks and a satisfiability modulo theories (SMT) solver. The first neural network is responsible for learning the unknown dynamics. The second neural network aims to identify a valid Lyapunov function and a provably stabilizing nonlinear controller. The SMT solver then verifies that the candidate Lyapunov function indeed satisfies the Lyapunov conditions. We provide theoretical guarantees of the proposed learning framework in terms of the
closed-loop stability for the unknown nonlinear system. We illustrate the effectiveness of the approach with a set of numerical experiments. We then examine another popular data driven method for system identification involving the Koopman operator. Methods based on the Koopman operator aim to approximate advancements of the state under the flow operator by a high-dimensional linear operator. This is accomplished by the extended mode decomposition (eDMD) algorithm which takes non-linear measurements of the state. Under the suitable conditions we have a result on the weak convergence of the eigenvalues and eigenfunctions of the eDMD operator that can serve as components of Lyapunov functions. Finally, we review methods for finding the region of attraction of an asymptotically stable fixed point and compare this method to the two methods mentioned above.
2023-01-27T00:00:00ZOptimization of Tin Selenide Thermoelectric Propertiesgolabek, andrewhttp://hdl.handle.net/10012/191382023-01-27T17:17:39Z2023-01-27T00:00:00ZOptimization of Tin Selenide Thermoelectric Properties
golabek, andrew
The high performance thermoelectric material tin selenide is of notable interest to the field of thermoelectric materials; since breaking the record for being the most efficient thermoelectric material due to the ultralow thermal conductivity. These materials have many potential and current applications such as radioisotope generators, waste heat recovery in vehicles, power generation, sensors, and refrigeration.
The optimization of the thermoelectric properties of p-type double doped tin selenide, and n-type double doped tin selenide have been investigated through the course of this thesis project. The experimental synthesis parameters have been thoroughly investigated to determine a consistent, optimized procedure for the production of polycrystalline tin selenide thermoelectric materials. The key components of the optimized synthesis procedure include, cooling method from melt synthesis (water quenching), preparation before hot pressing (ball milling 600 rpm, 6 hours), reduction (773 K, 8 hours, 5 % H2/Ar), and hot pressing parameters (773 K, 48 MPa, 10 min).
Using consistent synthesis methods, the optimization of the composition for the double doped p-type, and n-type samples was determined by using a triangulated 3-dimensional surface plot for each of the systems. The p-type system NaxCuySn1-x-ySe (0≤x≤0.035), (0≤y≤0.016) had two compositions of interest with notably high average and peak thermoelectric figure of merit (ZT) Na0.034Cu0.016Sn0.961Se (0.45, 0.96), Na0.0113Cu0.0077Sn0.978Se (0.45, 0.77) between 298 K and 773 K, low minimum thermal conductivities (K) of (0.36 W m-1 K-1 ), (0.45 W m-1 K-1 ), and peak electrical conductivity (σ ) (132 S cm-1 at 420 K), (239 S cm-1 at 323 K) respectively. The n-type system Sn1-xBixSe1-yBry (0≤x≤0.06), (0≤y≤0.06) had a composition of interest with notably high peak and average thermoelectric figure of merit (zT) (0.57 at 773 K, 0.21 from 298 K to 773 K), low minimum thermal conductivity (0.48 W m-1 K-1 ), and power factor (3.66 μW cm-1 K-2) for SnSe0.94Br0.06 .
Finally using the fully optimized procedure and compositions three high performance p-type, and two n-type polycrystalline tin selenide samples were prepared with the compositions; Na0.033Cu0.015Sn0.96Se, Na0.033Ag0.015Sn0.96Se, Na0.034Au0.015Sn0.96Se, SnSe0.94Br0.06, SnSe0.94Cl0.06.
All five samples were prepared using identical sources of tin, and were prepared in parallel to ensure comparison between the different dopants can be consistently determined.
The highest performance p-type sample was Na0.033Ag0.016Sn0.963Se, with a maximum zT of 2.12 at 910 K, an average zT of 0.87 from 298 K to 910 K, minimum thermal conductivity of 0.24 W m-1 K-1 at 910 K and peak power factor of 6.01 μW cm-1 K-2 at 468-516 K.
The highest performance n-type sample was SnSe0.9Br0.1, with a maximum zT of 0.77 at 910 K, and an average zT of 0.34 from 298 K to 910 K, minimum thermal conductivity of 0.49 W m-1 K-1 at 811 K and peak power factor of 4.89 μW cm-1 K-2 at 910 K.
2023-01-27T00:00:00ZA Study of the Capabilities of Message-Oriented Middleware SystemsAl-Manasrah, Waelhttp://hdl.handle.net/10012/191372023-01-27T16:02:50Z2023-01-27T00:00:00ZA Study of the Capabilities of Message-Oriented Middleware Systems
Al-Manasrah, Wael
We present a comprehensive characterization study of open-source Message-Oriented Middleware (MOM) systems. We devised a rigorous methodology to select and study 10 popular and diverse MOM systems. For each system, we examine 42 features with a total of 134 different options. We found that MOM systems have evolved to provide a framework for modern cloud applications through high flexibility and configurability and by offering core building blocks for complex applications including transaction support, active messaging, resource management, flow control, and native support for multi-tenancy. A key result of our study, is that we believe there is an opportunity for the community to consolidate its efforts on fewer open-source projects.
We have also created an annotated data set that makes it easy to verify our findings, which can also be used to help practitioners and developers determine and understand the features of different systems. For a wider impact, our data set is publicly available at [https://docs.google.com/spreadsheets/d/1HrZ7ub19FuuBzA5z4aA6RfR5vnkdnm0bg3hxfADspEA/edit?usp=sharing].
2023-01-27T00:00:00Z