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Title: Many-body entanglement in classical & quantum simulators
Author: Gray, Johnnie
ISNI:       0000 0004 7660 6455
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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Entanglement is not only the key resource for many quantum technologies, but essential in understanding the structure of many-body quantum matter. At the interface of these two crucial areas are simulators, controlled systems capable of mimicking physical models that might escape analytical tractability. Traditionally, these simulations have been performed classically, where recent advancements such as tensor-networks have made explicit the limitation entanglement places on scalability. Increasingly however, analog quantum simulators are expected to yield deep insight into complex systems. This thesis advances the field in across various interconnected fronts. Firstly, we introduce schemes for verifying and distributing entanglement in a quantum dot simulator, tailored to specific experimental constraints. We then confirm that quantum dot simulators would be natural candidates for simulating many-body localization (MBL) - a recently emerged phenomenon that seems to evade traditional statistical mechanics. Following on from that, we investigate MBL from an entanglement perspective, shedding new light on the nature of the transition to it from a ergodic regime. As part of that investigation we make use of the logarithmic negativity, an entanglement measure applicable to many-body mixed states. In order to tie back into quantum simulators, we then propose an experimental scheme to measure the logarithmic negativity in realistic many-body settings. This method uses choice measurements on three or more copies of a mixed state along with machine learning techniques. We also introduce a fast method for computing many-body entanglement in classical simulations that significantly increases the size of system addressable. Finally, we introduce quimb, an open-source library for interactive but efficient quantum information and many-body calculations. It contains general purpose tensor-network support alongside other novel algorithms.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available