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|Quantum optics of exciton polariton networks
|Science::Physics::Optics and light
|Nanyang Technological University
|Xu, H. (2022). Quantum optics of exciton polariton networks. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163440
|The strong coupling between light and matter in a semiconductor microcavity will induce the formation of a quasi-particle known as an exciton-polariton. Such quasi-particles are found to support an efficient information processing platform due to their capability to capture the characters from both photons and excitons, for example: fast response time, strong nonlinear interaction, longer dephasing time, etc. This thesis concerns different proposals for information processing by utilizing the exciton-polariton system, while some schemes are general and applicable to other physical systems as well. First, I demonstrate that a disordered exciton polariton neural network can work as a reservoir computing platform to process binary information with the ability to correct an input error automatically. The Toffoli logic gate and a composite logic circuit are taken as examples for demonstration. In the next part, I take the quantum nature of exciton-polaritons into account. I utilize the Fock states of one single polariton mode to construct the reservoir network and perform a pattern classification task. By comparing the performance of such a quantum polaritonic reservoir network and the classical polariton reservoir network, I illustrate that there is a superpolynomial quantum enhancement in terms of the physical system size that requires to achieve the same classification e fficiency. Then I consider a multimode bosonic network. I demonstrate that the Kerr nonlinearity in a bosonic quantum neural network can correct environment noise and measurement errors. In the last chapter, I explain that the orbital angular momentum can be non-resonantly transferred onto a polariton condensate. A unidirectional signal propagation is achieved in a coupled polariton ring network by using the orbital angular momentum preserving pump. The control of signal propagation direction is essential for developing more complex multilayered neural networks.
|School of Physical and Mathematical Sciences
|This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
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Updated on Feb 20, 2024
Updated on Feb 20, 2024
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