Smart Quantum Technologies using Photons
Narayan Bhusal
Abstract
Open-access reader
Narayan Bhusal
Abstract
Open-access reader
The technologies utilizing quantum states of light have been in the spotlight for the last two decades. In this regard, quantum metrology, quantum imaging, quantum-optical communication are some of the important applications that exploit fascinating quantum properties like quantum superposition, quantum correlations, and nonclassical photon statistics. However, the state-of-art technologies operating at the single-photon level are not robust enough to truly realize a reliable quantum-photonic technology. In Chapter 1, I present a historical account of photon-based technologies. Furthermore, I discuss recent encouraging developments in the field of quantum-photonic technologies, and major challenges for the implementation of reliable quantum technologies, setting up a stage for unveiling our smart methodologies to cope with them. Similarly, in Chapter 2, I review the fundamental concepts of quantum optics and machine learning. In Chapter 3, I present a theoretical work on a nonlinear quantum metrology scheme, showing a sub-shot-noise limited phase estimation using the displaced-squeezed light and on/off detection. Furthermore, I discuss a camera-based squeezed-light detection that can be a smart and time-efficient alternative to balanced-homodyne detection. In Chapter 4, I discuss our efforts to incorporate artificial intelligence in a quest to improve the efficiency of discriminating thermal light from coherent light sources. In Chapter 5, I present a communication protocol in presence of random phase distortions. We utilize convolutional neural networks to perform the spatial mode correction of single photons, resulting in a near-unity fidelity of correction. Finally, I wrap up my dissertation in Chapter 6 by summarizing the historical context, challenges facing state-of-art techniques, and the importance of our efforts to introduce artificial intelligence in quantum technologies.
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The technologies utilizing quantum states of light have been in the spotlight for the last two decades. In this regard, quantum metrology, quantum imaging, quantum-optical communication are some of the important applications that exploit fascinating quantum properties like quantum superposition, quantum correlations, and nonclassical photon statistics. However, the state-of-art technologies operating at the single-photon level are not robust enough to truly realize a reliable quantum-photonic technology. In Chapter 1, I present a historical account of photon-based technologies. Furthermore, I discuss recent encouraging developments in the field of quantum-photonic technologies, and major challenges for the implementation of reliable quantum technologies, setting up a stage for unveiling our smart methodologies to cope with them. Similarly, in Chapter 2, I review the fundamental concepts of quantum optics and machine learning. In Chapter 3, I present a theoretical work on a nonlinear quantum metrology scheme, showing a sub-shot-noise limited phase estimation using the displaced-squeezed light and on/off detection. Furthermore, I discuss a camera-based squeezed-light detection that can be a smart and time-efficient alternative to balanced-homodyne detection. In Chapter 4, I discuss our efforts to incorporate artificial intelligence in a quest to improve the efficiency of discriminating thermal light from coherent light sources. In Chapter 5, I present a communication protocol in presence of random phase distortions. We utilize convolutional neural networks to perform the spatial mode correction of single photons, resulting in a near-unity fidelity of correction. Finally, I wrap up my dissertation in Chapter 6 by summarizing the historical context, challenges facing state-of-art techniques, and the importance of our efforts to introduce artificial intelligence in quantum technologies.
Key concepts: Quantum imaging, Quantum metrology, Quantum technology, Quantum information science, Quantum sensor, Photon, Physics, Computer science