使用光子的智能量子技术
Smart Quantum Technologies using Photons
摘要：
利用光的量子态的技术在过去的二十年中一直处于聚光灯下。在这方面，量子精密测量，量子成像，量子光通信是一些重要的应用，利用迷人的量子特性，如量子叠加，量子关联，和非经典光子统计。然而，在单光子水平上操作的最先进的技术还不足以真正实现可靠的量子光子技术。在第一章中，我介绍了基于光子的技术的历史。此外，我还讨论了量子光子技术领域最近令人鼓舞的发展，以及实施可靠量子技术所面临的主要挑战，为揭示我们应对这些挑战的智能方法奠定了基础。同样，在第2章中，我将回顾量子光学和机器学习的基本概念。在第三章中，我提出了一个非线性量子精密测量方案的理论工作，展示了一个亚散粒噪声限制的相位估计使用位移压缩光和开/关检测。此外，我讨论了一个基于相机的挤压光检测，可以是一个智能和时间效率的平衡零差检测的替代方案。在第4章中，我讨论了我们将人工智能融入到提高热光与相干光源区分效率的努力。在第五章中，我提出了一个存在随机相位失真的通信协议。我们利用卷积神经网络来执行单光子的空间模式校正，从而获得接近统一的校正保真度。最后，我在第6章总结了我的论文，总结了历史背景、最先进技术面临的挑战以及我们在量子技术中引入人工智能的重要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
