Smart Quantum Technologies using Photons

TL;DR

This paper reviews recent advances in quantum photonics, introduces smart methodologies combining quantum optics and machine learning, and demonstrates novel techniques for quantum metrology, light discrimination, and communication.

Contribution

It presents new theoretical schemes for quantum metrology, innovative AI-based methods for light discrimination, and neural network-based correction protocols for quantum communication.

Findings

Sub-shot-noise limited phase estimation using displaced-squeezed light

Camera-based squeezed-light detection as an efficient alternative

High-fidelity mode correction with convolutional neural networks

Abstract

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.