Quantum sensors are sensing devices that exploit quantum phenomena in such a way that makes them perform substantially better than their classical counterparts. This course uses an information-theoretic approach to identify and explain the basic design principles and potential applications of quantum sensors. A primary goal of the course is to describe those aspects of quantum phenomena that can be harnessed in order to design and develop novel sensing devices. To this end, the course summarizes recent theoretical and experimental results that showcase the feasibility of quantum sensors. In addition, the course compares the theoretical performance of quantum sensors with their classical counterparts in the areas of radar, lidar, photo-detection, magnetometry, and gravimetry.

Quantum computing, one of the most recent joint ventures between physics and the theory of computation, can be defined as the scientific field whose purpose is to develop hardware and algorithms based on quantum mechanical phenomena. In addition to further advance the mathematical and physical foundations of quantum computing, scientists and engineers who work in this field focus on developing cutting-edge quantum algorithms in areas like artificial intelligence, cryptanalysis, machine learning, database search, chemical simulations, and image processing. The course summarizes recent theoretical and experimental results that showcase the feasibility of large-scale quantum computation. In addition, the course describes the potential applications of quantum computing to signal analysis, sensor fusion, and computer vision.

Quantum computation is a multidisciplinary field focused on the development of computers and algorithms based on the quantum mechanical properties of Nature. Quantum computation is now an established research field with solid theoretical and experimental results. Additionally, a growing number of scientists and engineers are devoting their efforts to developing cross-fertilizing initiatives (e.g., advanced quantum algorithms) in fields such as machine learning, cryptanalysis, image processing, analysis of biological data, chemical simulations, and blockchain technology. High-tech businesses across various sectors are increasingly experimenting with quantum computing technological solutions: quantum computing and the broader field of quantum technologies (embracing computing, communications, cryptography, and sensing) are becoming an attractive emerging business ecosystem. This course summarizes the foundations of quantum computing as well as recent theoretical and experimental results that showcase the feasibility of large-scale quantum computation. We review several quantum algorithms and describe potential applications of quantum computing to cybersecurity, molecular dynamics, analysis of biological data, and computer vision.

Quantum cryptography is a scientific and engineering field devoted to harnessing physical objects whose behavior is governed by the rules of quantum mechanics to generate and distribute keys in order to convert ordinary plain text messages into meaningless (codified) messages and vice versa. In this paradigm, safe key distribution relies on the physical properties of quantum-mechanical systems rather than on mathematical conjectures. This course presents a succinct review of key generation & distribution and its role in symmetric and assymetric cryptography protocols, followed by a concise yet complete introduction to the BB84 and E91 quantum key distribution (QKD) protocols (this section comprises the theoretical foundations and several computer simulations of both QKD protocols). We finish this course by showing some real-world applications of QKD protocols.