There have been numerous advances in the fields of engineering, medicine, environment and so forth that involve measurements as a critical part of research and applications. Especially, when it comes to reaching efficient and precision measurements - what comes in handy are optical sensors that provide results beyond just size or location. In recent years, research have primarily relied on providing solutions by using deep learning and novel training algorithms for efficiently determining and eliciting results in the fields that optical metrology is applied. However, there is also analysed optical quantum metrology that is bringing a new finding to light. This paper will analyse the impact of deep learning in quantum metrology and will provide future suggestions on how to improve the results in terms of classical means by using the technologies of quantum photonics metrology to provide actual examples and applications of both technologies in real life.
There have been numerous advances in the fields of engineering, medicine, environment and so forth that involve measurements as a critical part of research and applications. Especially, when it comes to reaching efficient and precision measurements - what comes in handy are optical sensors that provide results beyond just size or location. In recent years, research have primarily relied on providing solutions by using deep learning and novel training algorithms for efficiently determining and eliciting results in the fields that optical metrology is applied. However, there is also analysed optical quantum metrology that is bringing a new finding to light. This paper will analyse the impact of deep learning in quantum metrology and will provide future suggestions on how to improve the results in terms of classical means by using the technologies of quantum photonics metrology to provide actual examples and applications of both technologies in real life.
Fiber laser sources from visible to near-infrared wavelengths have driven innovative developments, impacting various domains such as telecommunications, biology, and medicine. The development of such fiber laser relies on the accurate knowledge of both optical properties as chromatic dispersion and material properties. On the other hand, quantum metrology is one of the promising field enabled by quantum technologies. It allows to get precise results compare to classical methods when measuring physical properties. A very common approach is to inject non classical states of light in interferometers to increase accuracy as well as sensitivity. Recently, this scheme has been used for detecting gravitational waves for example [1]. During the conference, we show how we take advantage of these capabilities to gather optical fiber photonic engineering with quantum optics. More specifically, we aim at presenting two quantum-based method for (i) high-accuracy (10-5) and dispersion-free measurement of refractive index difference and (ii) chromatic dispersion measurement based on the concept of quantum white-light interferometry that allows absolute measurement of chromatic dispersion with ~2.5 times improved accuracies compared to state-of-the-art realizations at telecom wavelengths. [1] B. P. Abbott et. al., ”Observation of Gravitational Waves from a Binary Black Hole Merger”, Phys. Rev. Lett., 116, 061102 (2016)
In recent years, deep learning algorithms have shown promising results for different image analyzing tasks, particularly in remote sensing image processing. Inspired by the success of remote sensing sensors in geo-located imagery, many studies have been carried out on remote sensing sensors for image processing, which brings a new approach into intelligent remote sensing and photogrammetric computer vision. At the same time, algorithms for quantum processors have been shown to efficiently solve some issues that are intractable on conventional, classical processors. This study summarizes the novel techniques of deep learning and quantum deep learning and its research progress and real-world applications in remote sensing image processing, introduces the current main challenges in processing and its development of geo-located datasets, focuses on the analysis and elaboration of the research status of quantum deep learning in sensing and imaging, and on this basis, summarizes the intelligent remote sensing applications and their application effects in scene understanding.
Quantum states of light, such as squeezed states or entangled states, can be used to make measurements (metrology), produce images, and sense objects with a precision that far exceeds what is possible classically, and also exceeds what was once thought to be possible quantum mechanically. The primary idea is to exploit quantum effects to beat the shot-noise limit in metrology and the Rayleigh diffraction limit in imaging and sensing. Quantum optical metrology has received a boost in recent years with an influx of ideas from the rapidly evolving field of optical quantum information processing. Both areas of research exploit the creation and manipulation of quantum-entangled states of light. We will review some of the recent theoretical and experimental advances in this exciting new field of quantum optical metrology, focusing on examples that exploit a particular two-mode entangled photon state – the High-N00N state.
With the rapid advancement of science and technology, phase measurement plays a crucial role in various fields such as optics, communication, and radar. Traditional phase measurement methods, relying on classical light sources, produce results following the central limit theorem, limiting measurement precision to the standard quantum limit. However, recent in-depth studies of quantum theory and quantum enhanced experimental techniques are breaking this barrier. The use of specially designed non-classical light sources for phase measurement not only provides the possibility of breaking through the limits of measurement precision but also meets the growing demand for high-precision measurements.This paper started from a statistical perspective, introduced key indicators for evaluating measurement precision, including the Cramér-Rao Boundary (CRB) and Fisher Information, and extends to experimental approaches beyond the standard quantum limit. Subsequently, by introducing quantum light sources such as squeezed states, entangled states, and methods like harmonic amplification to achieve amplifier the phase and optical detection, the paper provides a comprehensive review of recent research achievements in super-resolution phase measurement techniques. Finally, we select several technological categories with a promising engineering outlook, analyze the technical challenges limiting their maturity, and provide a prospective outlook.
The convergence of quantum photonics and artificial intelligence (AI) is redefining the landscape of optical metrology, sensing, and communication, enabling unprecedented precision, adaptability, and data processing capabilities. This paper explores the synergistic integration of AI-driven algorithms with quantum photonic systems, highlighting transformative advancements such as machine learning-enhanced quantum state estimation, intelligent control of photonic circuits, and adaptive quantum error correction. Emphasis is placed on how AI facilitates real-time decision-making and noise mitigation in complex quantum environments, thereby enhancing the sensitivity and resolution of optical sensors and metrological instruments. Additionally, the study investigates AI-assisted quantum communication protocols that optimize entanglement distribution, secure key generation, and photonic resource management. By bridging theoretical insights with emerging experimental frameworks, this work presents a comprehensive perspective on the mutual reinforcement between quantum photonics and AI, outlining their collective potential to drive the next generation of ultra-precise, intelligent optical technologies.
The field of quantum computing is also no longer in its infancy but is now being used to transform the healthcare sector with unprecedented computing power; this power can be used to improve machine learning as well as increase the security measures, which will spawn future innovations. The chapter provides an overview of the new environment of quantum-enabled healthcare, especially concentrating on collision points between machine learning and quantum computing to speed up diagnostics, and advancements in tailored healthcare. The quantum-enhanced algorithms would unlock complex biomedical trends that are beyond the capability of classical systems. Simultaneously, quantum cryptography is a security framework capable of providing strong security solutions that are needed to protect sensitive patient data in a healthcare environment that is becoming more digitized. With further development of quantum hardware and algorithms, they are likely to be incorporated in clinical practices, which will bring additional accuracy, efficiency, and ethical practices to medical research and practice. The chapter introduces the recent innovations and radical applications and discusses the issues that will have to be overcome to utilize the possibility of quantum-computing in healthcare to the fullest. Fused with artificial intelligence and secure health informatics, the study work predicts that there will come a time when smart medicine will be more intelligent as well as safer and predictive. The final part looks out into the future and shows how quantum advances could eventually transform the current healthcare paradigms, thereby sparking a new scientific discovery and human health renaissance.