The utilization of photonic neural networks in the domain of quantum sensing

Quantum sensing is a quantum-based process that concerns the development of principles, methods, and devices using superposition of quantum states. Quantum sensors find application in a variety of contexts, serving as a base sensitive element in high-precision synchronization systems, functioning as a key part of gravitometers, accelerometers, and electric and magnetic field sensors. They are also being utilized in quantum metrology and lidar technology. The data generated by quantum sensors can be employed for machine learning purposes. A discussed approach involves the direct utilization of quantum sensor data as input parameters in machine learning models based on photonic neural networks. It has been determined that there is an actual problem of integration for results from quantum sensing and photonic neural networks features. A photonic neural network is discussed as a hardware-implemented algorithm, that belongs to the category of artificial intelligence algorithms in general. The hardware-implemented algorithm reveals a complex interconnection between physical processes at the photonic-based neurons. This paper provides an analysis of quantum sensing in general. It discusses the main type of photonic neural networks and their applications for quantum sensing. The paper considers an integration scheme for photonic neural networks for key points control of quantum sensing processes. Furthermore, the paper presents a proposed functional scheme for quantum sensing control based on the regulator with photonic neural networks.