Ricerc@Sapienza

Quantum State Discrimination of multiple states is a fundamental issue in Quantum Information and Communication. The experimental realization of optimal strategies for this task is often hindered by the necessity of complex quantum receivers or supplemental resources. In this project I am proposing and implementing a scheme for multiple-state discrimination which relies on a neural network-inspired quantum receiver featuring a dynamical information processing. The experimental realization of this strategy, through an optical implementation of the quantum receiver, represents a completely new approach in view of discriminating actual quantum states. Indeed, this protocol exploits as a supplemental degree of freedom for discrimination the detection time of photons, relieving from the need for physical auxiliary resources.
Because of the network-like structure of the receiver, the implementation of Machine Learning (ML) methods becomes feasible in our framework. Through the development of adaptive protocols relying on ML, we shall be able to achieve optimal results in a wider range of scenarios through our experimental platform.