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We propose a machine learning method to characterize photonic states via a simple optical circuit and the data processing of photon number distributions as photonic patterns. The input states consist of two coherent states used as references and a two-mode unknown state to be studied. We successfully trained a supervised learning algorithm to predict the degree of entanglement in the two-mode state and to perform the full tomography of one photonic mode, obtaining good accuracy and an $r$-factor performance of our algorithm $r > 0.75$.
Generating entangled graph states of qubits requires high entanglement rates, with efficient detection of multiple indistinguishable photons from separate qubits. Integrating defect-based qubits into photonic devices results in an enhanced photon col
Transistors play a vital role in classical computers, and their quantum mechanical counterparts could potentially be as important in quantum computers. Where a classical transistor is operated as a switch that either blocks or allows an electric curr
We propose a method for the implementation of one-way quantum computing in superconducting circuits. Measurement-based quantum computing is a universal quantum computation paradigm in which an initial cluster-state provides the quantum resource, whil
Memristors are resistive elements retaining information of their past dynamics. They have garnered substantial interest due to their potential for representing a paradigm change in electronics, information processing and unconventional computing. Giv
Topological photonics has been introduced as a powerful platform for integrated optics, since it can deal with robust light transport, and be further extended to the quantum world. Strikingly, valley-contrasting physics in topological photonic struct