Detecting gravity-mediated entanglement can provide evidence that the gravitational field obeys quantum mechanics. We report the result of a simulation of the phenomenon using a photonic platform. The simulation tests the idea of probing the quantum nature of a variable by using it to mediate entanglement and yields theoretical and experimental insights, clarifying the operational tools needed for future gravitational experiments. We employ three methods to test the presence of entanglement: the Bell test, entanglement witness, and quantum state tomography. We also simulate the alternative scenario predicted by gravitational collapse models or due to imperfections in the experimental setup and use quantum state tomography to certify the absence of entanglement. The simulation reinforces two main lessons: (1) which path information must be first encoded and subsequently coherently erased from the gravitational field and (2) performing a Bell test leads to stronger conclusions, certifying the existence of gravity-mediated nonlocality.
We present an experiment where a reconfigurable photonic processor fabricated in glass by femtosecond laser micromachining is used for the generation of four-photons GHZ entangled states, with high efficiency and fidelity. The chip is used in synergy with a bright and quasi-deterministic source of single photons based on semiconductor quantum dot. The very efficient interfacing of these two platforms is ensured by the excellent connectivity between glass photonic circuits and standard optical fibers. In addition, in order to benchmark the quality of the generated states, this processor is used to implement a quantum secret sharing protocol on chip.
Over the last decades, the combination of quantum computing and machine learning has opened many possibilities, for example enhancing machine learning algorithms through quantum platforms. However, one of the current challenges consists in combining the linear unitary evolution of closed quantum systems with the nonlinearity required by neural networks, which are currently the most widely used and versatile machine learning algorithms. This issue can now be addressed by a novel photonic tool, the quantum memristor,1 which displays a nonlinear behavior, while preserving quantum coherence, through a weak controlled interaction of its input state with the environment. Here, we show how its operation can be extended to deal with higher frequency modulations of the input and, possibly, with a simplification in its scheme. This method can prove useful for the future implementation of memristor-based quantum neural networks.
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