In this presentation, we show the efficacy of neural networks in reducing classical resources required for quantum state estimation. The developed methods achieve near-unity fidelities in reconstructed density matrices, and outperform Stokes reconstruction in a wide variety of scenarios.
Multipartite entanglement is a key resource for various quantum information tasks. Here, we present multiple schemes for multimode entanglement and squeezing via nonlinear optical processes. We define a new "coupled three-mode squeezed vacuum" state. Non-intuitive behaviors arise in intensity squeezing between two of the three output modes due to the coupling. We also show that this state can be genuinely tripartite entangled, and extend the work to a four-mode output system.
We present the experimental conversion of a spatially-Gaussian optical mode into a self-healing, approximate Bessel-Gauss mode by a non-collinear, spatially-multimode four-wave mixing process in warm atomic vapor. In addition to the mode conversion, a second, spatially-separate conjugate beam is created in a non-Gaussian mode that mimics that of the resulting converted probe beam. Additionally, we show that these resulting beams exhibit the ability to partially self-heal their mode profiles after encountering an obstacle in their paths. This multi-spatial-mode nonlinear gain platform may thus be used as a new method for all-optically generating pairs of self-healing beams.
The generation of light containing large degrees of orbital angular momentum (OAM) has recently been demon- strated in both the classical and quantum regimes. Since there is no fundamental limit to how many quanta of OAM a single photon can carry, optical states with an arbitrarily high difference in this quantum number may, in principle, be entangled. This opens the door to investigations into high-dimensional entanglement shared between states in superpositions of nonzero OAM. Additionally, making use of non-zero OAM states can allow for a dramatic increase in the amount of information carried by a single photon, thus increasing the information capacity of a communication channel. In practice, however, it is difficult to differentiate between states with high OAM numbers with high precision. Here we investigate the ability of deep neural networks to differentiate between states that contain large values of OAM. We show that such networks may be used to differentiate be- tween nearby OAM states that contain realistic amounts of noise, with OAM values of up to 100. Additionally, we examine how the classification accuracy scales with the signal-to-noise ratio of images that are used to train the network, as well as those being tested. Finally, we demonstrate the simultaneous classification of < 100 OAM states with greater than 70 % accuracy. We intend to verify our system with experimentally-produced classi- cal OAM states, as well as investigate possibilities that would allow this technique to work in the few-photon quantum regime.
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