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Nanyang Technological University 637371, SingaporeMachine learning software applications are nowadays ubiquitous in many fields of science and society for their outstanding capability of solving computationally vast problems like the recognition of patterns and regularities in big datasets. One of the main goals of research is the realization of a physical neural network able to perform data processing in a much faster and energyefficient way than the state-of-the-art technology. Here we show that lattices of exciton-polariton condensates accomplish neuromorphic computing using fast optical nonlinearities and with lower error rate than any previous hardware implementation. We demonstrate that our neural network significantly increases the recognition efficiency compared to the linear classification algorithms on one of the most widely used benchmarks, the MNIST problem, showing a concrete advantage from the integration of optical systems in reservoir computing architectures.

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Quantum vortices, the quantized version of classical vortices, play a prominent role in superfluid and superconductor phase transitions. However, their exploration at a particle level in open quantum systems has gained considerable attention only recently. Here we study vortex pair interactions in a resonant polariton fluid created in a solid-state microcavity. By tracking the vortices on picosecond time scales, we reveal the role of nonlinearity, as well as of density and phase gradients, in driving their rotational dynamics. Such effects are also responsible for the split of composite spin–vortex molecules into elementary half-vortices, when seeding opposite vorticity between the two spinorial components. Remarkably, we also observe that vortices placed in close proximity experience a pull–push scenario leading to unusual scattering-like events that can be described by a tunable effective potential. Understanding vortex interactions can be useful in quantum hydrodynamics and in the development of vortex-based lattices, gyroscopes, and logic devices.

In this work, we experimentally demonstrate for the first time the spontaneous generation of two-dimensional exciton-polariton X-waves. X-waves belong to the family of localized packets that can sustain their shape without spreading, even in the linear regime. This allows the wavepacket to maintain its shape and size for very low densities and very long times compared to soliton waves, which always necessitate a nonlinearity to compensate the diffusion. Here, we exploit the polariton nonlinearity and uniquely structured dispersion, comprising both positive- and negative-mass curvatures, to trigger an asymmetric four-wave mixing in momentum space. This ultimately enables the self-formation of a spatial X-wave front. Using ultrafast imaging experiments, we observe the early reshaping of the initial Gaussian packet into the X-pulse and its propagation, even for vanishingly small densities. This allows us to outline the crucial effects and parameters that drive the phenomena and to tune the degree of superluminal propagation, which we found to be in close agreement with numerical simulations.

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The estimation of the transmission matrix of a disordered medium is a challenging problem in disordered photonics. Usually, its reconstruction relies on a complex inversion that aims at connecting a fully controlled input to the deterministic interference of the light field scrambled by the device. At the moment, iterative phase retrieval protocols provide the fastest reconstructing frameworks, converging in a few tens of iterations. Exploiting the knowledge of speckle correlations, we construct a new phase retrieval algorithm that reduces the computational cost to a single iteration. Besides being faster, our method is practical because it accepts fewer measurements than state-of-the-art protocols. Thanks to reducing computation time by one order of magnitude, our result can be a step forward toward real-time optical imaging that exploits disordered devices.