Single-shot polarimetry of vector beams by supervised learning
States of light encoding multiple polarizations – vector beams – offer unique capabilities in metrology and communication. However, their practical application is limited by the lack of methods for measuring many polarizations in a scalable and compact way. Here we demonstrate polarimetry of vector beams in a single shot without any polarization optics. We map the beam polarization content into a spatial intensity distribution through multiple light scattering and exploit supervised learning for single-shot measurements of multiple polarizations. The method also allows us to classify beams with an unknown number of polarization modes, a functionality missing in conventional techniques. Our findings enable a fast and compact polarimeter for polarization-structured light, a universal tool that may radically impact optical devices for sensing, imaging, and computing.
Modern machine learning applications require huge artificial networks demanding in computational power and memory. Light-based platforms promise ultra-fast and energy-efficient hardware, which may help in realizing next-generation data processing devices. However, current photonic networks are limited by the number of input-output nodes that can be processed in a single shot. This restricted network capacity prevents their application to relevant large-scale problems such as natural language processing. Here, we realize a photonic processor with a capacity exceeding 1.5×1010 optical nodes, more than one order of magnitude larger than any previous implementation, which enables photonic large-scale text encoding and classification. By exploiting the full three-dimensional structure of the optical field propagating in free space, we overcome the interpolation threshold and reach the over-parametrized region of machine learning, a condition that allows high-performance natural language processing with a minimal fraction of training points. Our results provide a novel solution to scale-up light-driven computing and open the route to photonic language processing.
Quantum field theory beyond Minkowski is still largely unexplored, and the links with quantum information are surprising.
We know that the Minkowski vacuum appears as a thermal state in a uniformly accelerated frame (Rindler/Unruh) or in the proximity of a black hole (Hawking).
Beyond Unruh and Hawking radiation, how do quantum states appear to a noninertial observer? We derive a general technique to understand the way acceleration alters Minkowski-Fock states. It turns out that one can use and engineer quantum correlations to measure acceleration. But a lot of work is still needed with advanced mathematical approaches, although these new general results may be useful for many applications.
An explicit Wigner formulation of Minkowski particle states for non-inertial observers is unknown. Here, we derive a general prescription to compute the characteristic function for Minkowski-Fock states in accelerated frames. For the special case of single-particle and two-particle states, this method enables to derive mean values of particle numbers and correlation function in the momentum space, and the way they are affected by the acceleration of the observer. We show an indistinguishability between Minkowski single-particle and two-particle states in terms of Rindler particle distribution that can be regarded as a way for the observer to detect any acceleration of the frame. We find that for two-particle states the observer is also able to detect acceleration by measuring the correlation between Rindler particles with different momenta.
In the context of quantum information, highly nonlinear regimes, such as those supporting solitons, are marginally investigated. We miss general methods for quantum solitons, although they can act as entanglement generators or as self-organized quantum processors. We develop a computational approach that uses a neural network as a variational ansatz for quantum solitons in an array of waveguides. By training the resulting phase-space quantum machine learning model, we find different soliton solutions varying the number of particles and interaction strength. We consider Gaussian states that enable measuring the degree of entanglement and sampling the probability distribution of many-particle events. We also determine the probability of generating particle pairs and unveil that soliton bound states emit correlated pairs. These results may have a role in boson sampling with nonlinear systems and in quantum processors for entangled nonlinear waves
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