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Register a new userMachine learning-based classification of vector vortex beams
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Structured light is attracting significant attention for its diverse applications in both classical and quantum optics. The so-called vector vortex beams display peculiar properties in both contexts due to the non-trivial correlations between optical polarization and orbital angular momentum. Here we demonstrate a new, flexible experimental approach to the classification of vortex vector beams. We first describe a platform for generating arbitrary complex vector vortex beams inspired to photonic quantum walks. We then exploit recent machine learning methods -- namely convolutional neural networks and principal component analysis -- to recognize and classify specific polarization patterns. Our study demonstrates the significant advantages resulting from the use of machine learning-based protocols for the construction and characterization of high-dimensional resources for quantum protocols.
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Light beams having a vectorial field structure - or polarization - that varies over the transverse profile and a central optical singularity are called vector-vortex (VV) beams and may exhibit specific properties, such as focusing into light needles or rotation invariance, with applications ranging from microscopy and light trapping to communication and metrology. Individual photons in such beams exhibit a form of single-particle quantum entanglement between different degrees of freedom. On the other hand, the quantum states of two photons can be also entangled with each other. Here we combine these two concepts and demonstrate the generation of quantum entanglement between two photons that are both in VV states - a new form of quantum entangled entanglement. This result may lead to quantum-enhanced applications of VV beams as well as to quantum-information protocols fully exploiting the vectorial features of light.
Harnessing the spontaneous emission of incoherent quantum emitters is one of the hallmarks of nano-optics. Yet, an enduring challenge remains-making them emit vector beams, which are complex forms of light associated with fruitful developments in fluorescence imaging, optical trapping and high-speed telecommunications. Vector beams are characterized by spatially varying polarization states whose construction requires coherence properties that are typically possessed by lasers-but not by photons produced by spontaneous emission. Here, we show a route to weave the spontaneous emission of an ensemble of colloidal quantum dots into vector beams. To this end, we use holographic nanostructures that impart the necessary spatial coherence, polarization and topological properties to the light originating from the emitters. We focus our demonstration on vector vortex beams, which are chiral vector beams carrying non-zero orbital angular momentum, and argue that our approach can be extended to other forms of vectorial light.
Vector vortex beams have played a fundamental role in the better understanding of coherence and polarization. They are described by spatially inhomogeneous polarization states, which present a rich optical mode structure that has attracted much attention for applications in optical communications, imaging, spectroscopy and metrology. However, this complex mode structure can be quite detrimental when propagation effects such as turbulence and birefringence perturb the beam. Optical phase conjugation has been proposed as a method to recover an optical beam from perturbations. Here we demonstrate full phase conjugation of vector vortex beams using three-wave mixing. Our scheme exploits a fast non-linear process that can be conveniently controlled via the pump beam. Our results pave the way for sophisticated, practical applications of vector beams.
Angular momentum plays a central role in a multitude of phenomena in quantum mechanics, recurring in every length scale from the microscopic interactions of light and matter to the macroscopic behavior of superfluids. Vortex beams, carrying intrinsic orbital angular momentum (OAM), are now regularly generated with elementary particles such as photons and electrons, and harnessed for numerous applications including microscopy and communication. Untapped possibilities remain hidden in vortices of non-elementary particles, as their composite structure can lead to coupling of OAM with internal degrees of freedom. However, thus far, the creation of a vortex beam of a non-elementary particle has never been demonstrated experimentally. We present the first vortex beams of atoms and molecules, formed by diffracting supersonic beams of helium atoms and dimers, respectively, off binary masks made from transmission gratings. By achieving large particle coherence lengths and nanometric grating features, we observe a series of vortex rings corresponding to different OAM states in the accumulated images of particles impacting a detector. This method is general and can be applied to most atomic and molecular gases. Our results may open new frontiers in atomic physics, utilizing the additional degree of freedom of OAM to probe collisions and alter fundamental interactions.
We consider the problem of probe-based quantum thermometry, and show that machine classification can provide reliable estimates over a broad range of scenarios. Our approach is based on the $k$-nearest-neighbor algorithm. Temperature is divided into bins, and the machine trains a predictor based on data from observations at different times (obtained e.g. from computer simulations or other experiments). This yields a predictor, which can then be used to estimate the temperature from new observations. The algorithm is flexible, and works with both populations and coherences. It also allows to incorporate other uncertainties, such as lack of knowledge about the system-probe interaction strength. The proposal is illustrated in the paradigmatic Jaynes-Cummings and Rabi models. In both cases, the mean-squared error is found to decrease monotonically with the number of data points used, showing that the algorithm is asymptotically convergent. This, we argue, is related to the well behaved data structures stemming from thermal phenomena, which indicates that classification may become an experimentally relevant tool for thermometry in the quantum regime.
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