Machine learning-based classification of vector vortex beams
arXiv:2005.07949 · doi:10.1103/PhysRevLett.124.160401
Abstract
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.
References in corpus (44)
- Optical spin-to-orbital angular momentum conversion in inhomogeneous anisotropic media
- Electromagnetic Angular Momentum
- Electromagnetic Force and Momentum
- Quantum Entanglement of High Angular Momenta
- Experimental Quantum State Tomography of Optical Fields and Ultrafast Statistical Sampling
- Unknown Quantum States and Operations, a Bayesian View
- High-dimensional quantum cryptography with twisted light
- Twisted Photons: New Quantum Perspectives in High Dimensions
- High-Dimensional Intra-City Quantum Cryptography with Structured Photons
- Communication with spatially modulated Light through turbulent Air across Vienna
- High-dimensional quantum communication: benefits, progress, and future challenges
- Free-space quantum key distribution by rotation-invariant twisted photons
- Polarization pattern of vector vortex beams generated by -plates with different topological charges
- Twisted Light Transmission over 143 kilometers
- Multi-photon entanglement in high dimensions
- Photonic polarization gears for ultra-sensitive angular measurements
- Detection of Zak phases and topological invariants in a chiral quantum walk of twisted photons
- Reconstructing quantum states with generative models
- Active learning machine learns to create new quantum experiments
- Hypergeometric-Gaussian Modes
- Creation and characterization of vector vortex modes for classical and quantum communication
- Nanointerferometric Amplitude and Phase Reconstruction of Tightly Focused Vector Beams
- Experimental Quantum Hamiltonian Learning
- Indefinite Causal Order in a Quantum Switch
- Orbital angular momentum states enabling fiber-based high-dimensional quantum communication
- Direct Measurement of a 27-Dimensional Orbital-Angular-Momentum State Vector
- Witnessing eigenstates for quantum simulation of Hamiltonian spectra
- Quantum walks and wavepacket dynamics on a lattice with twisted photons
- Photonic quantum walk in a single beam with twisted light
- Dynamical moments reveal a topological quantum transition in a photonic quantum walk
- Turbulence correction with artificial neural networks
- Implementing Quantum Walks Using Orbital Angular Momentum of Classical Light
- Experimental engineering of arbitrary qudit states with discrete-time quantum walks
- Limitations to the determination of a Laguerre-Gauss spectrum via projective, phase-flattening measurement
- Air-core fiber distribution of hybrid vector vortex-polarization entangled states
- Mapping Twisted Light into and out of a Photonic Chip
- Experimental Phase Estimation Enhanced By Machine Learning
- Experimental statistical signature of many-body quantum interference
- Light propagation in a birefringent plates with topological charge
- Calibration of quantum sensors by neural networks
- Implementation of one-dimensional quantum walks on spin-orbital angular momentum space of photons
- Quantum state engineering using one-dimensional discrete-time quantum walks
- Pattern recognition techniques for Boson Sampling validation
- Birth and evolution of an optical vortex
Cited by in corpus (27)
- Nonseparable states of light: From quantum to classical
- Roadmap on structured waves
- Vectorial light-matter interaction -- exploring spatially structured complex light fields
- Deep learning enhanced Rydberg multifrequency microwave recognition
- Machine learning recognition of light orbital-angular-momentum superpositions
- Reinforcement learning approach to non-equilibrium quantum thermodynamics
- Robust calibration of multiparameter sensors via machine learning at the single-photon level
- Experimental property-reconstruction in a photonic quantum extreme learning machine
- Orbital angular momentum based intra- and inter- particle entangled states generated via a quantum dot source
- Spatial Mode Correction of Single Photons using Machine Learning
- Dynamical learning of a photonics quantum-state engineering process
- Efficient sorting of orbital-angular-momentum states with large topological charges and their unknown superpositions via machine learning
- Enhanced detection techniques of Orbital Angular Momentum states in the classical and quantum regimes
- Regression of high dimensional angular momentum states of light
- Single-Pixel Pattern Recognition with Coherent Nonlinear Optics
- Neural-network-based parameter estimation for quantum detection
- Transfer of angular spectrum in parametric down-conversion with structured light
- Quantum Model Learning Agent: characterisation of quantum systems through machine learning
- Efficient learning of mixed-state tomography for photonic quantum walk
- Quantum reservoir computing for photonic entanglement witnessing
- Improved Tomographic Estimates by Specialised Neural Networks
- Fingerprint Recognition of Partial Discharge Signals in Deep Learning Enhanced Rydberg Atomic Sensors
- Fast remote spectral discrimination through ghost spectrometry
- Entanglement transfer, accumulation and retrieval via quantum-walk-based qubit-qudit dynamics
- Deep-learning-based recognition of multi-singularity structured light
- Memory-enhanced quantum extreme learning machines for characterizing non-Markovian dynamics
- Quantum process matrices as images: new tools to design novel denoising methods