Neural networks for detecting multimode Wigner-negativity

Abstract

The characterization of quantum features in large Hilbert spaces is a crucial requirement for testing quantum protocols. In the continuous variables encoding, quantum homodyne tomography requires an amount of measurements that increases exponentially with the number of involved modes, which practically makes the protocol intractable even with few modes. Here we introduce a new technique, based on a machine learning protocol with artificial Neural Networks, that allows to directly detect negativity of the Wigner function for multimode quantum states. We test the procedure on a whole class of numerically simulated multimode quantum states for which the Wigner function is known analytically. We demonstrate that the method is fast, accurate and more robust than conventional methods when limited amounts of data are available. Moreover the method is applied to an experimental multimode quantum state, for which an additional test of resilience to losses is carried out.

References in corpus (42)

• Machine learning and the physical sciences
• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Measurement-based quantum computation
• Many-body quantum state tomography with neural networks
• Generation of a superposition of odd photon number states for quantum information networks
• Ultra-Large-Scale Continuous-Variable Cluster States Multiplexed in the Time Domain
• Wavelength-Multiplexed Quantum Networks with Ultrafast Frequency Combs
• Experimental realization of multipartite entanglement of 60 modes of a quantum optical frequency comb
• Time-Domain Multiplexed 2-Dimensional Cluster State: Universal Quantum Computing Platform
• Positive Wigner functions render classical simulation of quantum computation efficient
• Deterministic generation of a two-dimensional cluster state
• Non-gaussian statistics from individual pulses of squeezed light
• Quantum Computing with Continuous-Variable Clusters
• Reinforcement Learning in Different Phases of Quantum Control
• Iterative maximum-likelihood reconstruction in quantum homodyne tomography
• Active learning machine learns to create new quantum experiments
• Generation of Optical Coherent State Superpositions by Number-Resolved Photon Subtraction from Squeezed Vacuum
• Resource theory of quantum non-Gaussianity and Wigner negativity
• Reinforcement Learning with Neural Networks for Quantum Feedback
• Teleportation of Nonclassical Wave Packets of light
• Machine Learning for Precise Quantum Measurement
• Convex resource theory of non-Gaussianity
• Experimental neural network enhanced quantum tomography
• Quantum-optical state engineering up to the two-photon level
• Non-Gaussian quantum states of a multimode light field
• Integrating Neural Networks with a Quantum Simulator for State Reconstruction
• Machine learning for discriminating quantum measurement trajectories and improving readout
• Optical continuous-variable qubit
• Entanglement and Wigner function negativity of multimode non-Gaussian states
• Experimental Phase Estimation Enhanced By Machine Learning
• Using Recurrent Neural Networks to Optimize Dynamical Decoupling for Quantum Memory
• Reconstruction of a Photonic Qubit State with Reinforcement Learning
• Experimental quantum homodyne tomography via machine learning
• Calibration of quantum sensors by neural networks
• Generation of highly pure Schrödinger's cat states and real-time quadrature measurements via optical filtering
• Entangling macroscopic light states by delocalized photon addition
• Tomography of a Mode-Tunable Coherent Single-Photon Subtractor
• Pattern recognition techniques for Boson Sampling validation
• Statistical signatures of multimode single-photon added and subtracted states of light
• Temporally multiplexed superposition states of continuous variables
• Neural-network approach for identifying nonclassicality from click-counting data
• Mode-Dependent Loss Model for Multimode Photon-Subtracted States

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• Efficient detection of nonclassicality using moments of the Wigner function
• Efficient Learning of Continuous-Variable Quantum States
• Two-mode light states before and after delocalized single-photon addition
• Efficient Characterization of Quantum Evolutions via a Recommender System
• Multi-photon-addition amplified coherent state
• Estimation of the geometric measure of entanglement with Wehrl Moments through Artificial Neural Networks
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• Observation of synchronization between two quantum van der Pol oscillators in trapped ions
• Artificial intelligence for representing and characterizing quantum systems