Recovery of Quantum Correlations using Machine Learning
arXiv:2410.02818 · doi:10.1103/PhysRevApplied.23.034083
Abstract
Quantum sources with strong correlations are essential but delicate resources in quantum information science and engineering. Decoherence and loss are the primary factors that degrade nonclassical quantum correlations, with scattering playing a role in both processes. In this work, we present a method that leverages Long Short-Term Memory (LSTM), a machine learning technique known for its effectiveness in time-series prediction, to mitigate the detrimental impact of scattering in quantum systems. Our setup involves generating two-mode squeezed light via four-wave mixing in warm rubidium vapor, with one mode subjected to a scatterer to disrupt quantum correlations. Mutual information and intensity-difference squeezing between the two modes are used as metrics for quantum correlations. We demonstrate a 74.7~\% recovery of mutual information and 87.7~\% recovery of two-mode squeezing, despite significant photon loss that would otherwise eliminate quantum correlations. This approach marks a significant step toward recovering quantum correlations from random disruptions without the need for hardware modifications, paving the way for practical applications of quantum protocols.
10 pages, 7 figures, 1 table
References in corpus (37)
- Quantum information with continuous variables
- Gaussian Quantum Information
- Dynamical Decoupling of Open Quantum Systems
- Enhancing the sensitivity of the LIGO gravitational wave detector by using squeezed states of light
- Continuous-variable optical quantum state tomography
- Area laws in quantum systems: mutual information and correlations
- Efficient quantum state tomography
- Biological measurement beyond the quantum limit
- Symplectic invariants, entropic measures and correlations of Gaussian states
- Ultrasensitive measurement of MEMS cantilever displacement sensitivity below the shot noise limit
- Phase sensing beyond the standard quantum limit with a truncated SU(1,1) interferometer
- Optimizing Quantum Error Correction Codes with Reinforcement Learning
- Mutual information as an order parameter for quantum synchronization
- Dynamical decoupling for superconducting qubits: a performance survey
- Quantum-Enhanced Plasmonic Sensing
- Quantum-Enhanced continuous-wave stimulated Raman spectroscopy
- Quantum Mutual Information Capacity for High Dimensional Entangled States
- Observation of Strong Radiation Pressure Forces from Squeezed Light on a Mechanical Oscillator
- Machine learning aided carrier recovery in continuous-variable quantum key distribution
- Machine learning assisted quantum state estimation
- Efficient quantum state tomography with convolutional neural networks
- Quantum-Enhanced Stimulated Brillouin Scattering Spectroscopy and Imaging
- Neural-network quantum state tomography
- Squeezed Light Induced Two-photon Absorption Fluorescence of Fluorescein Biomarkers
- Squeezing-enhanced Raman spectroscopy
- Quantum mutual information of an entangled state propagating through a fast-light medium
- Entanglement, Quantum Entropy and Mutual Information
- Quantum Advantage with Seeded Squeezed Light for Absorption Measurement
- Quantum State Tomography using Quantum Machine Learning
- Advanced Quantum Noise
- Experimental characterization of Gaussian quantum discord generated by four-wave mixing
- Revival of Quantum Interference by Modulating the Biphotons
- Improved measurement of two-mode quantum correlations using a phase-sensitive amplifier
- Experimental study of decoherence of the two-mode squeezed vacuum state via second harmonic generation
- Dynamical Decoupling in Optical Fibers: Preserving Polarization Qubits from Birefringent Dephasing
- Enhancing quantum state tomography via resource-efficient attention-based neural networks
- Mitigating scattering in a quantum system using only an integrating sphere