Quantum correlations in bound-soliton pairs and trains in fiber lasers (4 pages)

Ray‐Kuang Lee, Yinchieh Lai, Boris A. Malomed

Quantum correlations in pairs and arrays (trains) of bound solitons modeled by the complex Ginzburg-Landau equation (CGLE) are calculated numerically, on the basis of linearized equations for quantum fluctuations. We find strong correlations between the bound solitons, even though the system is dissipative. Some degree of the correlation between the photon-number fluctuations of stable bound soliton pairs and trains is attained and saturates after passing a certain distance. The saturation of the photon-number correlations is explained by the action of nonconservative terms in the CGLE. Photon-number-correlated bound soliton trains offer possibilities to produce multipartite entangled sources for quantum communication and computation.

P. D. Drummond

Sara Schneider, Edouard Brainis, Marc Haelterman

Claudio Conti

We use a neural network variational ansatz to compute Gaussian quantum discrete solitons in an array of waveguides described by the quantum discrete nonlinear Schroedinger equation. By training the quantum machine learning model in the phase space, we find different quantum soliton solutions varying the number of particles and interaction strength. The use of Gaussian states enables measuring the degree of entanglement and the boson sampling patterns. We compute the probability of generating different particle pairs when varying the soliton features and unveil that bound states of discrete solitons emit correlated pairs of photons. These results may have a role in boson sampling experiments with nonlinear systems and in developing quantum processors to generate entangled many-photon nonlinear states.

Claudio Conti

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-slearning 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.

Rupamanjari Ghosh