Variational quantum algorithm for Gaussian discrete solitons and their boson sampling
This work addresses the lack of general methods for quantum solitons, which could impact quantum information processing by enabling boson sampling and entangled nonlinear wave processors, though it appears incremental as it applies existing variational techniques to a new domain.
The paper tackled the problem of modeling quantum solitons in nonlinear regimes by developing a neural network-based variational ansatz for an array of waveguides, resulting in the discovery of different soliton solutions and the revelation that soliton bound states emit correlated particle pairs.
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 learning 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.