Search Results for author:
Found 3 papers, 2 papers with code
Building separable approximations for quantum states via neural networks
Finding the closest separable state to a given target state is a notoriously difficult task, even more difficult than deciding whether a state is entangled or separable.
Fast semidefinite programming with feedforward neural networks
Given the optimization constraints as an input, a neural network outputs values for the optimization parameters such that the constraints are satisfied, both for the primal and the dual formulations of the task.
A neural network oracle for quantum nonlocality problems in networks
As such, the neural network acts as an oracle, demonstrating that a behavior is classical if it can be learned.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
