(TechSpot) Researchers at the Skolkovo Institute of Science and Technology (Skoltech) in Moscow have developed a new method to accelerate the computation of quantum interactions. It’s straightforward enough: instead of storing/computing quantum information on classical computers via classical algorithms, they complete the whole process on a quantum neural network.
Unpredictability is an issue inherent to the modeling of quantum-scale interactions.
Skoltech merged a few developing theoretical methods of quantum computing to (sort of) substitute the randomness of sampling methods with the special properties of quantum computers. Their method uses an algorithm called the variational quantum eigensolver to create a quantum description of the starting positions of all the objects/forces interacting with each other. Some additional information is added to the positions from a classical neural network to estimate the type of interaction. Then, a quantum neural network (this bit is still theoretical) computes the interaction and searches for patterns in the output.
Skoltech Researchers Develop Method to Accelerate Computation of Quantum Interactions
