Quantum Annealing for Neural Network optimization problems: a new approach via Tensor Network simulations

Understand

Quantum Annealing (QA) is one of the most promising frameworks for quantum optimization.

• Here, we focus on the problem of minimizing complex classical cost functions associated with prototypical discrete neural networks, specifically the paradigmatic Hopfield model and binary perceptron.
• We show that the adiabatic time evolution of QA can be efficiently represented as a suitable Tensor Network.
• This representation allows for simple classical simulations, well-beyond small sizes amenable to exact diagonalization techniques.