Avoiding barren plateaus via transferability of smooth solutions in a Hamiltonian variational ansatz

Parametrized quantum circuits inspired by adiabatic quantum computation often suffer from vanishing gradients, hindering the trainability of hybrid quantum-classical algorithms. Here, the authors put forward a strategy to overcome this by reusing parameters and iterating from small to large systems.