Abstract
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.
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