Adiabatic quantum optimization with the wrong Hamiltonian

摘要: Analog models of quantum information processing, such as adiabatic computation and analog simulation, require the ability to subject a system precisely specified Hamiltonians. Unfortunately, hardware used implement these Hamiltonians will be imperfect limited in its precision. Even small perturbations imprecisions can have profound effects on nature ground state. Here we consider an implementation optimization show that, for widely applicable random control noise model, stabilizer encodings are able reduce effective magnitude thus improve likelihood successful or simulation. This reduction builds upon two design principles: summation equivalent logical operators increase energy scale encoded problem, inclusion penalty term comprising sum code elements. We illustrate our findings with Ising ladder that classical repetition coding drastically increases probability state perturbed model is decodable unperturbed while using only realistic two-body interaction. Finally, note encoding special case encodings, this principle allows us generalize results many types albeit at expense many-body interactions.