2020/05/15 by Hayato Goto, Taro Kanao
Chemistry · Computer Science · Physics and Astronomy · #Adiabatic process #Adiabatic quantum computation #Algorithm #Chemistry #Computation #Computer science #Dissipation #Excited state #Ground state #Neural Networks and Reservoir Computing #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum annealing #Quantum computer #Quantum mechanics #Quantum state #Robustness (evolution) #Statistical physics #quant-ph
paper · pdf · doi:10.1038/s42005-020-00502-2
published as Commun. Phys. 3, 235 (2020) · 7 pages, 3 figures
openalex publication_date 2020/05/15 · arxiv created 2020/06/18 · arxiv updated 2020/12/22 · openalex created_date 2021/01/05 · openalex updated_date 2026/08/05
Adiabatic quantum computation (AQC), which is particularly useful for combinatorial optimization, becomes more powerful by using excited states, instead of ground states. However, the excited-state AQC is prone to errors due to dissipation. Here we propose the excited-state AQC started with the most stable state, i.e., the vacuum state. This counterintuitive approach becomes possible by using a driven quantum system, or more precisely, a network of Kerr-nonlinear parametric oscillators (KPOs). By numerical simulations, we show that some hard instances, where standard ground-state AQC with KPOs fails to find their optimal solutions, can be solved by the present approach, where nonadiabatic transitions are rather utilized. We also show that the use of the vacuum state as an initial state leads to robustness against errors due to dissipation, as expected, compared to the use of a really excited (nonvacuum) state as an initial state. Thus, the present work offers new possibilities for quantum computation and driven quantum systems.