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Quantum Information Processing

Is the addition of an assisted driving Hamiltonian always useful for adiabatic evolution?

Jie Sun, Songfeng Lu, Li Li

Published Mar 4, 2017• 1 citation
Publisher
DOI
10.1007/s11128-017-1553-7

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In our recent work we showed, by investigating the initialization of some unusual forms of assisted driving Hamiltonians, that the addition of an assisted driving Hamiltonian is not always useful in quantum adiabatic evolution. These unusual forms are those that are not the relatively fixed ones that are widely used in the literature. In this paper, we continue this study, providing further evidence for the validity of the conclusion above by researching some relatively more complex forms of assisted driving scheme, which generalize the ones studied in our previous work.

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It is known that when the initial state has no overlap with the final state in an adiabatic computation, an extra assisted driving Hamiltonian has to be used to avoid the failure of quantum adiabatic algorithm. This assisted Hamiltonian has a relatively fixed form, which can be seen in the related literature but the mechanism behind it is still not clear enough. In this paper, we discuss the usefulness of a generalized form of assisted driving Hamiltonian in adiabatic evolution which may shed light on this problem.

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It has been known that when an assisted driving item is added to the Hamiltonian of the main system, the efficiency of the resultant adiabatic evolution can be significantly improved. In some special cases, it can be seen that, only by adding an assisted driving Hamiltonian can the resultant adiaba tic evolution work. So it seems that, the additional driving Hamiltonian plays an important role in adiabatic computing. In this paper, we show five kinds of possible forms of the driving Hamiltonian, and analyses the performance of the algorithms. We find that the former fourth kinds of driving Hamiltonian may still cause the adiabatic algorithm lose their efficiency when the overlap of the initial and final states equals some special value. More importantly, we find that only the final kinds of the driving Hamiltonian can be extended. By modifying its interpolating functions, the final kind of adiabatic algorithm can suit any fidelity of the systems to keep the adiabatic evolution always be valid. Finally, the time complexity of the extended algorithm is better than the original adiabatic algorithm, which owns a constant running time complexity. Although the time complexity is shifted to the complexity of implementing the driving Hamiltonian added.

Journal of Intelligent & Fuzzy Systems•Feb 27, 2018•1 citation
In our recent work we showed, by investigating the initialization of some unusual forms of assisted driving Hamiltonians, that the addition of an assisted driving Hamiltonian is not always useful in quantum adiabatic evolution. These unusual forms are those that are not the relatively fixed ones that are widely used in the literature. In this paper, we continue this study, providing further evidence for the validity of the conclusion above by researching some relatively more complex forms of assisted driving scheme, which generalize the ones studied in our previous work.
It is known that when the initial state has no overlap with the final state in an adiabatic computation, an extra assisted driving Hamiltonian has to be used to avoid the failure of quantum adiabatic algorithm. This assisted Hamiltonian has a relatively fixed form, which can be seen in the related literature but the mechanism behind it is still not clear enough. In this paper, we discuss the usefulness of a generalized form of assisted driving Hamiltonian in adiabatic evolution which may shed light on this problem.
It has been known that when an assisted driving item is added to the Hamiltonian of the main system, the efficiency of the resultant adiabatic evolution can be significantly improved. In some special cases, it can be seen that, only by adding an assisted driving Hamiltonian can the resultant adiaba tic evolution work. So it seems that, the additional driving Hamiltonian plays an important role in adiabatic computing. In this paper, we show five kinds of possible forms of the driving Hamiltonian, and analyses the performance of the algorithms. We find that the former fourth kinds of driving Hamiltonian may still cause the adiabatic algorithm lose their efficiency when the overlap of the initial and final states equals some special value. More importantly, we find that only the final kinds of the driving Hamiltonian can be extended. By modifying its interpolating functions, the final kind of adiabatic algorithm can suit any fidelity of the systems to keep the adiabatic evolution always be valid. Finally, the time complexity of the extended algorithm is better than the original adiabatic algorithm, which owns a constant running time complexity. Although the time complexity is shifted to the complexity of implementing the driving Hamiltonian added.