Name
Abstract | ||
|---|---|---|
An adiabatic quantum algorithm is essentially given by three elements: An initial Hamiltonian with known ground state, a problem Hamiltonian whose ground state corresponds to the solution of the given problem, and an evolution schedule such that the adiabatic condition is satisfied. A correct choice of these elements is crucial for an efficient adiabatic quantum computation. In this paper, we propose a hybrid quantum-classical algorithm that, by solving optimization problems with an adiabatic machine, determines a problem Hamiltonian assuming restrictions on the class of available problem Hamiltonians. The scheme is based on repeated calls to the quantum machine into a classical iterative structure. In particular, we suggest a technique to estimate the encoding of a given optimization problem into a problem Hamiltonian and we prove the convergence of the algorithm. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s42484-020-00030-w | QUANTUM MACHINE INTELLIGENCE |
Keywords | DocType | Volume |
Adiabatic quantum computing, Hybrid quantum-classical algorithms, Tabu search | Journal | 3 |
Issue | ISSN | Citations |
1 | 2524-4906 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Davide Pastorello | 1 | 0 | 0.34 |
| Enrico Blanzieri | 2 | 581 | 52.98 |
| Valter Cavecchia | 3 | 0 | 0.34 |