Exploring adiabatic quantum trajectories via optimal control
Adiabatic quantum computation employs a slow change of a time-dependent control function (or functions) to interpolate between an initial and final Hamiltonian, which helps to keep the system in the instantaneous ground state. When the evolution time is finite, the degree of adiabaticity (quantified...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2014-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/16/6/065013 |
| _version_ | 1797751437191544832 |
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| author | Constantin Brif Matthew D Grace Mohan Sarovar Kevin C Young |
| author_facet | Constantin Brif Matthew D Grace Mohan Sarovar Kevin C Young |
| author_sort | Constantin Brif |
| collection | DOAJ |
| description | Adiabatic quantum computation employs a slow change of a time-dependent control function (or functions) to interpolate between an initial and final Hamiltonian, which helps to keep the system in the instantaneous ground state. When the evolution time is finite, the degree of adiabaticity (quantified in this work as the average ground-state population during evolution) depends on the particulars of a dynamic trajectory associated with a given set of control functions. We use quantum optimal control theory with a composite objective functional to numerically search for controls that achieve the target final state with a high fidelity while simultaneously maximizing the degree of adiabaticity. Exploring the properties of optimal adiabatic trajectories in model systems elucidates the dynamic mechanisms that suppress unwanted excitations from the ground state. Specifically, we discover that the use of multiple control functions makes it possible to access a rich set of dynamic trajectories, some of which attain a significantly improved performance (in terms of both fidelity and adiabaticity) through the increase of the energy gap during most of the evolution time. |
| first_indexed | 2024-03-12T16:48:31Z |
| format | Article |
| id | doaj.art-55d9dd2dd0334cdcbaa643d098d9b526 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:48:31Z |
| publishDate | 2014-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-55d9dd2dd0334cdcbaa643d098d9b5262023-08-08T11:26:51ZengIOP PublishingNew Journal of Physics1367-26302014-01-0116606501310.1088/1367-2630/16/6/065013Exploring adiabatic quantum trajectories via optimal controlConstantin Brif0Matthew D Grace1Mohan Sarovar2Kevin C Young3Department of Scalable & Secure Systems Research , Sandia National Laboratories, Livermore, CA 94550, USADepartment of Scalable & Secure Systems Research , Sandia National Laboratories, Livermore, CA 94550, USADepartment of Scalable & Secure Systems Research , Sandia National Laboratories, Livermore, CA 94550, USADepartment of Scalable & Secure Systems Research , Sandia National Laboratories, Livermore, CA 94550, USAAdiabatic quantum computation employs a slow change of a time-dependent control function (or functions) to interpolate between an initial and final Hamiltonian, which helps to keep the system in the instantaneous ground state. When the evolution time is finite, the degree of adiabaticity (quantified in this work as the average ground-state population during evolution) depends on the particulars of a dynamic trajectory associated with a given set of control functions. We use quantum optimal control theory with a composite objective functional to numerically search for controls that achieve the target final state with a high fidelity while simultaneously maximizing the degree of adiabaticity. Exploring the properties of optimal adiabatic trajectories in model systems elucidates the dynamic mechanisms that suppress unwanted excitations from the ground state. Specifically, we discover that the use of multiple control functions makes it possible to access a rich set of dynamic trajectories, some of which attain a significantly improved performance (in terms of both fidelity and adiabaticity) through the increase of the energy gap during most of the evolution time.https://doi.org/10.1088/1367-2630/16/6/065013adiabatic quantum computationquantum optical control theoryadiabatic approximationmultiobjective optimization |
| spellingShingle | Constantin Brif Matthew D Grace Mohan Sarovar Kevin C Young Exploring adiabatic quantum trajectories via optimal control New Journal of Physics adiabatic quantum computation quantum optical control theory adiabatic approximation multiobjective optimization |
| title | Exploring adiabatic quantum trajectories via optimal control |
| title_full | Exploring adiabatic quantum trajectories via optimal control |
| title_fullStr | Exploring adiabatic quantum trajectories via optimal control |
| title_full_unstemmed | Exploring adiabatic quantum trajectories via optimal control |
| title_short | Exploring adiabatic quantum trajectories via optimal control |
| title_sort | exploring adiabatic quantum trajectories via optimal control |
| topic | adiabatic quantum computation quantum optical control theory adiabatic approximation multiobjective optimization |
| url | https://doi.org/10.1088/1367-2630/16/6/065013 |
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