Incorporating physical implementation concerns into closed loop quantum control experiments
J. Chem. Phys. 113, 10841 (2000); doi:10.1063/1.1326905
Issue Date: 22 December 2000
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In quantum control experiments, it is desirable to build features into the field that address physical concerns such as simplicity, robustness, dynamical coherence, power expenditure, etc. With a judicious choice for the cost functional, it is possible to incorporate such secondary features into the field, often without altering the experimental procedure or apparatus. Through simulated closed-loop population transfer experiments, we demonstrate the benefit of carefully designed cost functionals. As specific examples, we address two common physical concerns: removing extraneous structure from the control pulse and finding robust fields. Removing unnecessary field components is critical if information about the mechanism is to be interpreted from the structure of the optimal pulse. Robust fields produce a stable outcome despite noise in the field and, perhaps, environmental inhomogeneities in the quantum system as is typical of condensed phase experiments. ©2000 American Institute of Physics.
| History: | Received 20 July 2000; accepted 28 September 2000 |
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http://link.aip.org/link/?JCPSA6/113/10841/1 |
KEYWORDS and PACS
- 03.65.Bz
Quantum mechanics, field theories, and special relativity Quantum mechanics Foundations, theory of measurement, miscellaneous theories (including AharonovBohm effect, Bell inequalities, Berry's phase) - YEAR: 2000
RELATED DATABASES
PUBLICATION DATA
0021-9606 (print)
1089-7690 (online)
REFERENCES (13)
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- R. Judson and H. Rabitz, Phys. Rev. Lett. 68, 1500 (1992).
- D. Tannor and S. Rice, J. Chem. Phys. 83, 5013 (1985).
- M. Shapiro and P. Brumer, J. Chem. Phys. 84, 4103 (1986).
- A. Pierce, M. Dahleh, and H. Rabitz, Phys. Rev. A 37, 4950 (1988).
- H. Rabitz, R. de Vivie-Riedle, M. Motzkus, and K. Kompa,
Science 288, 824 (2000) . - R. Kosloff, S. Rice, P. Gaspard, S. Tersigni, and D. Tannor, J. Chem. Phys. 139, 201 (1989).
- T. Weinacht, J. White, and P. Bucksbaum,
J. Phys. Chem. A 103, 10166 (1999) . - A. Assion, T. Baumbert, T. Bergt, B. Brixner, B. Kiefer, M. Seyfried, M. Strehle, and G. Gerber,
Science 282, 919 (1998) . - C. Bardeen, V. Yakovlev, K. Wilson, S. Carpenter, P. Weber, and W. Warren,
Chem. Phys. Lett. 280, 151 (1997) . - K. Bergmann, H. Theuer, and B. Shore, Rev. Mod. Phys. 70, 1003 (1998).
- D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning (AddisonWesley, Reading, MA, 1989).
-
01 = 1.711,
02 = 3.026,
03 = 4.134,
12 = 1.314,
13 = 2.423,
23 = 1.108, µ01 = 1.653, µ02 = 0.254, µ03 = 0.000, µ12 = 0.814, µ13 = 0.025, µ23 = 0.059. Frequencies are in rad fs1 and transition dipole moments in 1.0×1030 C m. - A. Matsumoto and K. Iwamoto,
J. Quant. Spectrosc. Radiat. Transf. 55, 457 (1996) .








