Difference between revisions of "Rlx scalar.m"
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| − | {{DISPLAYTITLE:rlx_scalar.m}} | + | {{DISPLAYTITLE:rlx_scalar.m}} |
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Scalar relaxation superoperator using Redfield theory. | Scalar relaxation superoperator using Redfield theory. | ||
==Syntax== | ==Syntax== | ||
| − | R=rlx_scalar(spin_system,H0,H1, | + | R=rlx_scalar(spin_system,H0,H1,tau_c) |
| + | |||
| + | ==Description== | ||
| + | Computes Redfield superoperator in situations when the system has a static background Hamiltonian and a perturbation with a scalar stochastic function in front of it. Scalar hyperfine relaxation is a common example. This function is called by Spinach relaxation theory module, but may also be invoked directly. | ||
==Arguments== | ==Arguments== | ||
| − | H0 - background Hamiltonian | + | H0 - background Hamiltonian |
| − | + | ||
H1 - the stochastically modulated interaction operator | H1 - the stochastically modulated interaction operator | ||
multiplied by its root mean square modulation depth | multiplied by its root mean square modulation depth | ||
| − | + | ||
| − | + | tau_c - the correlation time of the stochastic modulation | |
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==Outputs== | ==Outputs== | ||
| − | R - relaxation superoperator as a negative definite matrix | + | R - relaxation superoperator as a negative definite matrix |
==Notes== | ==Notes== | ||
| − | |||
If H1(t) has a non-zero average value, it must be subtracted out and put into H0. | If H1(t) has a non-zero average value, it must be subtracted out and put into H0. | ||
==See also== | ==See also== | ||
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[[relaxation.m]], [[expmint.m]], [[lindbladian.m]], [[rlx_t1_t2.m]] | [[relaxation.m]], [[expmint.m]], [[lindbladian.m]], [[rlx_t1_t2.m]] | ||
''Version 2.1, authors: [[Ilya Kuprov]]'' | ''Version 2.1, authors: [[Ilya Kuprov]]'' | ||
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Revision as of 15:49, 5 April 2026
Scalar relaxation superoperator using Redfield theory.
Syntax
R=rlx_scalar(spin_system,H0,H1,tau_c)
Description
Computes Redfield superoperator in situations when the system has a static background Hamiltonian and a perturbation with a scalar stochastic function in front of it. Scalar hyperfine relaxation is a common example. This function is called by Spinach relaxation theory module, but may also be invoked directly.
Arguments
H0 - background Hamiltonian
H1 - the stochastically modulated interaction operator
multiplied by its root mean square modulation depth
tau_c - the correlation time of the stochastic modulation
Outputs
R - relaxation superoperator as a negative definite matrix
Notes
If H1(t) has a non-zero average value, it must be subtracted out and put into H0.
See also
relaxation.m, expmint.m, lindbladian.m, rlx_t1_t2.m
Version 2.1, authors: Ilya Kuprov
