Difference between revisions of "Rlx scalar.m"
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{{DISPLAYTITLE:rlx_scalar.m}} __NOTOC__ | {{DISPLAYTITLE:rlx_scalar.m}} __NOTOC__ | ||
Scalar relaxation superoperator using Redfield theory. | Scalar relaxation superoperator using Redfield theory. | ||
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| + | 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. | ||
==Syntax== | ==Syntax== | ||
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R=rlx_scalar(spin_system,H0,H1,tau_c_array) | R=rlx_scalar(spin_system,H0,H1,tau_c_array) | ||
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==Parameters== | ==Parameters== | ||
Revision as of 19:01, 5 June 2026
Scalar relaxation superoperator using Redfield theory.
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.
Syntax
R=rlx_scalar(spin_system,H0,H1,tau_c_array)
Parameters
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
