use timelist instead

qutip · Apr 11, 2024 · db4d8f8 · db4d8f8
1 parent 61c9e69
commit db4d8f8
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Showing 5 changed files with 25 additions and 27 deletions.
diff --git a/doc/guide/guide-control.rst b/doc/guide/guide-control.rst
@@ -254,9 +254,6 @@ It requires some extra arguments to prepare the optimization.
   # boundaries for the parameters
   p_bounds = q_bounds = [(-1, 1), (-1, 1)]
 
-  # time interval for the evolution
-  t_interval = qoc.TimeInterval(evo_time=1.)
-
 
 Eventually, the optimization for a desired `fid_err_targ` can be started by calling the ``optimize`` function.
 
@@ -268,7 +265,7 @@ Eventually, the optimization for a desired `fid_err_targ` can be started by call
         "p": {"guess": p_guess, "bounds": p_bounds},
         "q": {"guess": q_guess, "bounds": q_bounds}
     },
-    time_interval=t_interval,
+    tlist=np.linspace(0, 1, 100),
     algorithm_kwargs={
         "alg": "GOAT",
         "fid_err_targ": 0.1,

diff --git a/src/qutip_qoc/pulse_optim.py b/src/qutip_qoc/pulse_optim.py
@@ -4,14 +4,15 @@
 import qutip_qtrl.pulseoptim as cpo
 
 from qutip_qoc._optimizer import _global_local_optimization
+from qutip_qoc.time import TimeInterval
 
 __all__ = ["optimize_pulses"]
 
 
 def optimize_pulses(
     objectives,
     control_parameters,
-    time_interval,
+    tlist,
     algorithm_kwargs=None,
     optimizer_kwargs=None,
     minimizer_kwargs=None,
@@ -29,7 +30,7 @@ def optimize_pulses(
     control_parameters : dict
         Dictionary of options for the control pulse optimization.
         The keys of this dict must be a unique string identifier for each control Hamiltonian / function.
-        For the GOAT and JOPT algorithms, the dict may optionally also contain the key "__time__" to treat the pulse duration as optimization parameter.
+        For the GOAT and JOPT algorithms, the dict may optionally also contain the key "__time__".
         For each control function it must specify:
 
             control_id : dict
@@ -43,7 +44,7 @@ def optimize_pulses(
 
             __time__ : dict, optional
                 Only supported by GOAT and JOPT.
-                Dictionary of options for the time interval optimization.
+                If given the pulse duration is treated as optimization parameter.
                 It must specify both:
 
                     - guess: ndarray, shape (n,)
@@ -56,9 +57,8 @@ def optimize_pulses(
 
         GRAPE and CRAB bounds are only one pair of ``(min, max)`` limiting the amplitude of all tslots equally.
 
-    time_interval : :class:`qutip_qoc.TimeInterval`
-        Pulse duration time interval.
-        GRAPE and CRAB require n_tslots attribute for discretization.
+    tlist: List.
+        Time over which system evolves.
 
     algorithm_kwargs : dict, optional
         Dictionary of options for the optimization algorithm.
@@ -124,6 +124,9 @@ def optimize_pulses(
     if integrator_kwargs is None:
         integrator_kwargs = {}
 
+    # create time interval
+    time_interval = TimeInterval(tslots=tlist)
+
     time_options = control_parameters.pop("__time__", {})
     alg = algorithm_kwargs.get("alg", "GRAPE")  # works with most input types
 

diff --git a/tests/test_analytical_pulses.py b/tests/test_analytical_pulses.py
@@ -11,14 +11,13 @@
 
 from qutip_qoc.pulse_optim import optimize_pulses
 from qutip_qoc.objective import Objective
-from qutip_qoc.time import TimeInterval
 
 Case = collections.namedtuple(
     "Case",
     [
         "objectives",
         "control_parameters",
-        "time_interval",
+        "tlist",
         "algorithm_kwargs",
         "optimizer_kwargs",
     ],
@@ -65,7 +64,7 @@ def grad_sin(t, p, idx):
         "p": {"guess": p_guess, "bounds": p_bounds},
         "q": {"guess": q_guess, "bounds": q_bounds},
     },
-    time_interval=TimeInterval(evo_time=1.0),
+    tlist=np.linspace(0, 1, 100),
     algorithm_kwargs={
         "alg": "GOAT",
         "fid_err_targ": 0.01,
@@ -84,7 +83,7 @@ def grad_sin(t, p, idx):
         "p": {"guess": p_guess, "bounds": p_bounds},
         "q": {"guess": q_guess, "bounds": q_bounds},
     },
-    time_interval=TimeInterval(evo_time=1.0),
+    tlist=np.linspace(0, 1, 100),
     algorithm_kwargs={
         "alg": "GOAT",
         "fid_err_targ": 0.01,
@@ -104,7 +103,7 @@ def grad_sin(t, p, idx):
             "bounds": (0, 10),
         },
     },
-    time_interval=TimeInterval(evo_time=1.0),
+    tlist=np.linspace(0, 1, 100),
     algorithm_kwargs={
         "alg": "GOAT",
         "fid_err_targ": 0.01,
@@ -131,7 +130,7 @@ def grad_sin(t, p, idx):
         "p": {"guess": p_guess, "bounds": p_bounds},
         "q": {"guess": q_guess, "bounds": q_bounds},
     },
-    time_interval=TimeInterval(evo_time=1.0),
+    tlist=np.linspace(0, 1, 100),
     algorithm_kwargs={
         "alg": "GOAT",
         "fid_err_targ": 0.1,  # relaxed objective
@@ -209,7 +208,7 @@ def test_optimize_pulses(tst):
     result = optimize_pulses(
         tst.objectives,
         tst.control_parameters,
-        tst.time_interval,
+        tst.tlist,
         tst.algorithm_kwargs,
         tst.optimizer_kwargs,
     )

diff --git a/tests/test_fidelity.py b/tests/test_fidelity.py
@@ -14,14 +14,13 @@
 
 from qutip_qoc.pulse_optim import optimize_pulses
 from qutip_qoc.objective import Objective
-from qutip_qoc.time import TimeInterval
 
 Case = collections.namedtuple(
     "Case",
     [
         "objectives",
         "control_parameters",
-        "time_interval",
+        "tlist",
         "algorithm_kwargs",
         "optimizer_kwargs",
     ],
@@ -67,7 +66,7 @@ def grad_sin(t, p, idx):
         "p": {"guess": p_guess, "bounds": p_bounds},
         "q": {"guess": q_guess, "bounds": q_bounds},
     },
-    time_interval=TimeInterval(evo_time=5.0),
+    tlist=np.linspace(0, 1, 100),
     algorithm_kwargs={"alg": "GOAT", "fid_type": "PSU"},
     optimizer_kwargs={
         "seed": 0,
@@ -184,7 +183,7 @@ def test_optimize_pulses(tst):
     result = optimize_pulses(
         tst.objectives,
         tst.control_parameters,
-        tst.time_interval,
+        tst.tlist,
         tst.algorithm_kwargs,
         tst.optimizer_kwargs,
     )

diff --git a/tests/test_result.py b/tests/test_result.py
@@ -19,7 +19,7 @@
     [
         "objectives",
         "control_parameters",
-        "time_interval",
+        "tlist",
         "algorithm_kwargs",
         "optimizer_kwargs",
     ],
@@ -68,7 +68,7 @@ def grad_sin(t, p, idx):
         "q": {"guess": q_guess, "bounds": q_bounds},
         "r": {"guess": r_guess, "bounds": r_bounds},
     },
-    time_interval=TimeInterval(evo_time=10),
+    tlist=np.linspace(0, 10, 100),
     algorithm_kwargs={
         "alg": "GOAT",
         "fid_err_targ": 0.01,
@@ -120,7 +120,7 @@ def sin_z_jax(t, r, **kwargs):
 # ------------------- discrete CRAB / GRAPE  control ------------------------
 
 n_tslots, evo_time = 100, 10
-disc_interval = TimeInterval(n_tslots=n_tslots, evo_time=evo_time)
+disc_interval = np.linspace(0, evo_time, n_tslots)
 
 p_disc = q_disc = r_disc = np.zeros(n_tslots)
 p_bound = q_bound = r_bound = (-1, 1)
@@ -137,7 +137,7 @@ def sin_z_jax(t, r, **kwargs):
         "q": {"guess": q_disc, "bounds": q_bound},
         "r": {"guess": r_disc, "bounds": r_bound},
     },
-    time_interval=disc_interval,
+    tlist=disc_interval,
     algorithm_kwargs={"alg": "GRAPE", "fid_err_targ": 0.01},
 )
 
@@ -148,7 +148,7 @@ def sin_z_jax(t, r, **kwargs):
         "q": {"guess": q_disc, "bounds": q_bound},
         "r": {"guess": r_disc, "bounds": r_bound},
     },
-    time_interval=disc_interval,
+    tlist=disc_interval,
     algorithm_kwargs={"alg": "CRAB", "fid_err_targ": 0.01, "fix_frequency": False},
 )
 
@@ -171,7 +171,7 @@ def test_optimize_pulses(tst):
     result = optimize_pulses(
         tst.objectives,
         tst.control_parameters,
-        tst.time_interval,
+        tst.tlist,
         tst.algorithm_kwargs,
         tst.optimizer_kwargs,
     )