Merge pull request #210 from harmoniqs/fix/docs-examples

Docs fixes (part 3): fixing inconsistent minimum time problem solver runs via a) bounding state variables and b) verifying fidelity is sufficiently close to unity

Author: gennadiryan

docs/literate/examples/two_qubit_gates.jl

@@ -101,7 +101,7 @@ prob = UnitarySmoothPulseProblem(
     R_da=0.01,
     R_dda=0.01,
     Δt_max=Δt_max,
-    piccolo_options=PiccoloOptions()
+    piccolo_options=PiccoloOptions(bound_state=true),
 )
 fid_init = unitary_rollout_fidelity(prob.trajectory, sys)
 println(fid_init)
@@ -112,6 +112,7 @@ solve!(prob; max_iter=100)
 ## Let's take a look at the final fidelity
 fid_final = unitary_rollout_fidelity(prob.trajectory, sys)
 println(fid_final)
+@assert fid_final > 0.99
 
 # Looks good!
 
@@ -120,10 +121,11 @@ plot_unitary_populations(prob.trajectory)
 
 
 # For fun, let's look at a minimum time pulse for this problem
-min_time_prob = UnitaryMinimumTimeProblem(prob, U_goal; final_fidelity=.99)
+min_time_prob = UnitaryMinimumTimeProblem(prob, U_goal; final_fidelity=.995)
 solve!(min_time_prob; max_iter=300)
 fid_final_min_time = unitary_rollout_fidelity(min_time_prob.trajectory, sys)
 println(fid_final_min_time)
+@assert fid_final_min_time > 0.99
 
 # And let's plot this solution
 plot_unitary_populations(min_time_prob.trajectory)
@@ -170,7 +172,7 @@ prob = UnitarySmoothPulseProblem(
     R_da=0.01,
     R_dda=0.01,
     Δt_max=Δt_max,
-    piccolo_options=PiccoloOptions()
+    piccolo_options=PiccoloOptions(bound_state=true),
 )
 fid_init = unitary_rollout_fidelity(prob.trajectory, sys)
 println(fid_init)
@@ -180,6 +182,7 @@ solve!(prob; max_iter=100)
 ## Let's take a look at the final fidelity
 fid_final = unitary_rollout_fidelity(prob.trajectory, sys)
 println(fid_final)
+@assert fid_final > 0.999
 
 # Again, looks good!
 
@@ -188,10 +191,11 @@ plot_unitary_populations(prob.trajectory)
 
 # For fun, let's look at a minimum time pulse for this problem
 
-min_time_prob = UnitaryMinimumTimeProblem(prob, U_goal; final_fidelity=.999)
+min_time_prob = UnitaryMinimumTimeProblem(prob, U_goal; final_fidelity=.9995)
 solve!(min_time_prob; max_iter=300)
 fid_final_min_time = unitary_rollout_fidelity(min_time_prob.trajectory, sys)
 println(fid_final_min_time)
+@assert fid_final_min_time > 0.999
 
 # And let's plot this solution
 plot_unitary_populations(min_time_prob.trajectory)