Merge remote-tracking branch 'origin/develop' into feature-ams

fogi label fix needed to do AMS

.github/workflows/autodeploy.yml

@@ -4,8 +4,6 @@
 name: Deploy new version on pypi.org
 
 on:
-  push:
-    branches: [ "master" ]
   release:
     types:
       - published

.github/workflows/beta-master.yml

@@ -24,7 +24,7 @@ jobs:
       use-cython: ${{ matrix.use-cython }}
       run-unit-tests: 'true'
       run-extra-tests: 'true'
-      run-notebook-tests: 'true'
+      run-notebook-tests: 'false' # TODO: Turn off failing notebook tests for runner failures starting end of May 2024
 
 
 

.gitignore

@@ -31,6 +31,11 @@ doc/build
 *gst_checkpoints*
 *model_test_checkpoints*
 *standard_gst_checkpoints*
+*ibmqexperiment_checkpoint*
+
+# Serialization Testing Artifacts #
+###################################
+*LinearGateSetTomography_serialization*
 
 # Test Metadata #
 #################
@@ -39,6 +44,7 @@ test/output/pylint/*
 test/output/individual_coverage/*/*
 test/test_packages/cmp_chk_files/Fake_Dataset_none.txt.cache
 **.noseids
+**test_ibmq**
 
 # Tutorial Notebook Untracked Files #
 ####################################

optional-requirements.txt

@@ -10,5 +10,4 @@ cython
 cvxopt
 cvxpy
 seaborn
-qibo
 packaging

pygsti/algorithms/core.py

@@ -31,8 +31,9 @@
 from pygsti.modelmembers import states as _state
 from pygsti.circuits.circuitlist import CircuitList as _CircuitList
 from pygsti.baseobjs.resourceallocation import ResourceAllocation as _ResourceAllocation
-from pygsti.optimize.customlm import CustomLMOptimizer as _CustomLMOptimizer
-from pygsti.optimize.customlm import Optimizer as _Optimizer
+from pygsti.optimize.simplerlm import Optimizer as _Optimizer, SimplerLMOptimizer as _SimplerLMOptimizer
+from pygsti import forwardsims as _fwdsims
+from pygsti import layouts as _layouts
 
 _dummy_profiler = _DummyProfiler()
 
@@ -400,7 +401,7 @@ def _construct_ab(prep_fiducials, effect_fiducials, model, dataset, op_label_ali
         for j, rhostr in enumerate(prep_fiducials):
             opLabelString = rhostr + estr  # LEXICOGRAPHICAL VS MATRIX ORDER
             dsStr = opLabelString.replace_layers_with_aliases(op_label_aliases)
-            expd_circuit_outcomes = opLabelString.expand_instruments_and_separate_povm(model)
+            expd_circuit_outcomes = model.expand_instruments_and_separate_povm(opLabelString)
             assert(len(expd_circuit_outcomes) == 1), "No instruments are allowed in LGST fiducials!"
             unique_key = next(iter(expd_circuit_outcomes.keys()))
             outcomes = expd_circuit_outcomes[unique_key]
@@ -429,7 +430,7 @@ def _construct_x_matrix(prep_fiducials, effect_fiducials, model, op_label_tuple,
         for j, rhostr in enumerate(prep_fiducials):
             opLabelString = rhostr + _circuits.Circuit(op_label_tuple, line_labels=rhostr.line_labels) + estr
             dsStr = opLabelString.replace_layers_with_aliases(op_label_aliases)
-            expd_circuit_outcomes = opLabelString.expand_instruments_and_separate_povm(model)
+            expd_circuit_outcomes = model.expand_instruments_and_separate_povm(opLabelString)
             dsRow_fractions = dataset[dsStr].fractions
             assert(len(expd_circuit_outcomes) == nVariants)
 
@@ -617,7 +618,7 @@ def run_gst_fit_simple(dataset, start_model, circuits, optimizer, objective_func
     model : Model
         the best-fit model.
     """
-    optimizer = optimizer if isinstance(optimizer, _Optimizer) else _CustomLMOptimizer.cast(optimizer)
+    optimizer = optimizer if isinstance(optimizer, _Optimizer) else _SimplerLMOptimizer.cast(optimizer)
     objective_function_builder = _objfns.ObjectiveFunctionBuilder.cast(objective_function_builder)
     array_types = optimizer.array_types + \
         objective_function_builder.compute_array_types(optimizer.called_objective_methods, start_model.sim)
@@ -664,7 +665,7 @@ def run_gst_fit(mdc_store, optimizer, objective_function_builder, verbosity=0):
     objfn_store : MDCObjectiveFunction
         the objective function and store containing the best-fit model evaluated at the best-fit point.
     """
-    optimizer = optimizer if isinstance(optimizer, _Optimizer) else _CustomLMOptimizer.cast(optimizer)
+    optimizer = optimizer if isinstance(optimizer, _Optimizer) else _SimplerLMOptimizer.cast(optimizer)
     comm = mdc_store.resource_alloc.comm
     profiler = mdc_store.resource_alloc.profiler
     printer = VerbosityPrinter.create_printer(verbosity, comm)
@@ -677,16 +678,10 @@ def run_gst_fit(mdc_store, optimizer, objective_function_builder, verbosity=0):
         if _np.linalg.norm(mdc_store.model.to_vector() - v_cmp) > 1e-6:
             raise ValueError("MPI ERROR: *different* MC2GST start models"
                              " given to different processors!")                   # pragma: no cover
-
-    #MEM from ..baseobjs.profiler import Profiler
-    #MEM debug_prof = Profiler(comm)
-    #MEM debug_prof.print_memory("run_gst_fit1", True)
-
+
     if objective_function_builder is not None:
         objective_function_builder = _objfns.ObjectiveFunctionBuilder.cast(objective_function_builder)
-        #MEM debug_prof.print_memory("run_gst_fit2", True)
         objective = objective_function_builder.build_from_store(mdc_store, printer)  # (objective is *also* a store)
-        #MEM debug_prof.print_memory("run_gst_fit3", True)
     else:
         assert(isinstance(mdc_store, _objfns.ObjectiveFunction)), \
             "When `objective_function_builder` is None, `mdc_store` must be an objective fn!"
@@ -705,14 +700,8 @@ def run_gst_fit(mdc_store, optimizer, objective_function_builder, verbosity=0):
 
     printer.log("Completed in %.1fs" % (_time.time() - tStart), 1)
 
-    #if target_model is not None:
-    #  target_vec = target_model.to_vector()
-    #  targetErrVec = _objective_func(target_vec)
-    #  return minErrVec, soln_gs, targetErrVec
     profiler.add_time("do_mc2gst: total time", tStart)
-    #TODO: evTree.permute_computation_to_original(minErrVec) #Doesn't work b/c minErrVec is flattened
-    # but maybe best to just remove minErrVec from return value since this isn't very useful
-    # anyway?
+
     return opt_result, objective
 
 
@@ -853,7 +842,7 @@ def iterative_gst_generator(dataset, start_model, circuit_lists,
           (an "evaluated" model-dataset-circuits store).
     """
     resource_alloc = _ResourceAllocation.cast(resource_alloc)
-    optimizer = optimizer if isinstance(optimizer, _Optimizer) else _CustomLMOptimizer.cast(optimizer)
+    optimizer = optimizer if isinstance(optimizer, _Optimizer) else _SimplerLMOptimizer.cast(optimizer)
     comm = resource_alloc.comm
     profiler = resource_alloc.profiler
     printer = VerbosityPrinter.create_printer(verbosity, comm)
@@ -888,10 +877,30 @@ def _max_array_types(artypes_list):  # get the maximum number of each array type
     #The ModelDatasetCircuitsStore
     printer.log('Precomputing CircuitOutcomeProbabilityArray layouts for each iteration.', 2)
     precomp_layouts = []
+
+    #pre-compute a dictionary caching completed circuits for layout construction performance.
+    unique_circuits = list({ckt for circuit_list in circuit_lists for ckt in circuit_list})
+    if isinstance(mdl.sim, (_fwdsims.MatrixForwardSimulator, _fwdsims.MapForwardSimulator)):
+        precomp_layout_circuit_cache = mdl.sim.create_copa_layout_circuit_cache(unique_circuits, mdl, dataset=dataset)
+    else:
+        precomp_layout_circuit_cache = None
+
     for i, circuit_list in enumerate(circuit_lists):
         printer.log(f'Layout for iteration {i}', 2)
-        precomp_layouts.append(mdl.sim.create_layout(circuit_list, dataset, resource_alloc, array_types, verbosity= printer - 1))
-
+        precomp_layouts.append(mdl.sim.create_layout(circuit_list, dataset, resource_alloc, array_types, verbosity= printer - 1,
+                                                    layout_creation_circuit_cache = precomp_layout_circuit_cache))
+
+    #precompute a cache of possible outcome counts for each circuits to accelerate MDC store creation
+    if isinstance(mdl, _models.model.OpModel):
+        if precomp_layout_circuit_cache is not None: #then grab the split circuits from there.
+            expanded_circuit_outcome_list = mdl.bulk_expand_instruments_and_separate_povm(unique_circuits, 
+                                                                                          completed_circuits= precomp_layout_circuit_cache['completed_circuits'].values())
+        else:
+            expanded_circuit_outcome_list = mdl.bulk_expand_instruments_and_separate_povm(unique_circuits)        
+        outcome_count_by_circuit_cache = {ckt: len(outcome_tup) for ckt,outcome_tup in zip(unique_circuits, expanded_circuit_outcome_list)}
+    else:
+        outcome_count_by_circuit_cache = {ckt: mdl.compute_num_outcomes(ckt) for ckt in unique_circuits}
+
     with printer.progress_logging(1):
         for i in range(starting_index, len(circuit_lists)):
             circuitsToEstimate = circuit_lists[i]
@@ -908,7 +917,8 @@ def _max_array_types(artypes_list):  # get the maximum number of each array type
             mdl.basis = start_model.basis  # set basis in case of CPTP constraints (needed?)
             initial_mdc_store = _objfns.ModelDatasetCircuitsStore(mdl, dataset, circuitsToEstimate, resource_alloc,
                                                                   array_types=array_types, verbosity=printer - 1, 
-                                                                  precomp_layout = precomp_layouts[i])
+                                                                  precomp_layout = precomp_layouts[i],
+                                                                  outcome_count_by_circuit=outcome_count_by_circuit_cache)
             mdc_store = initial_mdc_store
 
             for j, obj_fn_builder in enumerate(iteration_objfn_builders):