Revert "simplify gaugeopt in the non-LS case"

This reverts commit 6e9ccb1.

Author: rileyjmurray

pygsti/algorithms/gaugeopt.py

@@ -587,24 +587,45 @@ def _mock_objective_fn(v):
     else:
         # non-least-squares case where objective function returns a single float
         # and (currently) there's no analytic jacobian
+        dim = int(_np.sqrt(mxBasis.dim))
+        if n_leak > 0:
+            B = _tools.leading_dxd_submatrix_basis_vectors(dim - n_leak, dim, mxBasis)
+            """
+            ^ Need to do something else. 
+            
+              In a leakage-friendly basis the operation matrices can be partitioned as 
+                  [comp     , semileak1]
+                  [semileak2, fulleak  ].
+              Instead of projecting only on to comp, we want to keep everything
+              except fullleak. ... But I don't think we can implement that just by
+              pre-multiplying by a projector.
+            
+              I think this distinction might not be significant under certain idealized
+              assumptions, but small deviations from those conditions (which are certain
+              to happen in practice) might make it matter.
+            """
+            P = B @ B.T.conj()
+            if _np.linalg.norm(P.imag) > 1e-12:
+                raise ValueError()
+            else:
+                P = P.real
+            transform_mx_arg = (P, None)
+            # ^ The semantics of this tuple are defined by the frobeniusdist function
+            #   in the ExplicitOpModelCalc class. There are intended semantics for 
+            #   the second element of the tuple, but those aren't implemented yet so
+            #   for now I'm setting the second entry to None. -- Riley
+        else:
+            transform_mx_arg = (_np.eye(mxBasis.dim), None)
 
-        assert target_model is not None
         assert gates_metric != "frobeniustt"
         assert spam_metric  != "frobeniustt"
+        assert spam_metric == gates_metric
+        metric = spam_metric
+        # assert spam_metric == gates_metric
         # ^ Erik and Corey said these are rarely used. I've removed support for
         # them in this codepath (non-LS optimizer) in order to make it easier to
         # read my updated code for leakage-aware metrics. It wouldn't be hard to
         # add support back, but I just want to keep things simple. -- Riley
-        assert spam_metric == gates_metric
-        metric = spam_metric
-
-        dim = int(_np.sqrt(mxBasis.dim))
-        B = _tools.leading_dxd_submatrix_basis_vectors(dim - n_leak, dim, mxBasis)
-        P = B @ B.T.conj()
-        assert _np.linalg.norm(P.imag) <= 1e-12
-        P = P.real
-
-        log = []
 
         def _objective_fn(gauge_group_el, oob_check):
             mdl = _transform_with_oob_check(model, gauge_group_el, oob_check)
@@ -620,35 +641,25 @@ def _objective_fn(gauge_group_el, oob_check):
                 spamPenaltyVec = _spam_penalty(mdl, spam_penalty_factor, mdl.basis)
                 ret += _np.sum(spamPenaltyVec)
 
+            assert target_model is not None
+            """
+            Leakage-aware metric supported, per implementation in mdl.frobeniusdist.
+            Refer to how mdl.frobeniusdist handles the case when transform_mx_arg
+            is a tuple in order to understand how the leakage-aware metric is defined.
+            
+                Idea: raise an error if mxBasis isn't leakage-friendly. 
+                    PROBLEM: if we're deep in the code then we end up needing to be
+                            working in a basis that has the identity matrix as its
+                            first element. The leakage-friendly basis doesn't even
+                            have the identity matrix as an element, let alone the first element
+            
+                TODO: dig into function calls below and see where we can
+                    access the mxBasis object. (I'm sure we can.)
+                    Looks like it isn't accessible within ExplicitOpModelCalc objects.
+                    It's most definitely available in ExplicitOpModel.       
+            """
             if "frobenius" in metric:
-                d = 0
-                nSummands = 0.0
-                for opLabel, gate in mdl.operations.items():
-                    wt = item_weights.get(opLabel, opWeight)
-                    gate_mx = gate.to_dense()
-                    other_mx = target_model.operations[opLabel].to_dense()
-                    delta = gate_mx - other_mx
-                    delta = delta @ P
-                    val = _np.linalg.norm(delta.flatten())
-                    d += wt * val**2
-                    nSummands += wt * (gate.dim)**2
-
-                for lbl, rhoV in mdl.preps.items():
-                    wt = item_weights.get(lbl, spamWeight)
-                    d += wt * rhoV.frobeniusdist_squared(target_model.preps[lbl], None, None)
-                    nSummands += wt * rhoV.dim
-
-                for lbl, Evec in mdl.effects.items():
-                    wt = item_weights.get(lbl, spamWeight)
-                    evec = Evec.to_dense()
-                    other = target_model.effects[lbl].to_dense()
-                    delta = evec - other
-                    delta = delta @ P
-                    val = _np.linalg.norm(delta.flatten())
-                    d += wt * val**2
-                    nSummands += wt * Evec.dim
-
-                val = _np.sqrt(d / nSummands)
+                val = mdl.frobeniusdist(target_model, transform_mx_arg, item_weights)
                 if "squared" in metric:
                     val = val ** 2
                 ret += val
@@ -696,7 +707,7 @@ def _objective_fn(gauge_group_el, oob_check):
             else:
                 raise ValueError("Invalid metric: %s" % metric)
             # end _objective_fn
-
+        
         _jacobian_fn = None
 
     return _objective_fn, _jacobian_fn