Model reparameterization changing outcome probabilities

[juangmendoza19]

calling set_all_parameterizations on a full TP model to a GLND model is changing some outcome probabilities non-trivially.

To reproduce:

`from pygsti.modelpacks import smq1Q_XY as std

datagen_model = std.target_model("GLND")

#Arbitrary error where I observed the problem error_vec = [0] * 48 error_vec[0] = .01 datagen_model.from_vector(error_vec)

design = std.create_gst_experiment_design(16)

#Circuit with the maximum difference bad_circuit = design.all_circuits_needing_data[394]

datagen_model_copy = datagen_model.copy() datagen_model_copy.set_all_parameterizations("full TP") datagen_model_copy.set_all_parameterizations("GLND", ideal_model=std.target_model("GLND"))

datagen_model.probabilities(bad_circuit)['0'] - datagen_model_copy.probabilities(bad_circuit)['0']`

Expected behavior The code above outputs a probability difference of -1.406968064276981e-08. This is a substantial difference causing issues in my current project which requires comparison of gauge-equivalent models.

Environment:

• pyGSTi version 0.9.12.1pyth
• python version 3.10.14
• macOS Sonoma 14.4.1

Additional context After an email exchange with Riley and Corey, Riley identified the problem in the state preparation. One of the vector entries deviates by 2.7057608985464707e-08 after conversion. This makes sense considering the model only has errors in the state preparation.

I believe I have identified the issue being in pygsti/modelmembers/states/__init__py line 269. This scipy optimization returns the exact number above "2.7057608985464707e-08" as the error. I tried changing the tolerance of the optimization, but this did not seem to change its behavior.