Add GTHNoiseModel to noise.py

[mho291]

This PR introduces the GST-trained Heuristic (GTH) noise model, GTHNoiseModel, which is a composite noise model to approximate hardware noise.

Following the outlined in https://arxiv.org/pdf/2502.19872, gate set tomography (GST) data from hardware is used as inputs with trained neural networks to infer the effective noise parameters for single- and two-qubit operations.

The inferred noise parameters are used to construct the GTHNoiseModel, enabling simulation of circuits with noise characteristics that closely resembles hardware.

In the 2-qubit example, we provide the exact values for the parameters that make up the 2-qubit composite noise model. It has been shown to approximate the hardware closely.

In the 4-qubit example, we give ficticious noise parameters that make up the 4-qubit composite noise model.

Checklist:

• Reviewers confirm new code works as expected.
• Tests are passing.
• Coverage does not decrease.
• Documentation is updated.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 99.50%. Comparing base (9ed79ea) to head (082cbab).

Additional details and impacted files

@@           Coverage Diff           @@
##           master    #1837   +/-   ##
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  Coverage   99.50%   99.50%           
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  Files          78       78           
  Lines       13673    13726   +53     
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+ Hits        13605    13658   +53     
  Misses         68       68           

Flag	Coverage Δ
unittests	99.50% <100.00%> (+<0.01%)	⬆️

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