ML Training step fails to complete

System and Environment Information

Running on mac os 15.6.1, python version 3.13.0 and mqt-predictor version 2.3.0

Bug Description

I'm following the framework setup section in the documentation. I was able to run Step 2 (RL training) in about 4 hours on my M4 Pro, using 50k timesteps for IBM Falcon 27.

For Step 3, I've been running the code for several hours but am not able to tell the progress in the ML training phase. Is there a way to assess the progress? Is there an estimate of how long it should take?

You can look at this log for output from my run before I killed the process. It looks like a call to bqskit is timing out, and then later on, it looks like some subprocesses may not be handling signals properly and lingering.

Steps to Reproduce

1. Setup environment with uv init and uv add mqt.predictor
2. Follow the steps in https://mqt.readthedocs.io/projects/predictor/en/latest/setup.html
3. For step 3, run:

from mqt.predictor.ml import setup_device_predictor
from mqt.bench.targets import get_device

devices = [get_device("ibm_falcon_27")]
setup_device_predictor(
    devices=devices,
    figure_of_merit="expected_fidelity",
)

Start with the Step 3 setup documentation and the setup_device_predictor entry point, then compare its behavior with the linked gist log for ibm_falcon_27. Completion should make ML training progress and duration understandable and address the reported bqskit timeout or lingering subprocess behavior in a reproducible run.