Job operations are not parallelizable

I'm trying to submit and process results of multiple jobs in parallel but I'm noticing that, in many cases, jobs are submitted sequentially.

import datetime
import imp
from multiprocessing import Pool
from azure.quantum import Workspace
from azure.quantum.optimization import Problem, ProblemType, Term
from azure.quantum.target.toshiba import SimulatedBifurcationMachine
import logging


def create_problem():
    problem = Problem('Test_Problem', problem_type=ProblemType.pubo)
    problem.add_terms([
        Term(c=1, indices=[]),
        Term(c=2, indices=[1, 2]),
        Term(c=-3, indices=[1, 2]),
        Term(c=1, indices=[0, 2])
    ])
    return problem


def exec_job(no):
    workspace = Workspace (
        resource_id = "<workspace-id>",
        location= "<location>"
    )

    problem = create_problem()
    solver = SimulatedBifurcationMachine(workspace, loops=0, timeout=10)
    print(str(datetime.datetime.utcnow()), "\t[%d] solver.submit() start" % no)
    job = solver.submit(problem)
    print(str(datetime.datetime.utcnow()), "\t[%d] job.id=%s" % (no, str(job.id)))

    print(str(datetime.datetime.utcnow()), "\t[%d] job.get_results() start" % no)
    result = job.get_results()
    print(str(datetime.datetime.utcnow()), "\t[%d] job.details=%s" % (no, str(job.details)))


if __name__ == '__main__':
    logging.basicConfig(level=logging.DEBUG)

    njobs = 2
    with Pool(processes=njobs) as pool:
        pool.map(func=exec_job, iterable=range(njobs))

For the above, output is:
2022-04-21 18:40:23.466530 [0] solver.submit() start
2022-04-21 18:40:23.542727 [1] solver.submit() start
2022-04-21 18:40:28.752803 [1] job.id=810bc3bb-c1a2-11ec-baa1-b831b575aea6
2022-04-21 18:40:28.752803 [1] job.get_results() start
..........2022-04-21 18:40:52.826348 [1] job.details={..........}
2022-04-21 18:44:19.108243 [0] job.id=81018a8e-c1a2-11ec-adf4-b831b575aea6
2022-04-21 18:44:19.109211 [0] job.get_results() start
..........2022-04-21 18:44:42.799796 [0] job.details={..............}

You can see that job 1 was submitted and awaited while job 0 hasn't been submitted at the same time and was done 4 mins after the first job.

Start by running the reported multiprocessing.Pool example with exec_job, then trace the solver.submit() and job.get_results() entry points used by SimulatedBifurcationMachine. Compare the timestamps for both workers and identify where submission or result retrieval becomes serialized. Done means independent jobs can be submitted and processed concurrently without the observed multi-minute delay.