Consider creating a mechanism for submitting multiple times to the same hybrid sampler

[arcondello]

For example, if you wanted to submit multiple times to the hybrid NL solver without reuploading the model between runs, you could do so with something like

import numpy as np

from dwave.cloud.computation import Future
from dwave.optimization.generators import knapsack
from dwave.system import LeapHybridNLSampler


model = knapsack(range(100), range(100), capacity=100)
x, = model.iter_decisions()

sampler = LeapHybridNLSampler()

# Get the timelimit for the problem
time_limit = sampler.estimated_min_time_limit(model)

# We can upload at most one state
problem_data_id = sampler.solver.upload_nlm(model, max_num_states=1).result()

def hook(model, future):
    model.states.from_file(future.answer_data, check_header=False)

# 10 submissions
futures = []
for _ in range(10):
    futures.append(sampler.solver.sample_nlm(problem_data_id, time_limit=time_limit))

states = []
for completed in Future.as_completed(futures):
    model.states.from_future(completed, hook)

    # This needs to be saved "off" the model! It will be overwritten in the
    # next loop
    print(x.state())

    states.extend(np.asarray(x.state(i)) for i in range(len(model.states)))

print(states)

This seems to be a semi-common use case so bundling it into a method or function might be helpful.

Probably needs dwavesystems/dwave-optimization#185 in the case of NL to be done elegantly. For CQM/BQM/DQM we could use dimod.concatenate().

[randomir]

Implementing #528 (including problem_data_id) could help here.