  Agile networks with fast adaptation and reconfiguration capabilities are
required for on-demand provisioning of various network services.
  We propose a new methodical framework for short-time network optimization
based on quantum computing (QC) and integer linear program (ILP) models, which
has the potential of realizing a real-time network automation. We define
methods to map a nearly real-world ILP model for resource provisioning to a
quadratic unconstrained binary optimization (QUBO) problem, which is solvable
on quantum annealer (QA).
  We concentrate on the three-node network to evaluate our approach and its
obtainable quality of solution using the state-of-the-art quantum annealer
D-Wave Advantage 5.2/5.3. By studying the annealing process, we find annealing
configuration parameters that obtain feasible solutions close to the reference
solution generated by the classical ILP-solver CPLEX.
  Further, we studied the scaling of the network problem and provide
estimations on quantum annealer's hardware requirements to enable a proper QUBO
problem embedding of larger networks. We achieved the QUBO embedding of
networks with up to 6 nodes on the D-Wave Advantage. According to our estimates
a real-sized network with 12 to 16 nodes require a QA hardware with at least
50000 qubits or more.
