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@ARTICLE{MontaezBarrera:1044402,
      author       = {Montañez-Barrera, J. A. and Willsch, Dennis and
                      Michielsen, Kristel},
      title        = {{T}ransfer learning of optimal {QAOA} parameters in
                      combinatorial optimization},
      journal      = {Quantum information processing},
      volume       = {24},
      number       = {5},
      issn         = {1570-0755},
      address      = {Dordrecht},
      publisher    = {Springer Science + Business Media B.V.},
      reportid     = {FZJ-2025-03166},
      pages        = {129},
      year         = {2025},
      abstract     = {Solving combinatorial optimization problems (COPs) is a
                      promising application ofquantum computation, with the
                      quantum approximate optimization algorithm (QAOA)being one
                      of the most studied quantum algorithms for solving them.
                      However, multiple factors make the parameter search of the
                      QAOA a hard optimization problem. Inthis work, we study
                      transfer learning (TL), a methodology to reuse pre-trained
                      QAOAparameters of one problem instance into different COP
                      instances. This methodologycan be used to alleviate the
                      necessity of classical optimization to find good
                      parametersfor individual problems. To this end, we select
                      small cases of the traveling salesman problem (TSP), the bin
                      packing problem (BPP), the knapsack problem (KP),the
                      weighted maximum cut (MaxCut) problem, the maximal
                      independent set (MIS)problem, and portfolio optimization
                      (PO), and find optimal β and γ parameters forp layers. We
                      compare how well the parameters found for one problem adapt
                      to theothers. Among the different problems, BPP is the one
                      that produces the best transferable parameters, maintaining
                      the probability of finding the optimal solution abovea
                      quadratic speedup over random guessing for problem sizes up
                      to 42 qubits andp = 10 layers. Using the BPP parameters, we
                      perform experiments on IonQ Harmony and Aria, Rigetti
                      Aspen-M-3, and IBM Brisbane of MIS instances for up to
                      18qubits. The results indicate that IonQ Aria yields the
                      best overlap with the ideal probability distribution.
                      Additionally, we show that cross-platform TL is possible
                      using theD-Wave Advantage quantum annealer with the
                      parameters found for BPP. We showan improvement in
                      performance compared to the default protocols for MIS with
                      up to170 qubits. Our results suggest that there are QAOA
                      parameters that generalize wellfor different COPs and
                      annealing protocols.},
      cin          = {JSC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / BMBF 13N16149 -
                      QSolid - Quantencomputer im Festkörper (BMBF-13N16149)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel1)BMBF-13N16149},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001485851800001},
      doi          = {10.1007/s11128-025-04743-4},
      url          = {https://juser.fz-juelich.de/record/1044402},
}