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@ARTICLE{Ji:1041624,
      author       = {Ji, Yanjun and Chen, Xi and Polian, Ilia and Ban, Yue},
      title        = {{A}lgorithm-oriented qubit mapping for variational quantum
                      algorithms},
      journal      = {Physical review applied},
      volume       = {23},
      number       = {3},
      issn         = {2331-7019},
      address      = {College Park, Md. [u.a.]},
      publisher    = {American Physical Society},
      reportid     = {FZJ-2025-02353},
      pages        = {034022},
      year         = {2025},
      abstract     = {Quantum algorithms implemented on near-term devices require
                      qubit mapping due to noise and limited qubit connectivity.
                      In this paper we propose a strategy called
                      algorithm-oriented qubit mapping (AOQMAP) that aims to
                      bridge the gap between exact and scalable mapping methods by
                      utilizing the inherent structure of algorithms. While exact
                      methods provide optimal solutions, they become intractable
                      for large circuits. Scalable methods, like swap networks,
                      offer fast solutions but lack optimality. AOQMAP bridges
                      this gap by leveraging algorithmic features and their
                      association with specific device substructures to achieve
                      depth-optimal and scalable solutions. The proposed strategy
                      follows a two-stage approach. First, it maps circuits to
                      subtopologies to meet connectivity constraints. Second, it
                      identifies the optimal qubits for execution using a cost
                      function and performs postselection among execution results
                      across subtopologies. Notably, AOQMAP provides both scalable
                      and optimal solutions for variational quantum algorithms
                      with fully connected two-qubit interactions on common
                      subtopologies including linear, T-, and H-shaped, minimizing
                      circuit depth. Benchmarking experiments conducted on IBM
                      quantum devices demonstrate significant reductions in gate
                      count and circuit depth compared to Qiskit, Tket, and swap
                      network. Specifically, AOQMAP achieves up to an $82\%$
                      reduction in circuit depth and an average $138\%$ increase
                      in success probability. This scalable and algorithm-specific
                      approach holds the potential to optimize a wider range of
                      quantum algorithms.},
      cin          = {PGI-12},
      ddc          = {530},
      cid          = {I:(DE-Juel1)PGI-12-20200716},
      pnm          = {5221 - Advanced Solid-State Qubits and Qubit Systems
                      (POF4-522)},
      pid          = {G:(DE-HGF)POF4-5221},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001469034100001},
      doi          = {10.1103/PhysRevApplied.23.034022},
      url          = {https://juser.fz-juelich.de/record/1041624},
}