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@ARTICLE{MontaezBarrera:1048663,
      author       = {Montañez-Barrera, J. A. and Beretta, G. P. and Michielsen,
                      Kristel and von Spakovsky, Michael R},
      title        = {{D}iagnosing crosstalk in large-scale {QPU}s using
                      zero-entropy classical shadows},
      journal      = {Quantum science and technology},
      volume       = {11},
      number       = {1},
      issn         = {2058-9565},
      address      = {Philadelphia, PA},
      publisher    = {IOP Publishing},
      reportid     = {FZJ-2025-04791},
      pages        = {17},
      year         = {2026},
      abstract     = {As quantum processing units (QPUs) scale toward hundreds of
                      qubits, diagnosing noise-induced correlations (crosstalk)
                      becomes critical for reliable quantum computation. In this
                      work, we introduce Zero-Entropy Classical Shadows (ZECS), a
                      diagnostic tool that uses information of a rank-one quantum
                      state tomography reconstruction from classical shadow
                      information to make a crosstalk diagnosis. We use ZECS on
                      trapped ion and superconductive QPUs including $ionq_forte$
                      (36 qubits), $ibm_brisbane$ (127 qubits), and $ibm_fez$ (156
                      qubits), using from 1000 to 6000 samples. With these
                      samples, we use the ZECS to characterize crosstalk among
                      disjoint qubit subsets across the full hardware. This
                      information is then used to select low-crosstalk qubit
                      subsets on $ibm_fez$ for executing the quantum approximate
                      optimization algorithm on a 20-qubit problem. Compared to
                      the best qubit selection via Qiskit transpilation, our
                      method improves solution quality by $10\%$ and increases
                      algorithmic coherence by $33\%.$ ZECS offers a scalable and
                      measurement-efficient approach to diagnosing crosstalk in
                      large-scale QPUs.},
      cin          = {JSC},
      ddc          = {530},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5122 - Future Computing $\&$ Big Data Systems (POF4-512) /
                      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)POF4-5122 / G:(DE-HGF)POF4-5111 /
                      G:(DE-Juel1)BMBF-13N16149},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1088/2058-9565/ae1e99},
      url          = {https://juser.fz-juelich.de/record/1048663},
}