Learning to rank quantum circuits for hardware-optimized performance enhancement

HIGHLIGHTS

• What: The authors focus on the concrete problem of layout selection within a single processor: the mapping of virtual or abstract qubits in a compiled circuit to physical device qubits in a manner that maximizes circuit fidelity. The authors develop a new form of predictive QCVV for layout selection for arbitrary target circuits based on a heuristic, machine_learning solution. The authors report direct measurements on an IBM quantum computer revealing that different layouts of individual target circuits exhibit up to a factor of 11× variation in the ratio of the maximum achieved to median Hellinger fidelity, highlighting . . .
  

   

  

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