Suppressing quantum circuit errors due to system variability

We present a post-compilation quantum circuit optimization technique that takes into account the variability in error rates that is inherent across present day noisy quantum computing platforms. This method consists of computing isomorphic subgraphs to input circuits and scoring each using heuristic cost functions derived from system calibration data. Using standard algorithmic test circuits we show that it is possible to recover on average nearly 40% of missing fidelity using better qubit selection via efficient to compute cost functions. We demonstrate additional performance gains by considering qubit placement over multiple quantum processors. The overhead from these tools is minimal with respect to other compilation steps such as qubit routing as the number of qubits increases. As such, our method can be used to find qubit mappings for problems at the scale of quantum advantage and beyond.Comment: 8 pages, 6 figure

Qiskit/qiskit: Qiskit 0.25.3

<h1>Changelog</h1> <h2>Fixed</h2> <ul> <li>Fix input normalisation of <code>transpile(initial_layout=...)</code> (backport #11031) (#11058)</li> <li>Fix calling backend.name() for backendV2 (#11065) (#11076) (#11092)</li> <li>Fix build filter coupling map with mix ideal/physical targets (#11009) (#11049)</li> <li>Emit a descriptive error when the QPY version is too new (#11094)</li> <li>BackendEstimator support BackendV2 without coupling_map (#10956) (#11006)</li> <li>Support dynamic circuit in BackendEstimator (#9700) (#10984)</li> <li>Avoid useless deepcopy of target with custom pulse gates in transpile (#10973) (#10978)</li> <li>Fix bug in qs_decomposition (#10850) (#10957)</li> </ul&gt