Resource-efficient context-aware dynamical decoupling embedding for
  arbitrary large-scale quantum algorithms
We introduce and implement GraphDD: an efficient method for real-time,
circuit-specific, optimal embedding of dynamical decoupling (DD) into
executable quantum algorithms. We demonstrate that for an arbitrary quantum
circuit, GraphDD exactly refocuses both quasi-static single-qubit dephasing and
crosstalk idling errors over the entire circuit, while using a minimal number
of additional single-qubit gates embedded into idle periods. The method relies
on a graph representation of the embedding problem, where the optimal
decoupling sequence can be efficiently calculated using an algebraic
computation that scales linearly with the number of idles. This allows optimal
DD to be embedded during circuit compilation, without any calibration overhead,
additional circuit execution, or numerical optimization. The method is generic
and applicable to any arbitrary circuit; in compiler runtime the specific
pulse-sequence solutions are tailored to the individual circuit, and consider a
range of contextual information on circuit structure and device connectivity.
We verify the ability of GraphDD to deliver enhanced circuit-level error
suppression on 127-qubit IBM devices, showing that the optimal circuit-specific
DD embedding resulting from GraphDD provides orders of magnitude improvements
to measured circuit fidelities compared with standard embedding approaches
available in Qiskit.
Paul Coote Roman Dimov Smarak Maity Gavin S. Hartnett Michael J. Biercuk Yuval Baum