Resource-efficient context-aware dynamical decoupling embedding for arbitrary large-scale quantum algorithms

Understand

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.