The Learning and Compiled Calibration of Clock Cycle Errors in QuantumComputing Architectures

The performance of quantum computers is inhibited by the occurrence of physical errors during computation.

For this reason, the development of increasingly sophisticated error suppression and error characterization techniques is central to the progress of quantum computing.

Error distributions are considerably influenced by the precise gate scheduling across the entire quantum processing unit.

To account for this holistic feature, we may ascribe each error profile to a (clock) cycle, which is a detailed list of instructions performed over the whole chip.

A celebrated technique known as randomized compiling introduces some randomness within cycles' instructions, which yields effective cycles with simpler, stochastic error signatures.

In the present work, we leverage known Pauli channel estimation techniques to derive a highly efficient and scalable method to estimate with Heisenberg-like precision the marginal error distribution associated with any effective cycle of interest.

Furthermore, we develop a fast compilation-based calibration method to identify and suppress local coherent error sources occurring in any effective cycle of interest.

We performed both protocols on IBM-Q 5-qubit devices.

Via our calibration scheme, we obtained up to a 5-fold improvement of the circuit performance.