Cycle Error Reconstruction on Trapped Ion Quantum Computer

Cycle Error Reconstruction provides an efficient and scalable way to learn the details of the noise profile on our quantum system. However computing the joint probability distribution of all the possible pauli errors becomes intractable with the increase in the system size and requires an exponential number of experiments. Here we show how we can efficiently reconstruct the joint probability distribution of all the possible pauli errors on a 14 qubits ion trap device using the idea of Gibbs Random Field. We also discuss the implication this has on fault tolerance.