Engineering Quantum Process Fidelity via Generalized Markovian Noise

Markovian noise causes errors in quantum processes in ways that are difficult to correct. Surprisingly, theoretical studies have recently proposed that short-memory (generalized Markovian) noise can be used as a resource to mitigate the effects of Markovian noise. We have investigated the efficacy of adding generalized Markovian noisy signals, with various types of memory kernels, at improving fidelity and reducing decoherence in superconducting qubits, using both computational and experimental methods. We present quantum trajectory simulations and experimental tests of different corrective noise schemes, and discuss paths forward for optimizing quantum process fidelity.