Characterizing and mitigating coherent errors in a trapped ion quantum processor using hidden inverses

Quantum computing testbeds exhibit high-fidelity quantum control over small collections of qubits, enabling performance of precise, repeatable operations followed by measurements. Currently, these noisy intermediate-scale devices can support a sufficient number of sequential operations prior to decoherence such that near term algorithms can be performed with prox-imate accuracy (like chemical accuracy for quantum chemistry problems). While the results of these algorithms are imper-fect, these imperfections can help boot-strap quantum computer testbed develop-ment. Demonstrations of these algorithms over the past few years, coupled with the idea that imperfect algorithm perfor-mance can be caused by several domi-nant noise sources in the quantum pro-cessor, which can be measured and cali-brated during algorithm execution or in post-processing, has led to the use of noise mitigation to improve typical com-putational results. Conversely, bench-mark algorithms coupled with noise miti-gation can help diagnose the nature of the noise, whether systematic or purely ran -dom. Here, we outline the use of coher-ent noise mitigation techniques as a char-acterization tool in trapped-ion testbeds. We perform model-fitting of the noisy data to determine the noise source based on realistic physics focused noise mod-els and demonstrate that systematic noise amplification coupled with error mitiga-tion schemes provides useful data for noise model deduction. Further, in order to connect lower level noise model details with application specific performance of near term algorithms, we experimentally construct the loss landscape of a vari-ational algorithm under various injected noise sources coupled with error mitiga-tion techniques. This type of connection enables application-aware hardware code-sign, in which the most important noise sources in specific applications, like quan-tum chemistry, become foci of improve-ment in subsequent hardware generations.