  Error mitigation is an essential component of achieving a practical quantum
advantage in the near term, and a number of different approaches have been
proposed. In this work, we recognize that many state-of-the-art error
mitigation methods share a common feature: they are data-driven, employing
classical data obtained from runs of different quantum circuits. For example,
Zero-noise extrapolation (ZNE) uses variable noise data and Clifford-data
regression (CDR) uses data from near-Clifford circuits. We show that Virtual
Distillation (VD) can be viewed in a similar manner by considering classical
data produced from different numbers of state preparations. Observing this fact
allows us to unify these three methods under a general data-driven error
mitigation framework that we call UNIfied Technique for Error mitigation with
Data (UNITED). In certain situations, we find that our UNITED method can
outperform the individual methods (i.e., the whole is better than the
individual parts). Specifically, we employ a realistic noise model obtained
from a trapped ion quantum computer to benchmark UNITED, as well as other
state-of-the-art methods, in mitigating observables produced from random
quantum circuits and the Quantum Alternating Operator Ansatz (QAOA) applied to
Max-Cut problems with various numbers of qubits, circuit depths and total
numbers of shots. We find that the performance of different techniques depends
strongly on shot budgets, with more powerful methods requiring more shots for
optimal performance. For our largest considered shot budget ($10^{10}$), we
find that UNITED gives the most accurate mitigation. Hence, our work represents
a benchmarking of current error mitigation methods and provides a guide for the
regimes when certain methods are most useful.
