  Quantum computers have the potential to solve problems that are intractable
to classical computers, nevertheless they have high error rates. One
significant kind of errors is known as Readout Errors. Current methods, as the
matrix inversion and least-squares, are used to unfold (correct) readout
errors. But these methods present many problems like oscillatory behavior and
unphysical outcomes. In 2020 Benjamin Nachman et al. suggested a technique
currently used in HEP, to correct detector effects. This method is known as the
Iterative Bayesian Unfolding (IBU), and they have proven its effectiveness in
mitigating readout errors, avoiding problems of the mentioned methods.
Therefore, the main objective of our thesis is to mitigate readout noise of
quantum computers, using this powerful unfolding method. For this purpose we
generated a uniform distribution in the Yorktown IBM Q Machine, for 5 Qubits,
in order to unfold it by IBU after being distorted by noise. Then we repeated
the same experiment with a Gaussian distribution. Very satisfactory results and
consistent with those of B. Nachman et al., were obtained. After that, we took
a second purpose to explore unfolding in a larger qubit system, where we
succeed to unfold a uniform distribution for 7 Qubits, distorted by noise from
the Melbourne IBM Q Machine. In this case, the IBU method showed much better
results than other techniques.
