Résumé:
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.
Keywords : Quantum computing, Qubit, Noise, Readout errors, Unfolding,
High energy physics, Iterative Bayesian Unfolding, IBM Yorktown
quantum computer, IBM Melbourne quantum computer.
