Banca de DEFESA: ALBERTO BEZERRA DE PALHARES JUNIOR

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : ALBERTO BEZERRA DE PALHARES JUNIOR
DATE: 12/06/2023
TIME: 09:00
LOCAL: online
TITLE:

QAOA Applied to the Portfolio Optimization Problem

KEY WORDS:

QAOA, Portfolio Optimization, Qiskit.

PAGES: 108
BIG AREA: Ciências Exatas e da Terra
AREA: Física
SUMMARY:

Quantum computing is no longer in its early stages. There already exists quantum computers with more qubits than a classical
computer is capable of simulating. This current stage is considered intermediate and is therefore called the NISQ era (noisy
intermediate-scale quantum). The main feature of this current stage is that there are still not enough qubits to perform quantum
error correction, hence the noisy name. In this context of quantum computing without quantum error correction and with an
intermediate number of qubits, variational algorithms gained prominence and, among them, there is one called QAOA
(quantum approximate optimization algorithm). As the name suggests, this is a quantum algorithm that approximates the
solution of optimization problems. The objective of this work was to apply this algorithm to solve an optimization problem in
the finance area known as portfolio optimization. This application took place both in an ideal way (without noise) and in a way
consistent with the current capacity of quantum computers (with noise). Both were simulated using IBM's Python tool for
simulation and access of quantum computers via cloud called Qiskit. The results suggest that the QAOA performance with
noise was, as expected, worse than the ideal case, but still satisfactory within the limitations of the method.

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