Pontus Vikstål, Tillämpad kvantfysik

Sammanfattning: 

This thesis explores the Quantum Approximate Optimization Algorithm (QAOA), a hybrid classical-quantum algorithm designed to solve combinatorial optimization problems. The goal of this algorithm is to iteratively optimize a variational state to approximate the ground state of a cost Hamiltonian that encodes a combinatorial optimization problem. The focus of this thesis is the application of QAOA to the Exact Cover problem, an abstraction of the Tail Assignment problem – a problem omnipresent in aviation.

This thesis also includes a demonstration of the practical implementation of QAOA on a superconducting quantum computer, demonstrating empirical proof of QAOA's functionality. It also investigates running QAOA using noise-biased qubits, namely cat qubits, which exhibit resilience to certain types of errors.

This thesis also explores novel multi-qubit gates obtained from the simultaneous application of two controlled-Z gates on current quantum hardware, leading to the efficient creation of large entangled states.

Lastly, the thesis delves into virtual distillation, an error-mitigation protocol, and assesses its performance under various types of errors.

Overall, this thesis validates the promise of QAOA in solving real-world optimization problems while also offering insights into error mitigation.