Browsing by Subject "quantum annealer"

There are two primary types of quantum computers: quantum annealers and circuit model computers. Quantum annealers are specifically designed to tackle particular problems, as opposed to circuit model computers, which can be viewed as universal quantum computers. Substantial efforts are underway to develop quantum-based algorithms for various classical computational problems. The objective of this thesis is to implement algorithms for solving graph problems using quantum annealer computers and analyse these implementations. The aim is to contribute to the ongoing development of algorithms tailored for this type of machine. Three distinct types of graph problems were selected: all pairs shortest path, graph isomorphism, and community detection. These problems were chosen to represent varying levels of computational complexity. The algorithms were tested using the D-Wave quantum annealer Advantage system 4.1, equipped with 5760 qubits. D-Wave provides a cloud platform called Leap and a Python library, Ocean tools, through which quantum algorithms can be designed and run using local simulators or real quantum computers in the cloud. Formulating graph problems to be solved on quantum annealers was relatively straightforward, as significant literature already contains implementations of these problems. However, running these algorithms on existing quantum annealer machines proved to be challenging. Even though quantum annealers currently boast thousands of qubits, algorithms performed satisfactorily only on small graphs. The bottleneck was not the number of qubits but rather the limitations imposed by topology and noise. D-Wave also provides hybrid solvers that utilise both the Quantum Processing Unit (QPU) and CPU to solve algorithms, which proved to be much more reliable than using a pure quantum solver.