Despite a wide array of possible benefits, there are still many challenges to overcome before the world understands the potential of bringing together the forces of quantum computing and AI. Quantum computers are still in the early stages of their development, and as such, they are prone to errors. In line with this infantile state are its algorithms, which are also still being developed; it’s not yet clear which of these will be most effective for AI applications. However, as quantum computing technology matures, its potential to transform AI will only grow.
The connection between quantum computing and AI is a promising area of research that could lead to significant breakthroughs in both fields. In powering AI, quantum computing could transform areas such as machine learning and optimization. Conversely, AI can be used in the development and control of quantum computers.
What we can say is that, combined, they are beginning to intersect in ways that promise to reshape the future of computation and problem-solving. While AI has made significant strides in recent years, its potential is still limited by the capabilities of classical computers. Going back to the Pac-Man example from part 1, consider the difficulty it takes sophisticated AI, large language models (LLMs) to recreate the classic video game experience. However, with the addition of quantum computing and its ability to perform calculations that are impossible for even the most powerful supercomputers, we can begin piecing together what it will mean to unlock AI’s potential.
AI Applications and Mannerisms of Quantum’s Algorithms
One of the key areas where quantum computing can enhance AI is in machine learning, whose learning algorithms, particularly deep learning models, require vast amounts of computational power to train on large datasets. Among the key quantum algorithms is one called quantum annealing, which, in our Pac-Man scenario, translates into finding the shortest route between Pac-Man and those ghosts. Another is variational quantum eigensolvers (VQE), whose applications affect everything from finance to chemistry, and have the potential to significantly speed up the training process.
One promising area where quantum computing can impact AI is in optimization problems. Concepts like route planning, resource allocation, and drug discovery involve finding the optimal solution from a vast number of possibilities. To this end, another quantum algorithm called Grover’s algorithm offers the ability to search through such possibilities much faster than classical algorithms. As a result, quantum optimization could lead to more efficient and effective AI solutions for these types of problems.
As AI becomes more sophisticated, it will increasingly be used to protect sensitive data. However, classical encryption methods may become vulnerable to attacks from quantum computers. As both technologies continue to evolve, the synergy between them will likely lead to new and unforeseen applications that could reshape the future of technology.
In the third and final installment in this series, we’ll explore the association between quantum computing and today’s fiber networks, including a deeper understanding of the applications that the technologies will contribute to serving.