Quantum Computing and Artificial Intelligence Use Cases

The convergence of artificial intelligence (AI) and quantum computing (QC) holds transformational potential across the economy. AI has evolved since its inception in the 1950s and now includes a wide range of approaches and an even wider range of application areas. QC, on the other hand, is still in the early days of a long-term research and development (R&D) path but has enormous future potential that would rival what is currently unfolding for AI. QC is projected to dramatically increase the scale, complexity, and scope of problems that can be solved computationally, while AI has already demonstrated its capacity to produce value in solving problems across numerous domains. As these two fields continue to develop, their combined use may offer opportunities to go beyond the current limits of either technology. 

Though independent technologies, QC and AI can complement each other in many significant and multidirectional ways. For example, AI could assist QC by accelerating the development of circuit design, applications, and error correction and generating test data for algorithm development. QC can solve certain types of problems more efficiently, such as optimization and probabilistic tasks, potentially enhancing the ability of AI models to analyze complex patterns or perform computations that are infeasible for classical systems. A hybrid approach integrating the strengths of classical AI methods with the potential of QC algorithms leverages the two technologies to substantially reduce algorithmic complexity, improving the efficiency of computational processes and resource allocation. 

This report summarizes different types of synergies that may emerge between QC and AI, and three recommendations for spurring the viability and adoption of QC + AI technologies.