Hybrid Quantum-Classical AI Models for Complex Problem Solving
This report explores the intersection of Artificial Intelligence and Hybrid Quantum-Classical Systems, focusing on their ability to solve complex problems in different sectors. The report therefore sets out the various stages of AI development, noting its huge milestones, and how machines can learn. It then goes on to explain how quantum computing is expected to upgrade AI - particularly with respect to optimization and machine learning, which is going to be used in applications in drug discovery, financial portfolio optimization, and logistics. Other report domains discussed also include actual applications of hybrid quantum-classical systems and the setbacks associated with the integration of quantum technologies. Finally, the paper speaks about future prospects within this hybrid approach, signifying transformed capacities within AI and quantum computing that could be leveraged towards solutions of global complex problems.
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2024-12-06
- DOI
- https://doi.org/10.5281/zenodo.14286984
- Primary Topic
- Quantum Computing Algorithms and Architecture
- Type
- article
- Field-Weighted Citation Impact
- 0.00