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
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article

Hybrid Quantum-Classical AI Models for Complex Problem Solving

Zenodo (CERN European Organization for Nuclear Research)
Quantum Computing Algorithms and Architecture
article

Hybrid Quantum-Classical AI Models for Complex Problem Solving

article en

Abstract

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.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 68%
Quantum Computing Algorithms and Architecture
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