Quantum Computing for AI: Current Status and Future Roadmap

The convergence of quantum computing and artificial intelligence (AI) promises to redefine the boundaries of computational capability. This paper explores the current status and future roadmap of using quantum technologies to advance AI. Currently, in the Noisy Intermediate-Scale Quantum (NISQ) era, research focuses on hybrid quantum-classical algorithms where quantum processors act as co-processors for specific, complex tasks. We examine key algorithmic frameworks such as Quantum Machine Learning (QML), including variational quantum circuits and quantum kernel methods, which demonstrate potential for accelerating tasks like pattern recognition and optimization. These approaches show theoretical advantages in processing high-dimensional data spaces more efficiently than classical counterparts. However, significant challenges persist, including qubit decoherence, error rates, and scalability issues. This study outlines a pragmatic roadmap for the field, navigating from the current NISQ limitations toward the anticipated era of fault-tolerant quantum computing. The future trajectory involves developing more robust quantum hardware, advanced error correction codes, and tailored QML algorithms to ultimately achieve a quantum advantage in solving real-world AI problems. This synthesis aims to provide a clear overview of the present landscape and a strategic vision for researchers navigating this rapidly evolving interdisciplinary frontier.

Keywords:

Artificial inteligence , Machine Learning , Deep Learning , Neural Network , Computer Vision