While quantum computing has demonstrated theoretical advantages for several classes of problems, translating these advantages into practical impact remains a challenging task. Many real-world problems in areas such as optimization, chemistry, and machine learning continue to be computationally intractable on classical systems, motivating the exploration of quantum approaches.

This talk will present an overview of the current state of quantum computing, focusing on how emerging quantum algorithms and hybrid classical–quantum techniques are being applied to address such problems. We will discuss key challenges, including noise, scalability, and resource constraints, and how these impact the realization of quantum advantage on near-term hardware.

Through selected case studies and experiences from ongoing research at TCS, the session will highlight where quantum methods show promise today, where classical methods continue to dominate, and what it will take to bridge this gap. The talk will conclude with an outlook on the directions researchers and practitioners should focus on to make quantum computing relevant for real-world applications.