Quantum Classical

Quantum-classical hybrid computing integrates classical and quantum computational methods to address complex problems intractable for either alone. Current research focuses on developing and applying hybrid algorithms across diverse fields, including machine learning (using quantum neural networks, variational quantum eigensolvers, and quantum kernel methods), optimization (leveraging quantum annealing and variational approaches), and specific applications like satellite network management and drug discovery. This interdisciplinary field is significant because it allows researchers to explore the potential of quantum computing for real-world problems while mitigating limitations of current quantum hardware, paving the way for practical quantum advantages in various scientific and industrial domains.