Description: Hybrid quantum-classical computing is a computing model that integrates the capabilities of quantum computing with classical computing to tackle complex problems more efficiently. This approach allows leveraging the advantages of both worlds: the speed and processing power of quantum computing, alongside the stability and maturity of classical computing. In this model, tasks are divided between the two types of computing, where operations that benefit from quantum superposition and entanglement are performed on a quantum processor, while more conventional tasks are handled on classical systems. This synergy not only optimizes performance but also facilitates the resolution of problems that are intractable for classical computers alone. Hybrid quantum-classical computing is becoming an active area of research, with the potential to revolutionize fields such as artificial intelligence, materials simulation, and complex system optimization, by enabling scientists and developers to use the best of both worlds to innovate and solve technological challenges.
History: The concept of hybrid quantum-classical computing began to take shape in the early 2000s when researchers started exploring how quantum computers could complement classical computers. In 2001, Grover’s algorithm and Shor’s algorithm demonstrated that certain problems could be solved more quickly with quantum computing. As quantum technology advanced, it became clear that a combination of quantum and classical computing could be more effective for solving complex problems. In 2019, various companies began developing platforms that allow the integration of both types of computing, marking a milestone in the evolution of this field.
Uses: Hybrid quantum-classical computing is used in various applications, including optimization of complex problems, simulation of quantum systems, and development of artificial intelligence algorithms. In the field of scientific research, it is applied to model molecules and materials at the quantum level, which can accelerate the discovery of new drugs. It is also used in enhancing machine learning algorithms, where quantum computers can process large volumes of data more efficiently than classical ones.
Examples: An example of hybrid quantum-classical computing is the use of various platforms that allow researchers to run quantum algorithms on quantum processors while using classical computers for pre- and post-processing tasks. Another case is the work on optimizing logistics problems, where quantum algorithms are combined with classical techniques to improve efficiency in resource distribution.
