Hybrid quantum-classical computing is a computational approach where quantum processors (QPUs) and classical computers work together quantum units handle tasks benefiting from quantum mechanics (e.g. optimization, simulation, sampling), while classical systems manage data preprocessing, orchestration, post-processing, and integration.
Because hybrid approaches offer a practical path to leveraging quantum advantages today enabling businesses to solve complex optimization or simulation problems that classical systems struggle with, and preparing them for future quantum-ready operations as technology matures.
Problems involving complex optimization (logistics, scheduling, resource allocation), large-scale simulation (chemistry, materials, financial modeling), combinatorial complexity, machine learning acceleration, and other compute-intensive tasks where quantum parallelism or entanglement can offer advantages.
Hybrid quantum-classical solutions are increasingly practical today: many providers offer cloud-accessible quantum resources, and hybrid algorithms enable useful experiments and pilot projects even if large-scale, fault-tolerant quantum machines are still under development.
Hybrid quantum-classical solutions are increasingly practical today: many providers offer cloud-accessible quantum resources, and hybrid algorithms enable useful experiments and pilot projects even if large-scale, fault-tolerant quantum machines are still under development.
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