Antonio FERNÁNDEZ@uni100h84607

AmdQuant demonstrates faster optimization and better solution quality using quantum-inspired computing on AMD GPUs versus a traditional CPU solver in recorded benchmarks, with side-by-side results and downloadable evidence. AmdQuant turns structured optimization problems into concrete decisions: Examples: which radio channel a transmitter should use which team receives a task which projects to select where to place a computing job. Users can choose from 18 reproducible examples across five categories, enter supported data through guided JSON templates, or define a custom quadratic binary objective. AmdQuant supports industrial resource planning by turning allocation problems into executable plans. Its radio-channel demonstration assigns two channels to thousands of transmitters while minimizing modeled interference. Additional models address workload balancing and computing-job placement. The prototype demonstrates measurable optimization-time improvements on synthetic data; validation with operational industrial datasets is the next step.

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