Tag
This paper proposes a qubit-efficient quantum framework for hyperdimensional computing decomposition that reduces representation cost from O(D) to O(log D) qubits while preserving the quadratic search advantage, achieving up to 2000x fewer qubits.
This paper presents a method for HDR video generation by leveraging pretrained generative models through logarithmic encoding alignment and camera-mimicking degradation training, enabling effective HDR synthesis without architectural redesign. The approach demonstrates that HDR generation can be achieved simply by adapting existing models to a representation naturally aligned with their learned priors.