Quantum Transformer: Accelerating model inference via quantum linear algebra
PositiveArtificial Intelligence
A recent study explores how quantum computing can enhance the efficiency of transformer architectures used in large language models. By developing quantum subroutines for key components like self-attention and normalization, researchers aim to accelerate model inference significantly. This advancement is crucial as it could lead to faster and more powerful AI systems, making them more accessible and effective in various applications.
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