Quantum linear algebra is all you need for Transformer architectures
Naixu Guo Zhan Yu Aman Agrawal Patrick Rebentrost
Generative machine learning methods such as large-language models are
revolutionizing the creation of text and images. While these models are
powerful they also harness a large amount of computational resources. The
transformer is a key component in large language models that aims to generate a
suitable completion of a given partial sequence. In this work, we investigate
transformer architectures under the lens of fault-tolerant quantum computing.
The input model is one where pre-trained weight matrices are given as block
encodings to construct the query, key, and value matrices for the transformer.
As a first step, we show how to prepare a block encoding of the self-attention
matrix, with a row-wise application of the softmax function using the Hadamard
product. In addition, we combine quantum subroutines to construct important
building blocks in the transformer, the residual connection, layer
normalization, and the feed-forward neural network. Our subroutines prepare an
amplitude encoding of the transformer output, which can be measured to obtain a
prediction. We discuss the potential and challenges for obtaining a quantum
advantage.