Vectorized Attention with Learnable Encoding for Quantum Transformer

• URL: http://arxiv.org/abs/2508.18464v2
• Date: Wed, 03 Sep 2025 22:25:32 GMT
• Title: Vectorized Attention with Learnable Encoding for Quantum Transformer
• Authors: Ziqing Guo, Ziwen Pan, Alex Khan, Jan Balewski,
• Abstract summary: We propose the Vectorized Quantum Transformer (VQT), a model that supports ideal masked attention matrix computation.<n>Our noise intermediate-scale quantum friendly VQT approach unlocks a novel architecture for end-to-end machine learning in quantum computing.
• Score: 0.6766416093990318
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vectorized quantum block encoding provides a way to embed classical data into Hilbert space, offering a pathway for quantum models, such as Quantum Transformers (QT), that replace classical self-attention with quantum circuit simulations to operate more efficiently. Current QTs rely on deep parameterized quantum circuits (PQCs), rendering them vulnerable to QPU noise, and thus hindering their practical performance. In this paper, we propose the Vectorized Quantum Transformer (VQT), a model that supports ideal masked attention matrix computation through quantum approximation simulation and efficient training via vectorized nonlinear quantum encoder, yielding shot-efficient and gradient-free quantum circuit simulation (QCS) and reduced classical sampling overhead. In addition, we demonstrate an accuracy comparison for IBM and IonQ in quantum circuit simulation and competitive results in benchmarking natural language processing tasks on IBM state-of-the-art and high-fidelity Kingston QPU. Our noise intermediate-scale quantum friendly VQT approach unlocks a novel architecture for end-to-end machine learning in quantum computing.

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