用可向量化编码的量子注意力，实现高效低噪的量子变压器推理
Vectorized Attention with Learnable Encoding for Quantum Transformer
- 采用向量化非线性量子编码器，支持理想掩码注意力计算
- 在IBM和IonQ上实现低采样开销与无梯度量子电路模拟
- 适合追求量子优势的端到端机器学习研究者
向量化量子块编码为将经典数据嵌入希尔伯特空间提供了途径，使量子变压器（QT）可通过量子电路模拟替代经典自注意力机制，从而更高效运行。现有QT依赖深度参数化量子电路（PQC），易受量子处理器噪声影响，限制实际性能。本文提出向量化量子变压器（VQT），通过量子近似模拟实现理想掩码注意力矩阵计算，并借助向量化非线性量子编码器实现高效训练，获得低采样开销、无梯度的量子电路模拟（QCS）与减少的经典采样开销。此外，我们在IBM和IonQ上对比了量子电路模拟的精度，在IBM最新高保真Kingston QPU上实现了自然语言处理任务的竞争力表现。所提出的噪声中等规模量子友好型VQT架构，为量子计算中的端到端机器学习开辟了新路径。
原文摘要 · Abstract (English)
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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