QNet: A Quantum-native Sequence Encoder Architecture

This work investigates how current quantum computers can improve the performance of natural language processing tasks. To achieve this goal, we proposed QNet, a novel sequence encoder model entirely inferences on the quantum computer using a minimum number of qubits. QNet is inspired by Transformer, the state-of-the-art neural network model based on the attention mechanism to relate the tokens. While the attention mechanism requires time complexity of $O(n^2 \cdot d)$ to perform matrix multiplication operations, QNet has merely $O(n+d)$ quantum circuit depth, where $n$ and $d$ represent the length of the sequence and the embedding size, respectively. To employ QNet on the NISQ devices, ResQNet, a quantum-classical hybrid model composed of several QNet blocks linked by residual connections, is introduced. We evaluate ResQNet on various natural language processing tasks, including text classification, rating score prediction, and named entity recognition. ResQNet exhibits a 6% to 818% performance gain on all these tasks over classical state-of-the-art models using the exact embedding dimensions. In summary, this work demonstrates the advantage of quantum computing in natural language processing tasks.