QuCoWE Quantum Contrastive Word Embeddings with Variational Circuits for NearTerm Quantum Devices
Rabimba Karanjai, Hemanth Hegadehalli Madhavarao, Lei Xu, Weidong Shi
我们向QuCoWE介绍了一个框架,通过训练浅层硬件效率的参数化量子电路来学习量子原生词嵌入,具有对比性跳过图的TPC,通过具有受控环纠缠相似的数据重新上传电路进行编码,并通过一个Logitfidelity头,该头将分数与SGNSNoiseContrastive Estimation的偏移PMI尺度对齐。Text2 QuCoWE获得具有竞争力的内在WordSim353 SimLex999和外部SST2 TREC6性能与50100d经典基线,同时使用更少的学习参数每个令牌 所有实验都在经典模拟中运行,我们分析去极化读取噪声,并包括错误缓解钩零noise外推随机编译,以促进硬件部署
We present QuCoWE a framework that learns quantumnative word embeddings by training shallow hardwareefficient parameterized quantum circuits PQCs with a contrastive skipgram objective Words are encoded by datareuploading circuits with controlled ring entanglement similarity is computed via quantum state fidelity and passed through a logitfidelity head that aligns scores with the shiftedPMI scale of SGNSNoiseContrastive Estimation To maintain trainability we introduce an entanglementbudget regula...