QuCoWE Quantum Contrastive Word Embeddings with Variational Circuits for NearTerm Quantum Devices

TL;DR

QuCoWE introduces a quantum framework for word embeddings using shallow parameterized circuits, achieving competitive performance on NLP benchmarks with fewer parameters, and includes noise mitigation techniques for near-term quantum devices.

Contribution

It presents a novel quantum contrastive word embedding method with a regularizer for trainability and noise mitigation strategies, advancing quantum NLP applications.

Findings

Competitive performance on NLP benchmarks with fewer parameters.

Effective noise mitigation techniques demonstrated in simulations.

Framework suitable for near-term quantum hardware.

Abstract

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 regularizer based on singlequbit purity that mitigates barren plateaus On Text8 and WikiText2 QuCoWE attains competitive intrinsic WordSim353 SimLex999 and extrinsic SST2 TREC6 performance versus 50100d classical baselines while using fewer learned parameters per token All experiments are run in classical simulation we analyze depolarizingreadout noise and include errormitigation hooks zeronoise extrapolation randomized compiling to facilitate hardware deployment