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With careful manipulation, malicious agents can reverse engineer privateinformation encoded in pre-trained language models. Security concerns motivatethe development of quantum pre-training. In this work, we propose a highlyPortable Quantum Language Model (PQLM) that can easily transmit information todownstream tasks on classical machines. The framework consists of a cloud PQLMbuilt with random Variational Quantum Classifiers (VQC) and local models fordownstream applications. We demonstrate the ad hoc portability of the quantummodel by extracting only the word embeddings and effectively applying them todownstream tasks on classical machines. Our PQLM exhibits comparableperformance to its classical counterpart on both intrinsic evaluation (loss,perplexity) and extrinsic evaluation (multilingual sentiment analysis accuracy)metrics. We also perform ablation studies on the factors affecting PQLMperformance to analyze model stability. Our work establishes a theoreticalfoundation for a portable quantum pre-trained language model that could betrained on private data and made available for public use with privacyprotection guarantees.

PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protection