# Quantum Transfer Learning for Acceptability Judgements

arXiv: https://arxiv.org/abs/2401.07777 · HTML: https://mlsanity.com/paper/2401.07777/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 70 references that are in the map.
- First posted on arXiv 15 Jan 2024.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies](https://mlsanity.com/paper/1611.01368/) (2016)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Targeted Syntactic Evaluation of Language Models](https://mlsanity.com/paper/1808.09031/) (2018)
- [Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog](https://mlsanity.com/paper/1810.13327/) (2018)
- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [Quantum circuit architecture search for variational quantum algorithms](https://mlsanity.com/paper/2010.10217/) (2020)
- [Foundations for Near-Term Quantum Natural Language Processing](https://mlsanity.com/paper/2012.03755/) (2020)
- [CLiMP: A Benchmark for Chinese Language Model Evaluation](https://mlsanity.com/paper/2101.11131/) (2021)
- [Quantum Self-Attention Neural Networks for Text Classification](https://mlsanity.com/paper/2205.05625/) (2022)
- [Recent advances for quantum classifiers](https://mlsanity.com/paper/2108.13421/) (2021)
- [Adversarial Domain Adaptation for Duplicate Question Detection](https://mlsanity.com/paper/1809.02255/) (2018)
- [Hybrid quantum-classical classifier based on tensor network and variational quantum circuit](https://mlsanity.com/paper/2011.14651/) (2020)
- [RuCoLA: Russian Corpus of Linguistic Acceptability](https://mlsanity.com/paper/2210.12814/) (2022)
- [Quantum Algorithms for Compositional Natural Language Processing](https://mlsanity.com/paper/1608.01406/) (2016)
- [Acceptability Judgements via Examining the Topology of Attention Maps](https://mlsanity.com/paper/2205.09630/) (2022)
- [Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus](https://mlsanity.com/paper/2109.12053/) (2021)
- [Knowledge Adaptation: Teaching to Adapt](https://mlsanity.com/paper/1702.02052/) (2017)
- [DaLAJ - a dataset for linguistic acceptability judgments for Swedish: Format, baseline, sharing](https://mlsanity.com/paper/2105.06681/) (2021)
- [What can linguistics and deep learning contribute to each other?](https://mlsanity.com/paper/1809.04179/) (2018)
- [NoCoLA: The Norwegian Corpus of Linguistic Acceptability](https://mlsanity.com/paper/2306.07790/) (2023)
- [Adapting Pre-trained Language Models for Quantum Natural Language Processing](https://mlsanity.com/paper/2302.13812/) (2023)

## Built on by (most cited first, 2 of 2)

- [Hybrid Classical-Quantum Transfer Learning with Noisy Quantum Circuits](https://mlsanity.com/paper/2603.16973/) (2026)
- [On the Transfer of Knowledge in Quantum Algorithms](https://mlsanity.com/paper/2501.14120/) (2025)

## Abstract (arXiv)

Hybrid quantum-classical classifiers promise to positively impact critical aspects of natural language processing tasks, particularly classification-related ones. Among the possibilities currently investigated, quantum transfer learning, i.e., using a quantum circuit for fine-tuning pre-trained classical models for a specific task, is attracting significant attention as a potential platform for proving quantum advantage. This work shows potential advantages, both in terms of performance and expressiveness, of quantum transfer learning algorithms trained on embedding vectors extracted from a large language model to perform classification on a classical Linguistics task: acceptability judgments. Acceptability judgment is the ability to determine whether a sentence is considered natural and well-formed by a native speaker. The approach has been tested on sentences extracted from ItaCoLa, a corpus that collects Italian sentences labeled with their acceptability judgment. The evaluation phase shows results for the quantum transfer learning pipeline comparable to state-of-the-art classical transfer learning algorithms, proving current quantum computers' capabilities to tackle NLP tasks for ready-to-use applications. Furthermore, a qualitative linguistic analysis, aided by explainable AI methods, reveals the capabilities of quantum transfer learning algorithms to correctly classify complex and more structured sentences, compared to their classical counterpart. This finding sets the ground for a quantifiable quantum advantage in NLP in the near future.