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ionq.com
article
https://www.ionq.com/resources/quantum-natural-language-processing-with-ionq-…
# Quantum Natural Language Processing With IonQ Hardware. Quantum Natural Language Processing With IonQ Hardware. Quantum Natural Language Processing With IonQ Hardware. IonQ recently participated in the second Quantum Natural Language Processing conference in Oxford. At the previous QNLP conference in 2019, the process of running programs on real quantum computers was barely getting started. Back then, even the most established quantum natural language processing (NLP) research initiative had yet to announce successful implementation on quantum hardware, a feat that was accomplished in 2020. New results from quantum computers in AI are being published almost every month, with applications including probabilistic reasoning, financial modeling, and image classification. The key thing being demonstrated here is that the common mathematical language of vectors enables us to produce quantum implementations for standard AI techniques. The key message here is that real quantum computers are performing examples of language processing tasks.
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nlp-lab.org
article
https://nlp-lab.org/quantumnlp/
# NLP Lab. The Natural Language Processing Lab and Quantum AI Study Group. * Cavar, D., Koushik Reddy Parukola, James Graves, Shane Sparks (2025) Old Wine in New Bottles: Using Classical Word Embeddings in Gate-Based Quantum NLP Systems. Paper to be presented at the Quantum AI and NLP Conference 2025, Indiana University, August 2025. * Damir Cavar, Koushik Reddy Parukola (2025) Word and Text Similarity Using Classical Word Embeddings in Quantum NLP Systems. * Cavar, D., James Bryan Graves, Shane Sparks, Koushik Reddy Parukola (2025) Hybrid Classical Quantum Embeddings for NLP and AI using Hamiltonians. Paper to be presented at the Quantum AI and NLP Conference 2025, Indiana University, August 2025. * Cavar, D., Koushik Reddy Parukola, Shane Sparks (2025) Old Wine in New Bottles: Using Classical Word Embeddings in Quantum NLP Systems. * Damir Cavar and Chi Zhang (2024) *Semantic Similarities using Classical Embeddings in Quantum NLP.* Paper and Poster presented at the IEEE Quantum Week 2024, Montreal, Canada, September 2024.
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arxiv.org
article
https://arxiv.org/html/2504.09909v2
6. [5 SURVEY RESULTS](https://arxiv.org/html/2504.09909v2#S5 "In Quantum Natural Language Processing: A Comprehensive Review of Models, Methods, and Applications"). (Widdows et al., [2024](https://arxiv.org/html/2504.09909v2#bib.bib132)) surveys quantum natural language processing, scrutinising natural language processing techniques such as word embeddings, sequential models, syntactic parsing, and attention mechanisms within transformer architectures, while proposing an innovative quantum-based text encoding paradigm. Quantum measurement-Based Encoding : A theoretical framework inspired by quantum computing (Álvaro Francisco Huertas-Rosero et al., [2008](https://arxiv.org/html/2504.09909v2#bib.bib154)) models textual documents such that the lexical measurements of text are analogous to the physical measurements performed in quantum states. Another DisCoCat model-based encoding technique (Du et al., [2022](https://arxiv.org/html/2504.09909v2#bib.bib46)) encodes textual information into quantum states by representing sentence meanings as vectors within quantum computational systems. Quantum Semantic and Tensor Based Encoding: A combination of tensor operations with the semantic distribution classification model (Han et al., [2023](https://arxiv.org/html/2504.09909v2#bib.bib59)) for Quantum Natural Language Processing (QNLP), which allows efficient coding of semantic spaces for the effective extraction of text structures.
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sciencedirect.com
article
https://www.sciencedirect.com/science/article/abs/pii/S0378437123006787
Researchers are trying to take advantage of quantum machine learning speedup in natural language processing applications.
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frontiersin.org
research
https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.…
Particularly in light of the quantum advantage in processing massive amounts of data (Kumar et al., 2024), the computational models and algorithms utilized in bioinformatics to manage huge datasets may be advantageous for QNLP. Indeed, quantum kernel methods have been applied in other bioinformatics applications, including quantum machine learning for genomics data (Abbas, 2024), quantum kernel clustering for protein sequences (Sarkar, 2018), and quantum support vector machines for gene expression analysis (Ghosh et al., 2024). The process includes data pre-processing where protein sequences are retrieved from databases such as PDB and converted into quantum states and includes QNLP techniques such as quantum language models for sequence analysis, quantum kernel methods for structural similarities, model training using experimental datasets such as cryo-EM and X-ray crystallography results in predicted protein structure as the output. Figure 15 shows the hybrid approach combines classical NLP methods with quantum computing capabilities to potentially improve natural language processing tasks by leveraging quantum parallelism, quantum embedding spaces, or quantum algorithms for sequence alignment, a critical task in bioinformatics.
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meegle.com
article
https://www.meegle.com/en_us/topics/natural-language-processing/natural-langu…
Which tools are best for natural language processing for quantum computing? Top tools include IBM Qiskit, Google Cirq, and Cambridge Quantum Computing's lambeq.
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youtube.com
video
https://www.youtube.com/watch?v=Wc1o_dGAL4Q
... quantum computing, and emerging technologies, consider ... Quantum Natural Language Processing Explained | Future of AI & Quantum Computing.
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huckiyang.github.io
article
https://huckiyang.github.io/quantum-ml-main/
This tutorial will provide an overview of the fundamentals of quantum mechanics, quantum machine learning and quantum neural networks.