# Quantum Language Processing

@article{Wiebe2019QuantumLP, title={Quantum Language Processing}, author={Nathan Wiebe and Alex Bocharov and Paul Smolensky and Matthias Troyer and Krysta Marie Svore}, journal={arXiv: Quantum Physics}, year={2019} }

We present a representation for linguistic structure that we call a Fock-space representation, which allows us to embed problems in language processing into small quantum devices. We further develop a formalism for understanding both classical as well as quantum linguistic problems and phrase them both as a Harmony optimization problem that can be solved on a quantum computer which we show is related to classifying vectors using quantum Boltzmann machines. We further provide a new training…

## 22 Citations

### Foundations for Near-Term Quantum Natural Language Processing

- Computer ScienceArXiv
- 2020

The encoding of linguistic structure within quantum circuits also embodies a novel approach for establishing word-meanings that goes beyond the current standards in mainstream AI, by placing linguistic structure at the heart of Wittgenstein's meaning-is-context.

### Generative training of quantum Boltzmann machines with hidden units

- Computer ScienceArXiv
- 2019

This article provides a method for fully quantum generative training of quantum Boltzmann machines with both visible and hidden units while using quantum relative entropy as an objective, and presents two novel methods for solving this problem.

### Grammar-Aware Question-Answering on Quantum Computers

- Computer ScienceArXiv
- 2020

This work performs the first implementation of an NLP task on noisy intermediate-scale quantum (NISQ) hardware and encodes word-meanings in quantum states and explicitly account for grammatical structure, which even in mainstream NLP is not commonplace, by faithfully hard-wiring it as entangling operations.

### QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer

- Computer ScienceArXiv
- 2021

Results on the first NLP experiments conducted on Noisy Intermediate-Scale Quantum (NISQ) computers for datasets of size ≥ 100 sentences are presented.

### A hybrid classical-quantum workflow for natural language processing

- Computer ScienceMach. Learn. Sci. Technol.
- 2021

This manuscript develops a hybrid workflow for representing small and large scale corpus data sets to be encoded, processed, and decoded using a quantum circuit model and provides the results showing the efficacy of the method.

### Recent Advances for Quantum Neural Networks in Generative Learning

- Computer Science, PhysicsArXiv
- 2022

This paper interprets these QGLMs, covering quantum circuit Born machines, quantum generative adversarial networks, quantum Boltzmann machines, and quantum autoencoders, as the quantum extension of classical generative learning models, and explores their intrinsic relation and their fundamental differences.

### Machine Learning: Quantum vs Classical

- Computer ScienceIEEE Access
- 2020

An overview of quantum machine learning in the light of classical approaches is presented, discussing various technical contributions, strengths and similarities of the research work in this domain and elaborate upon the recent progress of different quantum machinelearning approaches, their complexity, and applications in various fields such as physics, chemistry and natural language processing.

### A quantum search decoder for natural language processing

- Computer ScienceQuantum Mach. Intell.
- 2021

This work constructs a quantum algorithm to find the globally optimal parse (i.e. for infinite beam width) with high constant success probability, and applies this quantum beam search decoder to Mozilla's implementation of Baidu’s DeepSpeech neural net, which is shown to exhibit such a power law word rank frequency.

### Quantum Mathematics in Artificial Intelligence

- Computer ScienceJ. Artif. Intell. Res.
- 2021

Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products, which can potentially be implemented on quantum hardware.

### Quantum Representation for Sentiment Classification

- Computer Science2022 IEEE International Conference on Quantum Computing and Engineering (QCE)
- 2022

This preliminary study investigates how sentiment can be represented correctly and efficiently for quantum natural language processing (QNLP) and presents four possible approaches for representing sentiment words in the quantum case.

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