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Explore the Emerging Field of Quantum Natural Language Processing (QNLP) with lambeq QNLP Toolkit

What you will learn

 

Learn the fundamentals of Quantum Machine Learning (QML)

 

Get the basics of Diagrammatic Quantum Theory

 

Explore the topic of Quantum Natural Language Processing (QNLP)

 

Learn about the Distributional Compositional Categorical (DisCoCat) QNLP algorithm

 

Explore and learn the usage of lambeq : World’s first High-level QNLP Toolkit

 

Gain familiarity with potential applications of QNLP and its future research directions

Description

Quantum Natural Language Processing (QNLP) is an emerging field which is at an intersection of Categorical Quantum Mechanics (CQM) and Computational Linguistics. This is one of those unique field which combines Quantum Computing with Natural Language Processing to take advantage of the properties which Quantum Computing paradigm provides. QNLP is quantum-native which means that the language structure wants to run itself on a quantum computer rather than a classical computer because a natural model of language is equivalent to a natural model utilized to describe quantum mechanical phenomena!

 

The only prominent company which is working in the field of QNLP is Quantinuum (formerly Cambridge Quantum) and has achieved major milestones in the field of QNLP. They were the first to display the true potential of running language on real quantum hardware such as the IBM quantum hardware. They have released the world’s first high-level Python based QNLP toolkit called lambeq which is able to convert any diagram (representing the language structure) into a quantum circuit that helps to run the language on a quantum hardware and simulator.

 

This is a short course on Quantum Natural Language Processing giving the primary foundations which will help to get started with QNLP and explore its practical applications using the lambeq QNLP toolkit. The course does not provides the mathematical foundations i.e. category theory but rather touches on the diagrammatic quantum theory which is used entirely to build an algorithm (again pictorial) called DisCoCat (Distributional Compositional Categorical).


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The course has been divided into the following parts which has a coherent structure to help you navigate according to your requirements:

 

  • Part 1 – Brief Introduction to Quantum Computing

  • Part 2 – Basics of Quantum Machine Learning

  • Part 3 – Diagrammatic Quantum Theory

  • Part 4 – Quantum Natural Language Processing

 

I am very confident that the field of QNLP is developing rapidly and it will take advantage of the quantum computers which we have today just like other applications of quantum computing are taking advantage. The pictorial nature of QNLP concepts is going to attract many to do more research on this unique and amazing field!

 

English
language

Content

Welcome to the course

Welcome lecture

—-Part 1 Brief Introduction to Quantum Computing—-

Part 1 Brief Introduction to Quantum Computing

Brief Introduction to Quantum Computing

Welcome to Part 1 Brief Introduction to Quantum Computing
Introduction to Quantum Computing
Properties of Quantum Computing
Single Qubit Quantum Gates
Multi Qubit Quantum Gates
ZX Calculus Representation of Quantum Gates
Brief Introduction to Quantum Computing Notes

—-Part 2 Basics of Quantum Machine Learning—-

Part 2 Basics of Quantum Machine Learning

Basics of Quantum Machine Learning

Welcome to Part 2 Basics of Quantum Machine Learning (QML)
Introduction to Machine Learning
Neural Network Basics
Quantum Machine Learning (QML) – Variational Circuits & QML Architecture
Quantum Neural Networks Briefly
Basics of Quantum Machine Learning Notes

—-Part 3 Diagrammatic Quantum Theory—-

Part 3 Diagrammatic Quantum Theory

Diagrammatic Quantum Theory

Welcome to Part 3 Diagrammatic Quantum Theory
Process Theory – Boxes & Wires
States, Effects & Scalars- Kets, Bras & Numbers
Circuit Diagrams – Parallel & Sequential Composition
String Diagrams – Cups & Caps
Diagrammatic Quantum Theory Notes

—-Part 4 Quantum Natural Language Processing—-

Part 4 Quantum Natural Language Processing

Quantum Natural Language Processing (QNLP)

Welcome to Part 4 Quantum Natural Language Processing (QNLP)
Introduction to Quantum Natural Language Processing
Distributional Word Representation
Compositionality of Grammar
QNLP Basics – Adjective & Noun
Subject Verb Object Sentence
DisCoCat Algorithm
String Diagram to ZX Quantum Circuit
Introducing lambeq & it’s Features
QNLP Training Process
Sentence Classification Code Tutorial – Classical Pipeline
Sentence Classification Code Tutorial – Quantum Pipeline
Sentence Classification Code – Classical and Quantum ZIP File
Potential Applications of QNLP
Future Directions for Research in QNLP
References and Thank you Lecture
Quantum Natural Language Processing Notes

Bonus Lecture

BONUS LECTURE

 

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