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Updated in [September 05th, 2023]

Skills and Knowledge Acquisition:
1. Foundations of Quantum Computing: Gain insights into the fundamentals of quantum computing, providing a strong foundation for understanding QNLP.
2. Quantum Machine Learning Basics: Explore the basics of quantum machine learning, a key component of QNLP.
3. Diagrammatic Quantum Theory: Familiarize yourself with diagrammatic quantum theory, a pictorial approach used in QNLP.
4. Practical Applications: Discover how to apply QNLP principles in real-world scenarios using the lambeq QNLP toolkit.
5. DisCoCat Algorithm: Learn about the DisCoCat (Distributional Compositional Categorical) algorithm, a pivotal element in QNLP.
6. Quantinuum's Milestones: Understand the significant achievements of Quantinuum (formerly Cambridge Quantum), a leading company in the QNLP field.

Contribution to Professional Growth:
- Cutting-Edge Expertise: Acquire knowledge and skills in a rapidly evolving field, positioning yourself as an expert in Quantum Natural Language Processing.
- Interdisciplinary Proficiency: Combine insights from quantum computing, machine learning, and linguistics, enhancing your interdisciplinary capabilities.
- Practical Applications: Learn how to practically apply QNLP principles using the lambeq toolkit, opening doors to various industries.
- Innovation Potential: Be at the forefront of innovation in quantum computing by exploring the unique and promising field of QNLP.
- Alignment with Quantum Trends: Align your professional growth with the growing trend of quantum computing applications in diverse sectors.
- Quantum Computing Skills: Develop a valuable skill set in quantum computing, a transformative technology with numerous applications.

Suitability for Preparing Further Education:
- Academic Pursuits: Ideal for individuals considering further academic studies or research in quantum computing, linguistics, or interdisciplinary fields.
- Quantum Computing Research: Prepare for advanced research or studies in quantum computing, particularly in the context of natural language processing.
- Quantum Technology Careers: Suitable for those aspiring to careers in quantum technology, quantum software development, or quantum computing research.
- Data Science and NLP: Complement your knowledge in data science and natural language processing with quantum-based approaches.
- Advanced Quantum Topics: Lay the groundwork for advanced studies in quantum computing, quantum algorithms, and quantum information theory.

Course Syllabus

Welcome to the course

----Part 1 Brief Introduction to Quantum Computing----

Brief Introduction to Quantum Computing

----Part 2 Basics of Quantum Machine Learning----

Basics of Quantum Machine Learning

----Part 3 Diagrammatic Quantum Theory----

Diagrammatic Quantum Theory

----Part 4 Quantum Natural Language Processing----

Quantum Natural Language Processing (QNLP)

Bonus Lecture