Learning Objectives
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Understand and explain the fundamental principles of quantum computing, including superposition, entanglement, and quantum gates.
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Apply core Python programming skills to build and run quantum circuits using the Qiskit framework.
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Use Q, an AI-powered coding assistant, to accelerate quantum software development and enhance learning efficiency.
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Develop, simulate, and debug quantum algorithms in a practical coding environment using Visual Studio Code and GitHub.
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Identify the role of quantum computing in the broader deep tech ecosystem and assess its potential impact on real-world applications.
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Collaborate with peers on open-source projects and contribute to reproducible, future-facing codebases.
Learning Community to help ambitious individuals grow in the space of deep tech
The AI-SEQ Learning Program has a duration of 10-12 weeks depending on the group's speed of learning and the final projects.
Starting Date: 1st of May 2025
End Date: 7th/14th / 21st of July 2025
01
Learning Module 1 - Software Engineering Essentials
As a learner you will get to apply your skills in a final project, developing and
presenting a working quantum application that demonstrates their readiness for the deep tech space.
02
Learning Module 2 - AI-assisted Software Engineering
Introduces Q by QuLearnLabs, an open-source AI coding assistant developed by QuLearnLabs. Learners explore how to integrate Q into their development workflow to accelerate coding and speed up the learning process.
03
Learning Module 3 - Python Programming for Beginners & Refresher
Provides a review of Python essentials relevant to quantum
development - variables, functions, control structures, data types, libraries, and scripting in Jupyter Notebooks.
04
Learning Module 4 - Introduction to Quantum Computing
Explores the motivation behind quantum computing, the limitations of classical systems, and the rise of quantum processing units (QPUs). You will also get to
examine the broader deep tech ecosystem in the quantum and advanced computing space.
05
Learning Module 5 - Quantum Information and Qubits
Covers the principles of quantum information science, including qubits, superposition, entanglement, Dirac notation, and the mathematics of multi-qubit systems.
06
Learning Module 6 - Quantum Circuits and Algorithms with Qiskit
Focuses on building and simulating quantum circuits using Qiskit. Topics include basic quantum gates, entanglement, and algorithms such as Grover’s search.
07
Learning Module 7 - Post-Quantum Cryptography (PQC)
Introduces quantum-safe cryptographic techniques, including lattice- and hash-based approaches, and discusses their importance for future digital security.
08
Learning Module 8 - Capstone Project
As a learner you will get to apply your skills in a final project, developing and
presenting a working quantum application that demonstrates your job readiness for the deep tech space.