Quantum Computing Development: Laboratory and Industry Projects (Classroom, Synchronous & Asynchronous)
About This Course
This hands-on lab course immerses participants in the complete lifecycle of quantum software development through a large-scale, industry-aligned project. Participants will design, implement, and validate a functional prototype that addresses real-world challenges using quantum technologies.
The course emphasises how quantum computing can complement advanced digital systems and intelligent automation, enabling breakthroughs in areas such as optimisation, predictive analytics, and secure data processing. These capabilities are increasingly critical for sectors adopting interconnected technologies and data-driven operations.
Participants will develop competencies such as computational thinking, problem-solving, and technology integration. By working on authentic projects with academic and industry mentors, participants will experience collaborative workflows that mirror modern development environments, fostering adaptability and innovation, key attributes for thriving in digitally transformed ecosystems.
What You'll Learn
• Design and implement quantum circuits and algorithms using platforms such as Qiskit or Cirq, applying them to scenarios like resource optimisation, secure communication, and advanced analytics.
• Apply debugging and verification techniques through unit tests, simulators, and formal checks to ensure correctness, reinforcing rigorous development practices essential for complex systems.
• Analyse and interpret quantum outputs using systematic evaluation of measurement results and validation metrics, strengthening data literacy and analytical reasoning for high-impact decision-making.
Entry Requirements
• Applicants are required to have completed Quantum Foundations and Algorithms (Classroom, Synchronous & Asynchronous) [TGS-2026061385].
• A bachelor’s degree (or equivalent qualification) in Computer Science, Engineering, Physics, Mathematics, or a closely related discipline.
• Familiarity with linear algebra (vectors, matrices, eigenvalues), probability, and basic complex numbers.
• Basic programming experience in Python or another high-level language.
• For applicants without a strong quantitative background, evidence of equivalent industry experience in data science, software engineering, or related technical domains may be considered.