Practical Assignments You Can Expect in Quantum Computing Courses

 Typical Practical Assignments in Quantum Computing Courses

1. Programming Exercises (Quantum Katas & Q# Lab Work)

At the University of Washington, students undertook weekly programming problems via Quantum Katas—hands-on tasks such as:

Preparing specific quantum states.

Implementing entanglement-based protocols like teleportation or superdense coding.

Developing oracle functions for oracular algorithms like Deutsch–Jozsa and Bernstein–Vazirani.

quantum.microsoft.com

These exercises help users build algorithms against defined interfaces, fostering practical fluency in quantum programming.

2. Debugging Quantum Programs

In the same course, students received buggy Q# programs and had to identify and correct issues related to syntax, logic, and quantum-specific runtime errors—like improper measurement handling or incorrect use of controlled functors.

quantum.microsoft.com

3. Cloud-Based Quantum Experimentation

Students also ran programs on Azure Quantum cloud resources, leveraging both simulators and real quantum hardware (e.g., IonQ). They explored concepts like:

Random-number generation outputs from simulator vs. physical device.

Effects of noise in Bell-state measurements and Grover's algorithm as problem scale increased.

quantum.microsoft.com

4. Capstone Projects / Final Projects

Courses often culminate in team-based projects where students:

Select a quantum problem (e.g., Grover’s search, quantum chemistry, quantum crypto protocols).

Implement and evaluate solutions in Q#.

Document their work in a mini-paper and present findings to the class.

Microsoft for Developers

ar5iv

These projects build full-stack experience—from problem formulation to implementation, deployment, and presentation.

5. Algorithm Implementation & Benchmarking

Research-driven assignments include projects like:

Applying the HHL algorithm (quantum linear systems solver) to CT image reconstruction.

Comparing performance and limitations between quantum algorithms and classical analogs like Algebraic Reconstruction Technique.

arXiv

6. Hybrid Quantum-Classical Applications

Some courses require students to:

Implement classical algorithms (e.g., K-Means clustering).

Identify parts suitable for quantum acceleration and integrate quantum subroutines (e.g., quantum-augmented K-Means, quantum SVM classification).

Work on combinatorial optimization tasks such as bin-packing using QAOA or VQE.

arXiv

7. Hands-On Algorithm Labs

Educators in EECS domains have designed lab modules covering algorithms like:

Quantum key distribution, Deutsch(-Jozsa), Simon’s, and Grover’s algorithms.

Entanglement, quantum circuits, and foundational operations.

arXiv

8. Mini-Projects & Syllabus-Relevant Applications

Assignments from undergraduate syllabi commonly include:

Implementing a 16-qubit random number generator.

Tackling error correction, quantum teleportation, Fourier transforms, Shor’s algorithm, and randomized benchmarking.

Scribd

Emerging and creative application ideas also surface, like using quantum-classical transfer learning for COVID-19 detection or configuring quantum glasses for specific tasks!

Scribd

Summary: Common Assignment Types

Assignment Type Description

Quantum Programming Exercises Hands-on tasks via Quantum Katas (state prep, oracles, algorithms).

Debugging Challenges Fix buggy Q# programs with intentional syntax/runtime logic errors.

Cloud Execution Tasks Run code on Azure Quantum simulators and hardware; observe noise effects.

Final Capstone Projects Team-based implementation, reporting, and presentation.

Advanced Algorithm Case Studies Apply HHL to CT imaging, or integrate quantum solutions into classical pipelines.

Hybrid Implementation Tasks Blend classical and quantum parts (e.g., K-Means + QML, bin-packing via QAOA).

Algorithmic Lab Modules Step-by-step labs on fundamental and advanced quantum algorithms.

Mini Projects from Syllabi Classic quantum tasks like teleportation, error correction, benchmarking.

Why These Assignments Matter

Build competence in both quantum theory and programming.

Expose learners to real-world noise, hardware limitations, and debugging scenarios.

Encourage research thinking, critical evaluation, and communication skills.

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