Quantum Computing for Optimization (BADM 5XX or MSBAi510Q)
Program-level details: See program/CURRICULUM.md
| Credits: 2 | Term: Spring 2027 (Weeks 9-12, Spring 2 half) | Instructor: Abhijeet |
Course Vision
“Quantum Computing: Applications for Decision Making” prepares business analytics students to understand and apply quantum computing to optimization and decision problems. This course emphasizes practical skills using Python/Qiskit simulators, focusing on when and how quantum methods improve business decisions. Students will build foundational quantum computing literacy while developing hands-on skills to identify quantum opportunities in their domains.
Why This Course Matters: Companies like Chase, Goldman Sachs, pharmaceutical firms, Shell, and Walmart are already assessing quantum integration. The biggest obstacle is the lack of business graduates who understand how to convert traditional decision problems into quantum-solvable problems. This course addresses that gap.
Prerequisites
Required Knowledge (Pre-Course Self-Study):
- Basic linear algebra (vectors, matrices, eigenvalues, eigenvectors)
- Trigonometry (sine, cosine, tangent, identities)
- Complex numbers (x + iy, De-Moivre’s theorem)
- Python programming basics (Jupyter Notebooks)
Pre-Course Assessment: Students take a diagnostic quiz to verify comfort level with prerequisites.
Learning Outcomes (L-C-E Framework)
Literacy:
- L1: Explain quantum computing fundamentals (qubits, superposition, entanglement, unitary matrices) in business contexts
- L2: Understand quantum algorithms (Deutsch-Jozsa, Grover, Shor, QAOA, VQE) and their applications
- L3: Distinguish quantum advantage vs. hype for real-world business problems
- L4: Recognize when quantum computing can outperform classical methods
Competency:
- C1: Program quantum algorithms using IBM Qiskit in Python/Jupyter
- C2: Implement quantum optimization algorithms (QAOA, VQE) for business problems
- C3: Run quantum simulations and interpret results
- C4: Compare quantum vs. classical solution performance
- C5: Use quantum circuit diagrams to design algorithms
Expertise:
- E1: Identify business optimization problems suitable for quantum approaches
- E2: Design quantum-classical hybrid solutions for real decision problems
- E3: Evaluate quantum hardware readiness and timeline to production
- E4: Build business cases for quantum computing investments
- E5: Communicate quantum opportunities to non-technical stakeholders
Week-by-Week Breakdown
| Week | Topic | Learning Activities | Hands-On Labs | Deliverables |
|---|---|---|---|---|
| 1 | Quantum Foundations + Algorithms | Qubits, superposition, entanglement, unitary matrices, eigenvalues; Postulates of QM; Quantum circuits; Deutsch, Deutsch-Jozsa, Grover, Shor algorithms | Lab 1: Qiskit setup, run first quantum program; Lab 2: Implement basic algorithms | Project Milestone 1: Problem identification + classical baseline |
| 2 | Optimization with Quantum | Simple optimization problems; QAOA fundamentals; Max-cut problem; Applications in business domains | Lab 3: QAOA for Max-cut; Lab 4: Apply QAOA to business problem | Project Milestone 2: Quantum solution design + feasibility analysis |
| 3 | VQE + Advanced Applications | Variational Quantum Eigensolver (VQE); Applications in finance, supply chain, scheduling; Quantum hardware landscape | Lab 5: VQE implementation; Lab 6: Run simulations + performance comparison | Project Milestone 3: Implementation + quantum vs. classical comparison |
| 4 | Quantum Readiness + Business Cases | Quantum-classical hybrid systems; Timeline to production quantum; Building business cases; Industry adoption patterns | Lab 7: Hybrid approach design; Final Project: Business case presentation | Final Deliverable: Quantum Opportunity Portfolio |
Textbooks & Resources
Required:
- Quantum Information: A First Course by Asma Al-Qasimi and Daniel F. V. James (Cambridge University Press)
Highly Recommended:
- Quantum Computation and Quantum Information by Michael A. Nielsen and Isaac L. Chuang
Other Recommended:
- Quantum Mechanics by David Griffiths (for deeper mathematical foundations)
Software:
- IBM Qiskit (Python package) - primary platform
- Access to IBM Quantum simulators (guaranteed)
- Limited access to IBM Quantum hardware (not guaranteed, but available for exploration)
Assessment & Projects
Project-Based Assessment (No Exams):
Continuous Labs (40% of grade):
- 7 weekly hands-on labs using Qiskit
- Each lab: Implement quantum algorithms, run simulations, document findings
- Submit as Jupyter Notebooks with code + business interpretation
- Graded on: correctness, code quality, business insight
Final Project: Quantum Opportunity Portfolio (60% of grade):
Task: Identify a business optimization problem from your domain (or assigned domain) and develop a quantum computing solution strategy.
Deliverables:
- Problem Definition (Week 1):
- Business context + classical solution approach
- Complexity analysis + computational bottlenecks
- Why quantum might help
- Quantum Solution Design (Week 2):
- Quantum algorithm selection (QAOA, VQE, or hybrid)
- Circuit design + implementation plan
- Feasibility analysis
- Implementation + Comparison (Week 3):
- Qiskit implementation (simulator)
- Quantum vs. classical performance comparison
- Results visualization + interpretation
- GitHub repo with documented code
- Business Case (Week 4):
- When quantum becomes cost-effective (timeline)
- Hardware requirements + vendor options
- ROI analysis + risk assessment
- Executive summary (2-page memo)
- 10-minute video presentation
Rubric (5 dimensions):
| Dimension | Excellent (A) | Proficient (B) | Developing (C) |
|---|---|---|---|
| Technical Accuracy | Quantum implementation correct, well-documented | Implementation functional with minor issues | Significant technical errors |
| Business Insight | Clear ROI, realistic timeline, actionable recommendations | Business case present but lacks depth | Weak business justification |
| Code Quality | Clean, well-commented Qiskit code in Jupyter Notebooks | Functional code, adequate documentation | Code difficult to understand or run |
| Quantum vs. Classical Comparison | Rigorous performance analysis with clear metrics | Basic comparison with some metrics | Incomplete or unfair comparison |
| Communication | Executive summary is clear, concise, persuasive | Adequate but lacks clarity or structure | Poor communication of findings |
Technology Stack
- Programming: Python 3.10+ with Jupyter Notebooks
- Quantum Framework: IBM Qiskit
- Simulators: Qiskit Aer (local), IBM Quantum simulators (cloud)
- Hardware Access: IBM Quantum (limited, not guaranteed)
- Version Control: GitHub (for project submission)
- Visualization: Matplotlib, Qiskit visualization tools
Why This Course Matters for Your Career
Quantitative Foundation:
- Quantum computing heavily uses linear algebra, which carries over to ML, statistics, and optimization
- Builds confidence in mathematical reasoning for business analytics
Rare Market Skills:
- Few business graduates understand quantum computing applications
- Demand for quantum-literate business professionals is growing faster than supply
- Companies are actively seeking talent who can bridge quantum computing and business strategy
Transferable Skills:
- Algorithm thinking (Qiskit) enhances Python/ML skills
- Optimization mindset applies to classical problems too
- Strategic thinking about emerging technologies
Course Philosophy
Hands-On First: Almost every week includes labs. You learn by doing, not just theory.
Business Context Always: Every quantum concept is tied to business applications (portfolio optimization, supply chain, scheduling, etc.).
Realistic Expectations: We balance quantum potential with honest assessment of current limitations and timelines.
Python/Jupyter Native: Aligns with MSBAi’s common computational thread across all courses.
Last Updated: February 2026