Quantum Computing and Decision Making — Part 2 of “Quantum Approaches for Decision Making”
Program-level details: See program/curriculum.md
Combined 2026-06-01: This course is Part 2 of the combined 8-week, 4-credit course “Quantum Approaches for Decision Making” (Part 1 = Quantum Cognition, Nathan Yang). Decision: the two files remain as separate part-files (confirmed 2026-08-04 by Maria Rodas — see DECISIONS.md “Quantum course files: keep as two separate part-files”).
Website/Catalog Description (2026-06-10): The combined-course description is in quantum_cognition.md § Website/Catalog Description. This Part 2 file covers the Quantum Computing half (Weeks 5-8, Abhijeet Ghoshal).
| Credits: 4 (combined course; this part ≈ Weeks 5-8) | Term: Spring 2027 (Weeks 5-8, from Feb 15) | Instructor: Abhijeet Ghoshal (abhi@illinois.edu) |
Syllabus review (2026-09-13): Revised syllabus submitted by Abhijeet Ghoshal. AIAS levels now declared (Gap 1 resolved); Canvas Discussion added at 5% (Gap 4 partial). Gaps 2 (exam weight 40% exceeds ICA ceiling 35%), 3 (oral component 10%), and teammate-evaluation portion of Gap 4 remain open — see flagged items below.
Course Description
This course provides a comprehensive foundation in quantum computing by bridging essential mathematical theory with practical, industry-relevant applications. The curriculum begins with an introduction to the postulates of quantum mechanics and the linear algebra required to understand qubits, superposition, and entanglement. Students gain proficiency in constructing quantum circuits using both single and multi-qubit gates. The course then transitions into pure quantum algorithms, including the Deutsch-Jozsa, Grover’s search, and Shor’s algorithms.
A significant portion is dedicated to quantum optimization, where students learn to reformulate complex combinatorial problems — such as the Traveling Salesman Problem and portfolio management — into Quadratic Unconstrained Binary Optimization (QUBO) models for solution via the Quantum Approximate Optimization Algorithm (QAOA). Further exploration covers variational quantum algorithms, including the Variational Quantum Eigensolver (VQE) for chemistry, and its role in quantum machine learning and reinforcement learning. The course concludes by examining current hardware technologies — from superconducting qubits to trapped ions — and the strategic commercial landscape of quantum technology, including the transition toward post-quantum cryptography.
This course is Part 2 of Quantum Approaches for Decision Making (Spring 2027, Weeks 5-8), running four weeks with two 90-minute sessions per week (one live, one lab) plus one asynchronous online session each week. Part 1 is Quantum Cognition (Weeks 1-4, Nathan Yang).
Student effort: ~10 hours/week outside of sessions (within MSBAi 8-12 hr target).
Prerequisites
Students should have working familiarity with linear algebra (linear equations, matrix operations, eigenvalues), trigonometric functions, and complex numbers. Prerequisite preparation materials and an optional self-assessment will be provided (≥80% → comfortable; <50% → work through prereq materials in detail). Recommended reference: Linear Algebra and its Applications by Gilbert Strang + MIT OpenCourseWare lectures.
Python programming proficiency assumed from earlier MSBAi courses (Jupyter Notebooks).
Learning Outcomes (L-C-E Tagged)
| Learning Outcome | L-C-E |
|---|---|
| Describe qubit state vectors, the Bloch sphere, unitary evolution, and quantum measurement, distinguishing these from classical probabilistic models. | Literacy |
| Explain Deutsch-Jozsa, Grover’s search, and Shor’s algorithm and articulate the computational advantage each provides over its classical counterpart. | Literacy |
| Critically evaluate claims of quantum advantage, distinguishing theoretical scaling from demonstrated empirical results on NISQ-era hardware. | Literacy |
| Construct and analyze single- and multi-qubit circuits, verify gate identities on a simulator, and predict measurement distributions before execution. | Competency |
| Formulate combinatorial optimization problems as Mixed-Integer Linear Programs and solve them using Gurobi, interpreting optimality gaps and scaling behavior. | Competency |
| Encode constrained binary optimization problems as QUBO models by mapping variables to Ising spins and converting constraints into quadratic penalty terms. | Competency |
| Design and execute QAOA circuits at greater depths, construct cost and mixer Hamiltonians from a QUBO formulation, tune variational parameters, and benchmark solution quality against a classical solver baseline. | Expertise |
| Implement a complete solution to a real-world combinatorial decision problem through a structured individual project. | Expertise |
Updated 2026-08-17 from Abhijeet Ghoshal’s submitted syllabus.
Course Schedule
| Week | Session | Topic | Content |
|---|---|---|---|
| 1 (Wk 5) | Online (async) | Quantum Computing Basics and Postulates of QM | Qubits, notation, superposition, entanglement, unitary matrices, eigenvalues, eigenvectors |
| Live (90 min) | Hands-on coding + Case 1 | Google Colab exercises; quantum intuition labs | |
| Lab (90 min) | Project kickoff | Discussion of project topics; problem selection | |
| 2 (Wk 6) | Online (async) | Multiple Qubits & Quantum Algorithms | Multi-qubit gates, quantum circuits (gate-based model), Deutsch-Jozsa, Grover’s search |
| Live (90 min) | Hands-on coding + Case 2 | Google Colab; multi-qubit circuit construction | |
| Lab (90 min) | Project progress | Work on optimization formulation (due end of week) | |
| 3 (Wk 7) | Online (async) | Quantum Approximate Optimization Algorithm (QAOA) | QAOA basics, Max-Cut problem, solution approach |
| Live (90 min) | Hands-on coding + Case 3 | QAOA applications in business domains | |
| Lab (90 min) | Project progress | Work on QAOA plan (due end of week) | |
| 4 (Wk 8) | Online (async) | VQE and the Future | VQE, quantum annealers, hardware technologies, post-quantum cryptography, commercial landscape |
| Live (90 min) | Short presentations (first half) | Individual project presentations | |
| Lab (90 min) | Short presentations (second half) | Individual project presentations (continued) |
Live Session attendance is not required or graded.
Individual Project: Combinatorial Optimization in Practice
Students work on an individually assigned real-world combinatorial decision problem (from business, logistics, finance, or operations) across all four weeks. The problem is designed to be naturally formulated as a combinatorial optimization problem, requiring students to apply the full methodological pipeline: problem structuring → classical formulation → quantum approximation.
Deliverables:
-
Optimization formulation (due end of Week 2, 10%): Decision variables, objective function, and all relevant constraints in standard mathematical notation (MILP).
-
QAOA application plan (due end of Week 3, 10%): QUBO encoding, penalty terms, circuit design strategy, specifying the transition from the classical MILP to the QAOA formulation.
-
In-class presentation (Week 4, 10%): Problem context, formulation, quantum approach, and key findings. Delivered across the live class and lab sessions in two splits.
All deliverables are individual throughout.
Assessment
⚠ Partially resolved — awaiting full instructor alignment before final approval. See coherence review (2026-08-17) in DECISIONS.md. Items marked [NEEDS RESOLUTION] require Abhijeet/Maria/Vishal confirmation before this table is considered final.
- ✅ Gap 1 resolved (2026-09-13): AIAS levels now declared on all components.
- ⚠ Gap 4 partial (2026-09-13): Canvas Discussion added at 5%; however program commitment requires 8–12% peer engagement including teammate evaluation — 5% Canvas only does not satisfy the full requirement.
- ⚠ Gap 2 still open: Exam weights total 40%, exceeding the 4-week ICA ceiling of 35%.
- ⚠ Gap 3 still open: Individual oral component is only the 10% presentation; program commitment expects a more substantial oral component per 4-week part.
| Component | AIAS Level | Weight | Description |
|---|---|---|---|
| Weekly Assessment 1 | 1 | 10% | Online exam, end of Week 1: QM postulates, qubit fundamentals, single- and multi-qubit gates, Bloch sphere |
| Weekly Assessment 2 | 1 | 10% | Online exam, end of Week 2: multi-qubit systems, quantum circuits, Deutsch-Jozsa, Grover |
| Weekly Assessment 3 | 1 | 10% | Online exam, end of Week 3: QAOA, Max-Cut, QUBO, cost Hamiltonian construction |
| Weekly Assessment 4 | 1 | 10% | Online exam, end of Week 4: VQE, variational algorithms, hardware, commercial/post-quantum landscape |
| Class exercise submissions | 3 | 25% | Weekly coding exercises completed in live/lab sessions, submitted as .ipynb with LLM prompts; graded on correctness, interpretation quality, and predict-before-running discipline |
| Canvas discussion | 1 | 5% | Weekly discussion on one question posted on Canvas. Students respond directly to the question or build on an existing thread with a new point. Must be directly relevant to the question posted. |
| Project — QUBO formulation (Wk 2) | 3 | 10% | See project description above |
| Project — QAOA plan (Wk 3) | 3 | 10% | See project description above |
| Project — presentation (Wk 4) | 3 | 10% | In-class individual presentation |
| [NEEDS RESOLUTION] Teammate evaluation / peer engagement | — | Program commitment requires 8–12% peer engagement including a teammate evaluation component (Peerceptiv). Not yet addressed in revised syllabus. |
AI Policy (as submitted):
- Online exams: AI not permitted.
- Class exercises: AI encouraged; submit LLM prompts with each submission.
- Project: AI encouraged; student holds responsibility for correctness.
- AI Attribution Log: LLM prompts submitted with exercises (note: the program-standard AI Attribution Log as a graded component was not explicitly specified — needs alignment).
Assessment updated 2026-09-13 per revised syllabus submitted by Abhijeet Ghoshal. AIAS levels added; Canvas Discussion (5%) added; Class exercises adjusted 30%→25%. Remaining open: exam weights 40% vs ICA ceiling 35%; oral component 10%; teammate evaluation component not yet specified.
Materials and Tools
Recommended textbooks (not required):
- Quantum Information: A First Course — Asma Al-Qasimi and Daniel F. V. James (Cambridge University Press)
- Quantum Computation and Quantum Information — Michael A. Nielsen and Isaac L. Chuang
Tools: Python notebooks, VS Code or Google Colab, NumPy, Gurobi (for MILP classical baseline), access to a language model API, qBraid platform (complimentary via QUEST program — provides unified access to 25+ real quantum devices from Rigetti, AQT, IonQ, and other partners). Assignments submitted as .ipynb files in a GitHub repository. No prior quantum experience assumed.
qBraid / QUEST: This course is part of the QUEST (Quantum University Education and Support Track) inaugural cohort 2026-27. Students receive complimentary qBraid platform access including a notebook environment, curriculum-ready materials, and real quantum hardware. The course also contributes to the first IRB-approved multi-university empirical study on quantum computing pedagogy (Abhijeet Ghoshal as co-author). See DECISIONS.md “BADM 590 quantum computing component accepted into qBraid QUEST program” (2026-08-21).
Syllabus submitted 2026-08-17 by Abhijeet Ghoshal; coherence review completed 2026-08-17. Revised syllabus with AIAS levels and Canvas Discussion submitted 2026-09-13. Three gaps remain open (weights, oral component, teammate eval). Full decision record: DECISIONS.md. Page history under “what changed?”
| Course Sequence: ← Quantum Cognition (concurrent: Agentic AI) | Next: BADM 557 — Business Intelligence → |