Events

Instructors: Dr. Stefanos Kourtis (UdeS), Dr. Bilal Farooq (TMU), Dr. Shohini Ghose (WLU)

Day/Time: Friday, October 3, 10 and 17, 2025 from 03:00 PM to 06:00 PM (EDT)

Location: Virtual (Zoom Link (external link, opens in new window) )

Course Outline: 

Session 1 (October 3, 2025)

Part I: Introduction to computation

• Types of computational problems (decision, search, sampling, counting) & real-world examples
• What is computation? The algorithm (AKA Turing machine)
• Preliminaries and notation (Boolean numbers, bit strings, graph theory basics)
• Logic gates & circuits as a model of computation; examples
• Optional (depending on time): Reversible computation
• Optional (depending on time): Computation with oracles
• Optional (depending on time): Randomness in computation & examples

Part II: Introduction to quantum computation

• Quantum states (qubits), operations (gates), and circuits
• The rules of the game: postulates of quantum mechanics
• Brief philosophical lament on the meaning of quantum mechanics (i.e., don't think about it too much) ​
• Quantum algorithms
• Worked-out example​: Deutsch-Josza algorithm

Session 2 (October 10, 2025)

Part I: Combinatorial Optimization (CO) Problems and Adiabatic Quantum Computing (QC)

• CO problems, Quadratic Unconstrained Binary Optimization (QUBO), and complexity analysis
• QUBO, Ising model, and quantum formulation via Hamiltonian
• Adiabatic theorem and adiabatic QC
• Quantum Annealing
• Implementation example in a Jupyter Notebook (e.g., max-cut, TSP)

Part II: Quantum Circuit based CO

• Hamiltonian time evolution discretization using trotterization
• Foundations of Quantum Approximate Optimization Algorithm (QAOA)
• Cost and mixer Hamiltonians and associated variational parameters
• Quantum circuit complexity analysis
• Implementation example in a Jupyter Notebook (e.g., max-cut, TSP)
• Issues around implementation on NISQ era hardware

Session 3 (October 17, 2025)

Part I: Quantum Search

• Introduction to Grover’s algorithm
• Diffusion operator, Amplitude amplification

• Circuit construction

Part II: Applications

• Using quantum search for optimization
• Introduction to Shor’s algorithm
• Relationship to optimization

Instructors: Dr. Borzou Rostami (UA), Dr. Stephen L. Smith (UW)

Day/Time: Friday, April 24, May 1 and 8, 2026 from 03:00 PM to 06:00 PM (EDT)

Location: Virtual (Zoom Link (external link) )

Course Outline:

Part 1 (2 hours): Introduction to OR and ML Overview

·        Fundamentals of optimization: linear, integer, and nonlinear programming.

·        Overview of machine learning paradigms (supervised, unsupervised, reinforcement).

·        Conceptual role of ML in optimization: from prediction to decision-making.

·        Framing optimization problems where ML becomes an integral component.

Part 2 (2 hours): ML for Modeling Optimization under Uncertainty

·        Stochastic optimization: scenario-based modeling and Sample Average Approximation (SAA).

·        Chance-constrained and risk-averse optimization (VaR, CVaR).

·        Predict-then-Optimize (two-step approaches linking ML predictions with optimization models).

·        Sequential Learning and Optimization (SLO): learning conditional distributions before solving.

·        Integrated Learning and Optimization (ILO): end-to-end frameworks where learning directly minimizes downstream decision cost.

·        Conceptual comparison of these paradigms: strengths, limitations, and when each applies.

Part 3 (2 hours): ML for Accelerating Optimization Algorithms

·        Classical optimization algorithms (branch-and-bound, cutting planes, heuristics).

·        How ML can improve algorithmic components:

o   Learning branching strategies.

o   Learning cut selection.

o   Learning primal heuristics.

·        Surrogate and approximation models for computational acceleration.

·        Positioning these methods in research: bridging heuristics with learned strategies.

Part 4 (2 hours): Large Language Models and Online Learning for Optimization

·        Large Language Models (LLMs):

o   Natural language interfaces for optimization modeling.

o   Automatic generation of formulations and constraints.

o   Current opportunities and risks (correctness, interpretability).

·        Online Learning and Optimization:

o   Framework for adaptive decision-making under uncertainty.

o   Multi-armed bandits, regret minimization, reinforcement learning perspectives.

o   Conceptual applications in dynamic pricing, scheduling, and adaptive planning.

Part 5 (1 hour): Wrap-Up, ML-Integrated Solvers and Future Directions

·        Overview of ML-based and ML-augmented solvers.

·        How modern solvers are beginning to integrate ML and LLMs.

·        Current trends and research frontiers in “learning to optimize.”

·        Reflections on theoretical and practical challenges for future research

Instructors: Dr. Trung Q. Duong (MUN), Dr. Octavia A. Dobre (MUN), Dr. Bhaskara Narottama (MUN), Dr. Mohamed Selim (MUN)

Day/Time: Friday, January 23, 30 and Feb 06, 2025 from 03:00 PM to 06:00 PM (EDT)

Location: Virtual (Zoom Link) (external link) 

Course Outline:

Session 1 (January 23, 2026): Quantum Circuits and Operations for Learning

• Quantum encoding and data feature inputs
• Types of quantum encoding: basis and angle encoding
• Quantum variational circuits for parameterized models
• Quantum measurements and extracting output variables
• Accounting for optimization constraints

Session 2 (January 30,2026): Defining Learning Workflows

• Quantum computing for supervised and unsupervised learning
• Optimization objectives and the corresponding loss functions
• Computing the gradient of the loss (e.g., using the parameter-shift rule)
• Updating the parameters of the circuit (e.g., using stochastic gradient descent)
• A practical example 1: Quantum ML to optimize a wireless system
• A practical example 1: Quantum ML for healthcare

Session 3 (February 06, 2026):  Quantum Machine Learning (QML) Algorithms

• Bridging classical and quantum machine learning
• Encoding classical data into quantum states (basis, amplitude, angle, etc.)
• Quantum variational circuits (QVCs)
• Example 1: QVC for self-interference cancellation in full-duplex transceivers
• Quantum kernels and quantum feature maps
• Quantum support vector machines and quantum support vector regressors (QSVRs)
• Example 2: QSVR for self-interference cancellation in full-duplex transceivers

Instructors: TBA

Day/Time:  Winter 2026 (TBA)

Location: Virtual