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QAOA on Simulator & Quantum Hardware
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Quantum optimization is the most commercially scrutinized corner of quantum computing, yet most engineers who want to assess it face a gap: tutorials are either toy demos with no real data or papers that assume a physics degree. This workshop closes that gap in one sitting. In 2.5 hours, you will derive QAOA from first principles, build the complete pipeline on real market data (10 and 6 US stocks, actual return and covariance figures), run it in Qiskit 2.4, and benchmark it against the exact classical optimum.
What you will get:
⭐ Full QAOA in 2.5 hours — theoretical foundations and Qiskit codes
⭐ Four Jupyter notebooks — Yahoo data fetching, Qiskit Primitives, QAOA on simulator for 10 stocks, and on IBM hardware for six stocks
⭐ IBM Hardware — Custom optimization routine to run on hardware in a series of single jobs through open plan
⭐ All PDF slides — complete derivations written to be a standalone reference
⭐ Full recording — Full recording available in two hours
⚛️ "Do I need a paid IBM account?" No. The custom optimizer has been specifically developed to run on the free IBM Open Plan in 2 minutes.
What you’ll learn
Master the full QAOA pipeline on real market data — and become the person your team trusts to assess quantum optimization.
Cast Markowitz portfolio selection as a QUBO
Set penalty weights that actually work
Map any QUBO to an Ising Hamiltonian via xᵢ → (I − Zᵢ)/2
Derive the ansatz from the adiabatic theorem through Trotterization
Translate every covariance entry into an explicit CNOT–Rz–CNOT block
Visualize the full (γ, β) optimization landscape at p = 1
Build the ansatz with QAOAAnsatz, estimate ⟨H_C⟩ with EstimatorV2
Train with POWELL using adiabatic-inspired linear-ramp initialization
Decode measured bitstrings into named portfolios and filter for budget feasibility
Rethink optimizer cost in terms of the number of jobs, not iterations
Transpile for a real utility-scale IBM device
Get verified optimum on real hardware for 6 stocks of your choice
Workshop agenda
0:00–0:20 | The Problem, Classically
Set up real market data (10 US stocks: Apple, Microsoft, JPMorgan, etc., with actual returns and covariances from Yahoo Finance). Frame portfolio selection as maximizing return and minimizing risk
0:20-0:50 | Flash Introduction to Quantum Computing and Qiskit
Learn the basic quantum gates and how to implement circuits along with measurements using Qiskit primitives.
0:50–1:15 | From QUBO to the Ising Hamiltonian
Map the classical cost function to the quantum Hamiltonian via the eigenvalues of quantum gates
1:25–1:50 | QAOA Mechanics
Motivate QAOA from the adiabatic theorem: start in the easy ground state of a mixer, evolve under an interpolation between the mixer and the cost Hamiltonian, and discretize into alternating unitaries
1:50–2:15 | Hands-On Coding: Five Checkpoints
Implement QAOA in Qiskit by dividing it into five checkpoints for 10 stocks using the Aer Simulator. Each checkpoint is self-contained; if you fall behind, jump to the next checkpoint marker.
2:15–2:30 | Custom Optimization Loop for Hardware Run
Revisit the complete pipeline and add Checkpoint 6 after running on IBM hardware for 6 stocks.
What you take home
Four Jupyter notebooks (fetching live stock data & classical optimal, Qiskit Primitives, QAOA on 10 stocks with Aer Simulator, QAOA on 6 stocks with IBM hardware using Free Plan). All slides.
Learn directly from Muhammad
Dr. Muhammad Faryad
Tier-2 IBM Qiskit Advocate, IBM-certified Qiskit developer
Who this workshop is for
ML engineer at a bank tasked with evaluating quantum optimization — strong ML background, no QC experience, want your own honest benchmark.
Physicist or QC researcher who knows the theory — want to see a complete, honestly-benchmarked real-world QML application, end-to-end.
Risk manager or CTO asked to evaluate quantum computing for your firm — technical enough to judge, tired of slick marketing and hype.
What's included
Live sessions
Learn directly from Dr. Muhammad Faryad in a real-time, interactive format.
Slides, Jupyter Notebooks, and recording
All material used during the lecture will be provided to the students ONE DAY BEFORE the workshop begins so that they can follow along. The recording will be provided 2 hours after the workshop ends.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
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Free resource
Five Jupyter Notebooks showing classical data encoding is the permanent bottleneck of Quantum ML
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