Quantum Portfolio Optimization
Dr. Muhammad Faryad
Tier-2 IBM Qiskit Advocate
Build Quantum Optimization Pipeline for Financial Portfolio in Qiskit 2.4
Use code EarlyBird to get 70% off. First 20 seats.
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 (8-12 US stocks, actual return and covariance figures), run it in Qiskit 2.4, and — critically — benchmark it against the exact classical optimum.
What you will get:
🎓 2.5 hours live with Q&A — theoretical foundations first, then a guided walkthrough of implementation in code
📓 Jupyter notebooks — complete, end-to-end QAOA pipelines in Qiskit 2.4, fully tested, checkpointed Jupyter notebooks you can rerun and extend
⚛️IBM Hardware — Run QAOA on IBM quantum hardware
📄 All PDF slides — complete derivations and written to be a standalone reference
🎥 Full recording — rewatch the derivations, pause on the code, or catch up if the time zone doesn't work for you
This saves you 3 weeks of DIY pipeline-building. Also, you will get a $300 credit towards my QML course.
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 — return, risk, and a budget constraint — as a QUBO
Set penalty weights that actually work (too small: infeasible portfolios win; too large: the landscape flattens)
Map any QUBO to an Ising Hamiltonian via xᵢ → (I − Zᵢ)/2 and verify the mapping numerically
Derive the ansatz from the adiabatic theorem through Trotterization to learnable parameters
Translate every covariance entry into an explicit CNOT–Rz–CNOT circuit block
Visualize the full (γ, β) optimization landscape at p = 1 — the picture that makes QAOA click
Build the ansatz with QAOAAnsatz, estimate ⟨H_C⟩ with EstimatorV2, sample with SamplerV2
Train with COBYLA using adiabatic-inspired linear-ramp initialization and multi-restart
Decode measured bitstrings into named portfolios and filter for budget feasibility
Compute approximation ratio and probability-of-optimum against exact brute-force enumeration
Transpile for a real utility-scale IBM device and read the depth and gate-count cost of dense covariance matrices
As of 2026, QAOA has no demonstrated advantage over classical solvers at accessible problem sizes. This workshop teaches you why.
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 | From QUBO to the Ising Hamiltonian
Map the classical cost function to the quantum Hamiltonian via the eigenvalues of quantum gates
0:50–1:15 | 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
1:15–1:25 | Break
Walk around, stretch, get yourself some coffee!
1:25–2:05 | Hands-On Coding: Five Checkpoints
Implement QAOA in Qiskit by dividing it into five checkpoints. Each checkpoint is self-contained; if you fall behind, jump to the next checkpoint marker.
2:05–2:25 | Hardware Reality & Honest Assessment
Transpile the circuit for a real 156-qubit IBM device. Inspect the depth and two-qubit gate count after routing — the reality check for near-term QAOA.
2:25–2:30 | Wrap-Up & Q&A
Revisit the complete pipeline. Open floor for questions and discussion of next steps.
What you take home
A fully tested, checkpointed Jupyter notebook; complete lecture slides with every derivation; and five concrete R&D directions ready for a research project or internal proof-of-concept.
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.
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