Project 2
Quantum for Portfolio Optimization
Portfolio
Optimization
This challenge, developed with Vanguard, explores how sampling-based quantum optimization can be harnessed to overcome the limitations of classical computing barriers. By leveraging hybrid quantum-classical algorithms and decomposition pipelines, your goal is to prototype a quantum-enhanced solution.
Vanguard’s portfolio construction process lies at the heart of its investment strategy, balancing risk, return, and investor preferences across a vast landscape of asset classes and constraints. However, as portfolios grow in complexity, spanning thousands of securities, intricate guardrails, and real-time trading demands, classical optimization tools like GUROBI face growing limitations in speed, scalability, and solution diversity.
This challenge explores how sampling-based quantum optimization can be harnessed to overcome these barriers. By leveraging hybrid quantum-classical algorithms and decomposition pipelines, the goal is to prototype a quantum-enhanced solution that can:
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Efficiently solve high-dimensional, constraint-heavy portfolio optimization problems.
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Deliver near-optimal asset allocations within tight runtime windows.
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Scale to real-world use cases like fixed income ETF creation and index tracking.
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Preserve critical business metrics such as tracking error, excess return, and risk exposure.
The project focuses on using binary decision variables and quadratic objectives to simulate realistic trading scenarios. The challenge lies not only in achieving computational gains but also in maintaining interpretability, robustness, and alignment with investment principles.
01.
Challenge Duration & Key Dates
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5 weeks
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Teams start working on July 1, 2025
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Teams submit their challenge solutions on August 10, 2025
02.
Team guidelines
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Max. Team size - 3
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All team participants must be enrolled in Womanium & WISER 2025 Quantum Program.
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Everyone is eligible to participate in this challenge and win awards.
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Best participants get selected for QSL fellowships
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We strongly encourage interdisciplinary collaborations pairing mathematically inclined researchers and practical developers familiar with diverse quantum SDKs and tooling.
03.
Quantum hardware & platform
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Participants may use any quantum SDK or platform of their choice.
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Participants may use any quantum hardware for noise-model simulations.
04.
Challenge deliverables
The submission github repo must include the following:
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Review the mathematical formulation provided below, focusing on binary decision variables, linear constraints, and the quadratic objective (see figure below)
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Convert the binary optimization problem to a formulation that is compatible with a quantum optimization algorithm. For example, convert the constrained problem to an unconstrained problem.
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Write a quantum optimization program for handling problems of the type in Task-2. An example of such an optimization routine, which is used in portfolio optimization is the Variational Quantum Eigensolver (see resources below) however, you may pursue what you judge to be the best solution.
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Solve the optimization problem in Task 1 using your quantum formulation.
Validate your solution in Task 4 using a classical optimization routine. -
Compare the solution quality against the benchmark classical solution in terms of the cost function, and include relevant performance metrics(e.g., convergence of the optimization routine, and scaling properties with problem size).
Note: Teams should walk through their approach in the presentation and demonstrate their prototype live. This is your opportunity to showcase your thinking, creativity, and results in an informal, interactive format.
05.
Judging Criteria
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The submissions must be in an easy-to-access, and well-structured format.
All participants must complete the first three Tasks 1, 2, and 3 to be eligible for project certificates. -
The best attempt at Tasks 4 and 5 would be given the most weight. Finalists for QSL fellowships will be decided on the basis of the highest cumulative scores from all the tasks,
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Technical Merit, Novelty, Communication and Presentation Skills.
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Solutions will be evaluated against internal benchmark implementations at Vanguard
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Evaluation will be based on:
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Speed of the solution
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Optimality (as measured by the cost function)
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Scalability (problem size handled)
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06.
Recommended reading list
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An example of how VQE can be implemented, Portfolio optimization with variational quantum eigensolver (VQE)-(2).
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Improving Variational Quantum Optimization using CVaR, Panagiotis Kl. Barkoutsos et al., arXiv:1907.04769v3.
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Vanguard Presentation and Other resources are available on the WISER Canvas instance. Please note Canvas is only accessible after successful registration in the WISER 2025 quantum program.
