<summer program project phase>
Vanguard Challenge
Quantum for Finance
Vanguard is an investment-management company known for low-cost index funds and investor-first principles. This challenge explores how quantum and hybrid quantum-classical optimization methods may be applied to complex finance and operations problems.
Teams may choose one of two use cases or optionally address both:
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Multi-Asset Portfolio Construction
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Call Center Staffing Optimization
Option 1: Multi-Asset Portfolio Construction
The Problem
Build a model that allocates capital across multiple asset classes while balancing:
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Expected return
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Risk
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Transaction costs
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Diversification
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Liquidity
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Sector or exposure limits
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Investment guardrails
The objective is to recommend an allocation that improves expected utility while remaining within defined constraints.
Key Project Tasks
Teams should:
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Define the mathematical formulation using binary variables, linear constraints, and quadratic objectives.
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Convert the cost function into a form compatible with a quantum optimization algorithm.
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Use synthetic or anonymized data covering returns, volatility, correlation, and costs.
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Build a classical mean-variance baseline.
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Add realistic constraints and scenario penalties.
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Compare risk, expected return, turnover, guardrail breaches, and explainability.
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Validate the solution using a classical optimization routine.
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Build a portfolio co-pilot that explains the recommended allocation and its trade-offs.
The strongest solution will produce a high-quality risk-adjusted result with no hard-constraint breaches.
Option 2: Call Center Staffing Optimization
The Problem
Develop a staffing model that matches forecasted demand with the right mix of agents while minimizing:
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Customer wait time
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Idle time
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Overtime
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Schedule friction
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Total staffing cost
The model should account for skills, channels, shifts, breaks, availability, and service-level agreements.
Key Project Tasks
Teams should:
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Define the optimization problem mathematically.
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Convert it into a form suitable for a quantum optimization algorithm.
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Generate synthetic call-arrival data by time, queue, and channel.
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Forecast demand and optimize staffing.
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Simulate queue outcomes.
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Report average speed of answer, abandonment, SLA attainment, and utilization.
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Allow a manager to adjust priorities such as cost, service, employee preference, and resilience.
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Validate the results using a classical optimizer.
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Build a workforce-planning interface that identifies understaffed intervals and explains trade-offs.
The strongest solution will meet the service-level target while minimizing staffing and overtime cost.
Required Deliverables
Your final GitHub repository should include:
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Mathematical formulation
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Source code
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Classical baseline
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Quantum or hybrid implementation
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Assumptions and constraints
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Results and benchmarking
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A cleanly formatted presentation
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A working prototype or demonstration
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Clear documentation of each team member’s contribution
Platforms & Tools
Teams may use:
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Any quantum SDK
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Any quantum platform
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Quantum hardware
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Quantum simulators
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Noise-model simulations
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Classical optimization libraries
Judging Criteria
Solutions will be evaluated based on:
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Solution speed
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Optimality according to the cost function
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Scalability
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Constraint satisfaction
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Interpretability
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Technical and business communication
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Quality of the GitHub repository and presentation
Data & Privacy Requirements
Use only synthetic, anonymized, or challenge-approved datasets.
Do not publish:
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Raw Vanguard operational data
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Confidential business information
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Restricted datasets
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Personally identifiable information
Any use of AI or coding assistants must be disclosed, and teams must be able to explain and defend all submitted work.
Team & Deadline
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Maximum team size: 3 participants
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Final submission deadline: August 7, 2026
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No deadline extensions will be offered.