Quantum-Optimised Portfolio Rebalancing for Asset Manager
A quantitative asset manager needed to solve large-scale portfolio optimisation problems that classical computers were taking 8+ hours to process.
Team
4 quantum engineers + 3 quant analysts
Timeline
20 weeks end-to-end
Client
Quantitative Asset Manager
Outcomes Delivered
78%
Computation Time Reduction
1.8%
Portfolio Return Improvement
8 hrs → 28 min
Rebalancing Cycle Time
How we delivered it
Formulated the portfolio optimisation problem as a Quadratic Unconstrained Binary Optimisation (QUBO) problem suitable for quantum solvers.
Implemented the Quantum Approximate Optimisation Algorithm (QAOA) on IBM Qiskit Runtime, benchmarking against classical solvers.
Built a hybrid quantum-classical pipeline that uses quantum computing for the combinatorial optimisation step and classical computing for pre/post-processing.
Developed a backtesting framework that validated the 1.8% portfolio return improvement across 3 years of historical data.
Delivered a portfolio manager dashboard showing optimisation run history, quantum vs. classical performance comparison, and rebalancing recommendations.
What we built
Implemented a hybrid quantum-classical optimisation algorithm using IBM Qiskit that reduces portfolio rebalancing computation time dramatically.
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