Project Lead: Applied Quantum Computing
Sector: Financial
Project Summary
This project investigates whether quantum computing can improve Risk-Weighted Asset (RWA) optimisation for banks, a core challenge in capital allocation and risk management under the Basel III regulatory framework. The objective is to solve the “Inverse RWA Optimisation” problem: determining the optimal distribution of lending exposure across asset classes to achieve target capital requirements while balancing profitability, risk, and regulatory constraints. The problem is highly complex due to the large number of possible portfolio configurations.
The team will develop and test quantum optimisation approaches on two hardware modalities: IBM superconducting gate-based quantum computers and PT -2’s photonic quantum processor. Algorithms including QAOA, adaptive and recursive QAOA, and ORCA’s bosonic optimisation solvers will first be tested on simulators before selected methods are run on real hardware.
Expected outcomes include an assessment of the viability of quantum computing for banking optimisation, benchmarking against classical methods, a catalogue of hardware-specific problem encodings, and greater understanding of which financial optimisation problems are best suited to near-term quantum computers.
