QAI Quantum Lab
Prototype & researchResearch frontier methods against real financial problems.
QAI Quantum Lab is a hardware-agnostic application and research practice for quantum-inspired optimisation, hybrid algorithms and post-quantum readiness in financial services.
The operating principle
Problem selection before technology selection.
Problem selection before technology selection. Every project begins with the economic objective, constraints, data availability and a strong classical or operations-research baseline.
Research tracks
A disciplined research agenda.
| RESEARCH TRACK | INDICATIVE PROBLEMS | EVIDENCE GATE |
|---|---|---|
| Optimisation | Portfolio, collateral, treasury, collections routing and resource allocation | Classical benchmark required |
| Risk and simulation | Scenario generation, sampling and sensitivity exploration | Reproducible quality and runtime |
| Machine-learning research | Feature selection, clustering, anomaly detection and hybrid kernels | No advantage claim without validation |
| Quantum-safe readiness | Cryptographic inventory, data-lifetime analysis and migration planning | Standards-aligned scope and expert review |
Four-stage method
Discover. Benchmark. Experiment. Decide.
Discover the problem. Benchmark the strongest practical classical approach. Experiment on suitable simulators, quantum-inspired methods or cloud hardware. Decide whether to stop, refine or progress based on reproduced evidence.
- 01
Discover
Define the economic objective, constraints and data availability.
- 02
Benchmark
Establish the strongest practical classical or operations-research approach.
- 03
Experiment
Use suitable simulators, quantum-inspired methods or cloud hardware.
- 04
Decide
Stop, refine or progress based on reproduced evidence.
Scope & responsibility
Prototype and pilot have precise meanings.
A prototype evaluates technical feasibility. A pilot is a controlled deployment with agreed users, data, safeguards and success measures. Neither term implies production readiness or quantum advantage.
Evidence requirements
Every experiment must be reviewable.
Each published experiment should show problem definition, dataset status, constraints, baseline, method, hardware or simulator, metrics, limitations, reproducibility status and named reviewers.
Build a clearer way forward