Role: Design Lead (UI/UX, Prototyping, Client Demo)

Team: EMEA Head of Quantum Sales, 2 Quantum Developer, 2 Engineers, 2 Designers.

Toolkit: Qiskit, Python

Overview

A global enterprise was spending significant compute time pricing fuel hedging instruments across quarterly portfolios. The underlying challenge — pricing call and put options across multiple assets over one- and two-year forward periods — aligned well with quantum amplitude estimation.

The algorithms were functioning. The challenge was making their outputs usable for quantitative trading teams operating under time pressure.

I led prototyping, interface design, and the final client demo, working closely with quantum developers to translate algorithm outputs into something traders could evaluate and act on.

The Design Problem

The audience was quantitative trading teams, not general users. They did not need an explanation of quantum computing. They needed:

1. Clear decision outputs
2. Transparent differentiation between quantum and classical methods
3. Traceability from algorithm to result

This created three concrete requirements.

Decision clarity.

Users needed to evaluate hedging positions and forecast outcomes without switching into technical tooling. The interface had to absorb the computational complexity and present trade-relevant outputs directly.

Method differentiation.

Quantum-derived outputs (via amplitude estimation) had to be visibly distinct from classical pricing results. This distinction needed to be embedded in the UI, not hidden in documentation.

Algorithm lineage.