Research

Quantum Readiness

Hybrid algorithms that bridge classical computing and the quantum future.

Quantum hardware is arriving unevenly, and the problems worth running on it are not the ones most teams have written down. Quantum readiness is the work of getting a problem into a shape where a quantum advantage, if and when it arrives, is something you can actually take.

What makes this hard

The problems most likely to benefit are buried inside systems that were never modelled as optimisation problems.
Hardware access is intermittent and expensive, so the classical fallback has to stay first-class.
Results are probabilistic, which breaks pipelines built to expect one deterministic answer.

How we approach it

Problem framing
Identify which parts of a workload are genuinely combinatorial, and express them so a solver, classical or quantum, can take them.
Hybrid execution
Run classically by default and route only the hard core to quantum backends, so nothing depends on hardware being available.
Result interpretation
Treat a distribution of answers as the output, with confidence carried through to whatever consumes it.

What you should expect

A shortlist of workloads where quantum methods are worth the effort, and an honest note on the ones where they are not.
A classical baseline you can ship today and measure any future advantage against.

Where this shows up

Guido

The agentic orchestrator the other products run on.

Guido is the workspace where the agents, tools and data behind every Trazup product live. Discovery, analysis, planning and reporting sit in one place, with the same permissions, datasets and audit trail underneath.