We work on hard optimization problems.
Not all of them — the right ones.
AIQue takes on two types of engagements: project-based work and research partnerships. In both cases, we start the same way: by determining whether your problem is actually suited to quantum hardware. That assessment is free, and we'll tell you honestly if it isn't.
What this hardware is built for?
Our system is designed for combinatorial optimization — problems where the solution space is too large for classical exhaustive search, and where the objective function and constraints can be expressed in discrete terms. Portfolio construction with complex allocation constraints. Logistics routing across large asset networks. Molecular screening for binding affinity. Resource allocation under competing demands.
The common thread: discrete decisions at scale, with constraints that interact in ways that defeat standard solvers.
Project Engagements
Project engagements follow the structure established in our published research: a defined problem, hardware execution, and results compared against a classical baseline using the same inputs and time period. Deliverables include the results, the methodology, and documentation.
Research Partnerships
For academic institutions and independent researchers, we offer hardware access partnerships. These are structured around a defined research question, with co-authorship on any published findings. We're particularly interested in novel problem classes and methodology development.
What we don't take on
Not every optimization problem is a candidate. We'll say so upfront if yours isn't.
Problems that are already solved efficiently by classical methods — standard linear programs, small combinatorial problems, anything a modern solver handles in under a second — don't benefit from quantum hardware. The value is at large scale and high constraint complexity.
Continuous optimization problems don't map directly to this hardware. Discretization is sometimes possible, but it introduces approximation error that has to be validated against your accuracy requirements.
Problems requiring guaranteed-optimal solutions aren't a match. Adiabatic quantum computing finds very good solutions with high probability — not provably optimal solutions. If your application has regulatory or contractual requirements for exact optimality, we'll tell you that at the assessment stage.
Real-time decision problems — anything requiring a sub-second response — aren't suited to current hardware latency.