RouteIQ.

The decision problem

How should physical or digital entities move through a network?

Routing problems are the classic face of combinatorial hardness: the number of possible paths and route plans explodes with every stop, vehicle or link. Vehicle routing, last-mile delivery, traffic engineering and network flow are all NP-hard, and at fleet or network scale the difference between a good plan and the optimal one is measured in fuel, latency and service level.

Capabilities

What RouteIQ does.

Representative NP-hard problem families

Recognized mathematics beneath the decision.

RouteIQ recognizes these families in your problem and compiles each to the formulation and solver that fits: classical, AI, quantum-inspired, quantum or hybrid.

Where it applies

Reused across industries.

Composition

Rarely used alone.

Routing across space is scheduling across time: ScheduleIQ times the movements, ResourceIQ assigns what moves, and DesignIQ shapes the network it moves through.

Hardware-independent execution

The API stays stable when the hardware changes.

You call RouteIQ in terms of your decision such as objectives, constraints and data, not in terms of a solver or a quantum architecture. The problem is captured in GradientIR and compiled to whatever executes it best: classical mathematical optimization, CPUs, HPC, GPUs, AI-based optimization, quantum-inspired methods, quantum annealing, gate-based quantum processors, or hybrid classical-quantum systems. When the execution technology evolves, your application does not have to change. We don't claim a quantum computer is in the loop when it isn't.