ResourceIQ.

The decision problem

What resources should go where?

Allocation decisions are deceptively hard: the number of ways to assign resources to demands grows combinatorially, and every placement constrains the next. Assigning shipments to vehicles, GPUs to jobs, capital to positions, or inventory to locations are all NP-hard problems where a small improvement compounds into large operating gains at scale.

Capabilities

What ResourceIQ does.

Representative NP-hard problem families

Recognized mathematics beneath the decision.

ResourceIQ 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.

ResourceIQ places resources into the structure DesignIQ chooses; ScheduleIQ then decides when they're used, and RouteIQ how they move. Assignment and scheduling are frequently co-optimized.

Hardware-independent execution

The API stays stable when the hardware changes.

You call ResourceIQ 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.