ScheduleIQ.

The decision problem

When should activities happen, and in what sequence?

Scheduling is among the most studied and most stubborn NP-hard families: sequencing jobs, rostering people, and timetabling activities against hard constraints produce search spaces that defeat exhaustive methods. As horizons lengthen and constraints multiply, the gap between a workable schedule and the best one widens — and it widens fastest exactly where utilization matters most.

Capabilities

What ScheduleIQ does.

Representative NP-hard problem families

Recognized mathematics beneath the decision.

ScheduleIQ 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.

Scheduling rarely stands alone: ResourceIQ decides which resource does the work, RouteIQ how it moves between activities, and DesignIQ the structure being scheduled. Schedule and route are often one coupled decision.

Hardware-independent execution

The API stays stable when the hardware changes.

You call ScheduleIQ 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.