QUSMOS is a hybrid, quantum-ready optimisation engine that plugs into existing industrial control and planning systems to improve day-to-day operations. It ingests real-time signals, forecasts, operational limits, and business goals, then computes feasible schedules and control setpoints that are cost-aware, constraint-safe, and auditable, with clear KPI reporting and guardrails. QUSMOS runs quantum-inspired optimisation today and is architected for hybrid quantum acceleration as hardware matures, while optional industry packs (e.g., Greenhouses and Smart Grids) and an AI agent interface reduce integration effort by helping engineers capture and validate objectives and constraints without replacing the existing “brain” of the system.
Smart Grids Pack
Distributed energy systems are increasingly complex: PV/wind variability, storage constraints, generator limits, import/export caps, tariff structures, and reliability policies must be coordinated in real time. Many sites operate with conservative heuristics or vendor-specific logic that can miss savings opportunities, cause unnecessary cycling, and struggle during peak events—because the real challenge is constraint-heavy dispatch under uncertainty.
The Smart Grids Pack provides a hybrid quantum optimisation engine that plugs into EMS/DERMS/SCADA and optimises dispatch across DER assets with explicit constraints and guardrails. It converts site objectives (cost, peak reduction, resilience) and operational policies into an optimisation model, then outputs dispatch schedules and setpoints that remain feasible under limits like SOC bounds, ramp constraints, and curtailment rules. QUSMOS runs quantum-inspired solvers today and stays quantum-ready for acceleration as hardware matures, while KPI traceability supports governance and performance reporting. An optional AI agent interface accelerates requirements capture and configuration validation so integration is faster and less error-prone.
Greenhouse Pack
Modern greenhouses are a tightly coupled control problem: heating, ventilation, humidity, CO₂ dosing, screens, and lighting continuously interact, while energy prices and weather change rapidly. Operators must balance crop and climate targets against cost, safety bounds, and equipment limits, but traditional rule-based control and manual tuning often lead to instability (actuator “chatter”), avoidable constraint violations, and higher energy spend—especially under volatility.
The Greenhouse Pack adds a hybrid, quantum-ready optimisation layer on top of the existing climate computer/SCADA stack. It ingests forecasts, real-time signals, and operational limits, then produces feasible schedules and setpoint trajectories that are cost-aware, constraint-safe, and stable in a rolling horizon. Under the hood, it uses quantum-inspired optimisation today with a clear path to hybrid quantum acceleration later, while KPI reporting and audit trails keep decisions explainable. An optional AI agent interface helps engineers capture and validate objectives/constraints and generate structured configurations, reducing setup friction without removing human control.