Qatalyst turns messy operations into solve-ready optimisation, then finds the best way to solve it: proven classical algorithms, hybrid quantum-classical, or pure quantum, benchmarked honestly against each other. Classical answers today. No optimisation expert required. And the same model keeps you quantum-ready for the bigger, more complex problems classical can’t reach.
Automated model generation and continuous re-optimisation as ground conditions shift. No manual maths. No waiting.
Every constraint is explicit, auditable, and communicable across teams. The logic behind every recommendation is traceable.
Optimise time, cost, energy, and emissions in one decision layer. Built for operations decarbonising under real constraints.
Four stages. Fully automated. No optimisation expert, no manual modelling: the solver stays behind the scenes and you get assignments, schedules, and routes.
Operational rules, vehicle specs, power limits, and time windows are ingested as structured constraints.
Constraints are compiled into a solve-ready optimisation model (MILP, QUBO, or hybrid) automatically.
Classical solvers answer by default: fast and proven. Hybrid and pure quantum back-ends run selectively where they earn their place, every result benchmarked honestly against the classical baseline.
Clear outputs: site selections, schedules, route allocations, and capacity plans. Full constraint traceability.
Qatalyst was selected as a winner of the 2026 Creative Challenge by IonQ and Qollab. Our entry, Quantum Courier, turns vehicle routing with time windows into a five-stage browser game. Play it free, no sign-up needed.
Operations where decisions are coupled, time-critical, and repeat daily.
Where to place battery storage across a distribution network, under a fixed capital budget and across multiple contingency scenarios. The combinatorial search space grows fast as networks scale, and classical methods stall. The same QUBO engine runs across every Qatalyst domain, applied here to grid planning.
Assigning people, vehicles, or equipment to tasks under hard constraints: capacity limits, time windows, and competing priorities. The problems every operation solves daily by spreadsheet and gut feel.
Rapid re-optimisation when weather, infrastructure failures, or supply disruptions force real-time schedule and route changes.
One constraint model. Multiple solver back-ends. Classical carries your operation today; the same model runs on quantum hardware the day your problem outgrows classical. No re-platforming, no rebuild.
Assignments, schedules, routes, and capacity plans surfaced via dashboard or API. Constraint audit trail included.
Operational rules compiled into MILP, QUBO, or CQM formulations. No manual modelling required.
Classical optimisation by default. Hybrid annealing and gate-model back-ends run today on the dense, highly-coupled problems where quantum is worth testing, and stand ready for the scale where classical stalls.
Public network datasets, operational timetables, grid capacity data, and energy tariffs. Customer-provided or synthetic data only.
Global Industry Challenge 2026
Conception X
Microsoft
Cambridge Female
Kipu
Whether it's grid storage siting, scheduling, or resource allocation, we'll show you what Qatalyst can do with your constraints.
hello@qatalyst-quantum.co.uk