Routing and assignment are NP-hard
Which drone flies where, in what order, within range and airspace limits. One failed unit or a new task and the whole plan must be recomputed, in seconds.
FSharp.Azure.Quantum · drone-first
Describe the problem. Skip the physics.
Quantum optimisation that ordinary developers and AI agents can ship. The fleet problem goes in as plain code; the optimised plan comes out. The algorithms, error correction and hardware connections are already done. First application: coordinating drone fleets.
First application · Drone fleet coordination
Coordinating a fleet is a combinatorial problem that grows faster than the fleet.
Quantum tooling is written for physicists
Qiskit, Cirq and Q# expect gates, Hamiltonians and hand-encoded QUBOs. A fleet-software team has none of that vocabulary and no way to hire it.
Hardware arrived without a developer path
Azure Quantum, D-Wave and Braket sell machine time today. Nothing lets an ordinary .NET application describe a business problem and run it there.
Drone-first: what flies today.
Give the swarm its tasks, calculate the routes, re-route on the fly if a unit fails. The hard part is the combinatorics — that is exactly what quantum optimisation is for.
Describe the fleet
Units, their range and capabilities, the tasks to cover and the airspace and altitude limits they fly under.
Give the objective
Shortest total flight time, balanced battery use, guaranteed coverage — expressed as a business goal, not a Hamiltonian.
Solve the assignment
The library turns the model into an optimisation problem and solves it on a simulator, a hybrid path or a real quantum backend.
Re-route on the fly
A unit fails or the mission changes, the swarm is re-planned in place and exported to the flight controllers again.
Three runnable examples in the repository
Fleet path planningWaypoints become a tour, split into range-safe sorties across the fleet, with altitudes checked against the regulatory ceiling.examples/Drones/FleetPathPlanningSwarm task allocationTasks with dependencies and priorities scheduled across drones with capacity limits, minimising mission time.examples/Drones/SwarmTaskAllocationSwarm choreographyFormation transitions solved with QAOA and exported to Crazyflie micro-drones indoors and MAVLink missions for ArduPilot and PX4 outdoors.examples/Drones/SwarmChoreography
Where the hardware is
The choreography example runs four drones on sixteen qubits on a laptop simulator, with the quantum solver executing every transition. Larger fleets run today through the hybrid path and on cloud QPUs and annealers. The code does not change when the machines grow.
Three layers. Developers touch only the top one.
Layer 1 · Builders
The business vocabulary: routing, scheduling, assignment, knapsack, portfolio, network flow; fraud, churn and workforce builders. Written by developers or AI agents in F# or C#.
Layer 2 · Solvers
QAOA for optimisation, VQE and ADAPT-VQE for chemistry, Grover and amplitude amplification, QFT-based arithmetic and phase estimation.
Layer 3 · Backends
One interface over the local simulator, Azure Quantum (IonQ, Rigetti, Quantinuum, Atom Computing), D-Wave annealers, an AWS Braket plugin and a topological simulator for the Majorana era.
Write it today. Run it at scale later.
One NuGet package
dotnet add package FSharp.Azure.Quantum — version 1.4.2, no research stack to assemble.
F# builders, C# fluent API
Written like any other .NET code, reviewable by the whole team.
Quantum-first, honestly
A quantum solver never silently returns a classical answer; the hybrid router says which method produced a result.
Every backend, one interface
Simulator on a laptop today, cloud QPUs and annealers when the problem needs them, with OpenQASM interop and error mitigation.
Second act · chemistry
Antibiotics from Western materials.
Most of the world's key antibiotic intermediates come from a single country. The same solver layer runs VQE to compare alternative β-lactam synthesis routes by activation energy, next to binding-affinity and metabolism examples. Regulated drug development is not a market we can demonstrate on our own, so we take it forward with pharma and academic partners who own that path — after the fleets are flying.
For investors and partners
We are raising a seed round to put quantum optimisation in the hands of every fleet developer.
The library is public domain; the hosted Swarm Planning API, the flight-controller integrations and the pilots are the business. Ask for the investor deck, or a live demo: four drones, one laptop, a quantum solver, twenty minutes.