Solver engine · Beta · Scheduling & Assignment
QG-SCHED.
Who does what, and when? QG-SCHED assigns people, machines and tasks against hard constraints — coverage, skills, hours, and dependencies — benchmarked against your current approach.
Overview
Scheduling & Assignment.
Job-latency improvement
On a 300-task edge-computing scheduling workload, against the incumbent scheduler. Preliminary, documented methodology.
PreliminaryBenchmarked first
We measure against your current scheduler before you change anything.
Your constraints
Skills, hours, fatigue rules, and dependencies you already operate under — respected, not simplified away.
Canonical problems
What QG-SCHED solves.
Industries
Where it's already at work.
The second most mature engine — exercised across five of our eight industries.
Methods
A hybrid quantum-classical scheduler.
QG-SCHED compiles scheduling and assignment problems to QUBO/Ising form and solves them with quantum simulated annealing, a direct link to D-Wave annealing hardware, and DAG-aware workflow optimization for dependency-heavy jobs. As with every engine, we route each job to whichever solver measurably wins.
Research behind this engine
A hybrid quantum-classical agent/task scheduler with quantum simulated annealing and a D-Wave link, plus DAG workflow optimization — developed at QuMatrix.
Proof of value
A paid, scoped, time-bound pilot.
We hold every published number to the same standard: independently reproduced and methodologically documented before it loses the "preliminary" label. If we can't defend it, we don't print it.
FAQ
Straight answers.
Is this actually quantum?
Yes — QG-SCHED runs on hybrid quantum-classical scheduling, including quantum simulated annealing with a direct D-Wave link. Most production workloads execute on classical accelerators today; we're transparent about which solver handled which job.
What's the maturity?
Beta. It runs real benchmarks against real scheduling workloads, but it is not general-availability yet.
Do I have to replace my scheduler?
No. The benchmark needs only a data export. Integration happens later, and only if the numbers justify it.
How is the 13.1% figure measured?
Against the incumbent scheduler, on a 300-task edge-computing workload, with identical constraints. It is preliminary and stays labeled that way until independently revalidated. Full methodology shared under NDA.
What data do you need to start?
Historical schedules or task queues, the constraints you operate under, and current performance. Anonymized or sampled data is fine for a first benchmark.
See QG-SCHED run against your data.
Thirty minutes to scope a pilot, a clear benchmark on your own data, and an honest read on whether it's worth going further.
Contact us →