Quantum optimization: test it on your hardest scheduling and allocation problems.

Optimization is one of the most studied uses for quantum computers, and one of the most overstated. We prototype quantum and quantum-inspired methods on your real constraints, compare them with the best classical solver we can build, and tell you whether the difference is worth pursuing.

Book a strategy call

What we cover

The problemsProblems with too many combinations to check
The methodsQuantum, quantum-inspired and classical, side by side
How we workA fair comparison, then a decision

The problems

Problems with too many combinations to check

Many business decisions are combinatorial: which staff work which shifts, which vehicles take which routes, which assets go into a portfolio, which jobs run on which machines. Each added option multiplies the number of possible answers, and the best answer has to respect many constraints at once, such as skills, capacity, budgets, rules and risk limits.

Classical solvers handle these problems well up to a point, and good modeling takes them a long way. Beyond that point, teams settle for rules of thumb or plans that are good enough. Quantum optimization research is aimed at that frontier.

What we do

Staff and shift scheduling
Vehicle routing and logistics
Portfolio selection under constraints
Resource and capacity allocation, including in hospitals
Production and job-shop scheduling

The methods

Quantum, quantum-inspired and classical, side by side

The quantum methods we work with include the Quantum Approximate Optimization Algorithm (QAOA) and variational approaches such as the Variational Quantum Eigensolver (VQE), which pair a small quantum circuit with a classical optimizer that tunes it. Quantum annealing is a related approach designed for this class of problem. Quantum-inspired algorithms run on classical hardware and borrow ideas from all of these.

Each starts the same way: the problem is written as a cost to minimize, often in a form called QUBO, short for quadratic unconstrained binary optimization. That step is useful in its own right, because it forces a precise statement of what you are optimizing and which constraints are hard rules and which are preferences.

How we work

A fair comparison, then a decision

We begin with your data and your rules, not a textbook problem. Together with your planners we define the objective, the constraints and a set of test cases drawn from real scenarios. Then we build a strong classical baseline, usually an integer-programming model or a well-tuned heuristic, because a quantum method is only interesting if it beats the best alternative, not a weak one.

Next we run quantum and quantum-inspired versions on simulators and, where it helps, on cloud quantum hardware, and compare solution quality, time to solution and cost. At the problem sizes today's hardware can handle, the classical baseline often wins. When it does, you keep a better solver than you started with. When a quantum or quantum-inspired method shows promise, the readout explains how it would scale and what would have to be true for it to pay off.

What we do

Problem modeling with your planners
A strong classical baseline solver
Quantum and quantum-inspired prototypes
A side-by-side readout on quality, time and cost

Questions

Common questions

Will quantum optimization beat our current solver?

Probably not on today's hardware, for problems of real business size. Research results are promising for some problem structures, and hardware is improving. That is why we always include a classical baseline: the comparison tells you where you stand, and the better model usually pays off even when the quantum method does not.

What is quantum-inspired optimization?

Algorithms that borrow ideas from quantum methods but run on ordinary computers or GPUs. They are available today and can be competitive on some scheduling and allocation problems, so we test them alongside quantum and classical approaches.

What data do you need from us?

Enough to describe real decisions: the options, the constraints, the costs and a few historical scenarios with known outcomes. We usually work from extracts or anonymized data, and the modeling sessions with your planners matter as much as the data itself.

How do we judge success?

Before we start, we agree the measures: solution quality against the baseline, time to a usable answer, cost to run, and how the result would fit your planning process. The readout reports each one.

Related services