Quantum computing

Quantum optimisation.

Optimisation is where quantum computing is most often sold to business, because every business has a scheduling or routing problem. It is also where the classical baseline is strongest, and where the comparison is most frequently rigged by omission.

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The two approaches

Quantum annealing

Special-purpose hardware that encodes a problem as an energy landscape and lets a physical system settle toward a low-energy configuration. Commercially available at scale for longer than gate-based machines, and restricted to problems expressible in a specific quadratic form — which is a real constraint on what you can pose to it.

QAOA on gate-based hardware

A variational algorithm alternating problem-specific and mixing operations, with a classical optimiser tuning the parameters. More general than annealing and currently limited by circuit depth: the versions that fit on today's machines are shallow, and shallow versions have not shown an advantage.

Why the classical baseline keeps winning

Combinatorial optimisation has had seventy years of intense classical attention, driven by logistics, chip design, airline scheduling and finance — industries with enormous incentives and enormous budgets. The result is a body of solvers that are extremely good: commercial mixed-integer programming solvers, specialised heuristics, simulated annealing, and metaheuristics tuned over decades against real instances.

Comparisons against quantum methods often skip that baseline, or use a weak version of it. A demonstration that a quantum annealer beats naive random search is not evidence of anything. The comparison that matters is against a well-configured commercial solver given the same time budget on the same instance — and in the published comparisons we have reviewed that survive scrutiny, the classical solver generally wins or ties.

There is a second, quieter finding worth knowing about. Repeatedly, a proposed quantum advantage has prompted researchers to look harder at the classical side and find a better classical algorithm. The field calls this dequantisation, and it has happened often enough to be a reasonable prior. Sometimes the most valuable output of a quantum proposal has been the classical algorithm it provoked.

How to evaluate a vendor claim

Four questions, in order. They are not hostile, and a serious vendor will have answers ready.

1. What was the classical baseline? Named solver, version, configuration, and how much time it was given. If the baseline is unnamed or the time budget is unequal, the comparison carries no information.

2. Is the problem instance representative? Quantum methods can be tuned to instances with structure that suits them. Ask whether the demonstration used your kind of instance at your kind of size, and whether the result held across a distribution or on one example.

3. Does the comparison include preparation time? Encoding a problem into a form the hardware accepts is real work with real latency, and it is frequently excluded from the quoted runtime.

4. What happens as the problem grows? A method that ties at small scale and degrades faster than the classical one as instances grow is not a path to anything. Ask for the scaling curve, not the single data point.

Our advice for a business with a real optimisation problem is unambiguous: solve it classically first, properly, with a good solver and someone who knows how to configure it. In our experience the achievable improvement from that alone is typically larger than anything currently on offer from quantum hardware, and it is available now.

Questions

Questions about Quantum Optimisation.

Are there real production deployments of quantum optimisation?

There are real pilots, real published collaborations and real internal capability-building programmes, several with large well-known companies. What we are not aware of is a case where quantum optimisation is load-bearing in production and beating a properly configured classical solver on the same problem. If you find one, read the classical baseline section carefully first.

Is quantum annealing the same as a quantum computer?

It is a distinct architecture built for one class of problem, rather than a general-purpose gate-based machine. Vendors sometimes blur this. It is worth keeping straight because the capabilities, the limits and the algorithms are all different.

Should we run a proof of concept?

If the goal is capability-building and learning, and the budget is small and honestly labelled as research, it is defensible. If the goal is a better answer to a business optimisation problem this year, spend it on a commercial solver and someone who can configure it — you will get a better result and you will get it sooner.