Our first quantum use case

Quantum Supply Chain Disruption Recovery

When a supplier fails, a planner has hours to choose a recovery plan. We are testing whether quantum and hybrid methods can match the best classical solvers at finding it.

Discuss a pilot See how we test it

Selected for the NQCC SparQ Access Industry Channel. Access, not a grant.

A supply network after one supplier fails.

4 November 2026

Selected for NQCC SparQ Access

The National Quantum Computing Centre named Softomate Solutions among nine organisations selected for the SparQ Access Industry Channel: free access to commercial quantum computing platforms, to explore and trial early-stage use cases.

This is the first problem we are working on, starting with the classical baselines. The access is a resource, not a grant or a cash prize.

Read the NQCC announcement (opens in a new tab)

The problem

When a supplier fails, what is the best recovery plan?

A key supplier cannot deliver, and orders still have to be served. Use stock, switch to a substitute part, buy elsewhere, move work to spare capacity, or delay the less urgent orders. Every choice changes the others.

The plan has to balance:

Stock on hand
Substitute parts
Alternative suppliers
Production capacity
Lead times
Order priorities
Total cost

Decisions to make (suppliers, jobs, routes)20

1,048,576

possible yes or no plans, before any rule is applied

Brute force, for illustration. Real solvers prune most of this space, which is exactly why the classical baseline matters.

How we test it

Benchmark first, then try quantum

We set the classical answer first, so any quantum result is measured against something real.

Model
Turn one real disruption into a precise model.
Benchmark
Tuned classical solvers set the bar.
Test quantum
Hybrid methods, where they fit.
Measure
Compare quality and total time.
Connect
Plug what works into your systems.

The success rule, written before any run

A quantum or hybrid method counts as useful only if it reaches the best classical solution quality faster than a tuned classical solver at the same problem size, including setup, queue and hardware access time.

We measure: solution quality, time to solution, scaling and practical overhead.

Exploratory. No results published yet.

The same method applies to other planning problems. See the research overview

A first benchmark

What the classical bar looks like

To show the scale, we built a small supplier-failure problem and solved it three ways on a laptop. This is classical only. It is not a quantum result.

A toy supplier-failure problem, solved three ways
Recovery actions	Possible plans	Check every plan	Smart classical search	Greedy rule: cost above best
10	1,024	under 1 ms	0.04 ms	+18.9%
14	16,384	9 ms	0.22 ms	+9.8%
18	262,144	152 ms	0.10 ms	+17.7%
22	4,194,304	2.9 s	0.57 ms	+18.5%

Checking every plan doubles with each extra action: 2.9 seconds at 22 actions.
A smart classical search finds the same best plan in under a millisecond.
A simple greedy rule is quick, but 10 to 19% more expensive than the best plan.

That is the bar a quantum method has to clear: a tuned solver, not brute force.

Measured on 9 November 2026 on a Windows laptop, Python 3.14, one core, fixed random seed. Each action covers some units of the shortfall at some cost, and some pairs of actions conflict. Both exact methods returned the same best plan every time. The generator and protocol will be shared with partners.

Work with us

A pilot on your own scenario

If supplier failure is a real cost for you, we can run the same method on your disruption.

Scenario workshopWe agree one real disruption to model, and what a good plan looks like.
Model and baselineWe build the model and set the classical benchmark on your data.
Hybrid testWe test hybrid methods against that benchmark, on simulators first and on hardware where access allows.
Written reportYou get the findings whichever way they go, and a view on whether to take it further.

A good fit

You run manufacturing, distribution or logistics
Supplier failure has cost you money or service
You can share a real or realistic scenario
You want evidence, not a demo

Probably not a fit

You need a finished product today
You want a guaranteed quantum advantage
You have no data to model, even anonymised

Questions

What people ask first

Is this a product I can buy today?

No. It is a research programme with optional paid pilots. Today we can model your disruption scenarios and benchmark them against strong classical solvers. A quantum step comes only if it earns its place.

Do you promise a quantum advantage?

No. We have no published results yet, and we write the success rule before any run. If a tuned classical solver wins, we say so.

What data do you need?

A real or representative disruption scenario: suppliers, parts, stock, capacity, lead times and order priorities. Anonymised or synthetic data is fine to start.

Can the result connect to our ERP?

Yes. That is the last step of the method. If an approach works, we connect it to your ERP, inventory and planning systems through APIs.

How much does a pilot cost?

It is scoped after a first conversation and agreed in writing. We do not quote a price before we understand your scenario.

Get in touch

Tell us about your disruption

A few lines on your supply chain and what a failure costs you. We reply by email.

Quantum Supply Chain Disruption Recovery

Selected for NQCC SparQ Access

When a supplier fails, what is the best recovery plan?

Benchmark first, then try quantum

1Model

2Benchmark

3Test quantum

4Measure

5Connect

What the classical bar looks like

A pilot on your own scenario

A good fit

Probably not a fit

What people ask first

Tell us about your disruption

Thank you. Your enquiry has been sent.

Model

Benchmark

Test quantum

Measure

Connect