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Applied quantum computing, London

Quantum Computing Research and Partnerships

We test whether quantum methods beat the best classical solvers on real planning problems, and report the answer either way.

Discuss a collaborationSee the method
Selected for the NQCC SparQ Access Industry Channel. Access, not a grant.
Interactive Bloch sphereThe standard picture of one qubit. The top pole is state zero, the bottom pole is state one. The orange arrow is the qubit's state. Drag it, or use the sliders, to change the odds of measuring zero or one.

4 November 2026

Selected for the NQCC SparQ Access Industry Channel

The National Quantum Computing Centre named Softomate Solutions among nine organisations selected for the SparQ Access Industry Channel. The channel gives selected companies free access to commercial quantum computing platforms, so they can explore, experiment with and trial early-stage use cases.

It is access to resources, not a grant or a cash prize. Our first step is the classical baselines, so that any later quantum result has an honest comparison.

Read the NQCC announcement (opens in a new tab)See our first use case

The question

Why is optimisation hard, and where might quantum help?

Every extra decision doubles the number of possible plans. Classical solvers are very good at cutting that space down. We are testing whether quantum methods can do better.

  1. Can a quantum or hybrid method match the best classical solver at the same problem size?
  2. What do setup, queue time and hardware access add to the total time?
  3. Could it run inside real planning software?

First problemSupply chain disruption recovery. Scheduling, allocation, logistics and energy planning are candidates.

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.

Method

How we keep a test fair

We set the classical answer first, and we write the success rule before we run anything.

  1. Frame

    Model the decision.

  2. Benchmark

    Classical solvers set the bar.

  3. Test quantum

    Hybrid methods, where they fit.

  4. Measure

    Report it either way.

  5. Engineer

    Build what works.

Classical

Optimiser

Tunes the parameters. Sets the benchmark with MILP and CP-SAT.

Quantum

QAOA circuit

  1. Start in an equal blend, |+⟩
  2. Cost layer e−iγHC
  3. Mixer layer e−iβHB
  4. Repeat the two layers p times
  5. Measure and read off a plan
The quantum approximate optimisation algorithm (QAOA), in outline. The classical optimiser and the quantum circuit take turns until the plan stops improving.

Evaluation protocol, draft v0.1

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.
  • The same problems for every method
  • Tuned MILP and CP-SAT as the classical baseline
  • Repeated runs, with the spread reported
  • Every result reported, including failures
  • Instance sizes and thresholds agreed with academic partners before any run
  • Protocol and results shared with partners

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

Exploratory. No results published yet.

  1. Protocol v0.1Agreed with partners.
  2. Classical baselinesOn shared instances.
  3. Hybrid experimentsWhere access allows.
  4. A written reportWhichever way it goes.

Dates are agreed with partners and depend on resource access. Nothing here promises a result.

Progress

What has happened so far

  1. Selected for the NQCC SparQ Access Industry Channel. Details

  2. Evaluation protocol, draft v0.1, written and published on this page. Read it

  3. First classical benchmark run on a toy supplier-failure problem. See the numbers

Work with us

Talk to us

Researchers First priority

  • We bring realistic problem formulations, strong classical baselines and engineering for repeatable runs.
  • We would like algorithm expertise, complementary methods or hardware access, and rigorous design.
  • Co-supervised student projects and joint bids are welcome.
Start a research conversation

Technology partners

  • You provide a quantum platform or hardware access: we bring a real use case for your toolchain.
  • You build a quantum product: we offer software engineering, scoped separately from any research.
Talk about your platform

Funders and consortia

  • A UK software partner for the build and evaluation work packages of a joint bid, or a paid pilot.
  • We are early stage and not yet externally funded. The NQCC channel gives access, not money.
Discuss a joint bid

Industry

  • You own a hard planning problem and have data we can use.
  • We set the classical baseline first. A quantum step comes only if it earns its place.
Describe your problem

Probably not for you if you want a quantum hardware vendor, a guaranteed quantum advantage or a finished product today.

Capabilities

What we can do for a research partner

Quantum research needs software that can be tested, repeated and connected to real operations. This is what we build.

  • Problem modelling

    We turn a planning decision into MILP and CP-SAT models with explicit constraints and a measurable objective.

  • Reproducible benchmarking

    Shared instances, fixed seeds and logged runs, so anyone can re-run an experiment and get the same answer.

    Our test engineering

  • Hybrid algorithm engineering

    We implement and tune QAOA-style methods, on simulators first and on hardware where access allows.

  • Enterprise integration

    We connect results to ERP, inventory and planning systems through APIs, so a method can be used, not just published.

    API and integration work

  • Production-grade delivery

    Tested, maintainable code, so a research prototype can grow into a product.

    Our software development

  • Data pipelines

    We clean, version and move the data the models depend on, so every run starts from the same inputs.

Deen Dayal Yadav

Research lead

Deen Dayal Yadav

Founder and Director, Softomate Solutions

LinkedIn (opens in a new tab)

Questions

What people ask first

What do you want from academic partners?

Expertise in quantum and hybrid optimisation, complementary methods or hardware access, and rigorous experiment design. We offer realistic problems, benchmark engineering and reproducible software.

Will you publish your results?

We intend to share methods and results with partners, including negative ones. Publication terms are agreed in writing with any academic partner before work starts.

Are you looking for funding?

We are early stage and not yet externally funded. We welcome joint grant bids where we are a project partner, co-supervised student projects and paid pilots. Every arrangement is agreed in writing.

Is Softomate a quantum hardware provider?

No. We are a London software engineering company. Where quantum hardware is needed we expect to use resources made available by others, such as the NQCC SparQ Access Industry Channel. We contribute the modelling, software, testing and integration.

Have you demonstrated quantum advantage?

No, and we make no such claim. We have no published results yet. Our success rule is written before any run, so any result, positive or negative, can be trusted.

Is this only about supply chains?

No. Quantum is a capability: the same method applies to any suitable planning problem. Supply chain disruption recovery is our first test. Scheduling, allocation, logistics and energy planning are candidates.

Get in touch

Let's explore a collaboration

Tell us who you are and what you would like to explore. We reply by email.

A few lines on the problem or the partner you are looking for.

We use your details only to answer this enquiry. See our privacy policy.

Deen Dayal Yadav, founder of Softomate Solutions

Deen Dayal Yadav

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