Decision optimisation for energy and logistics

Complex Operational Decisions. Solved With Evidence.

Qatalyst builds decision optimisation products for energy and logistics. Our SOLA decision tool helps energy teams decide where to place BESS, targeted grid expansion and flexible capacity under uncertain demand and network conditions, and we are expanding our logistics tools to optimise allocation, scheduling and routing. Classical by default. Quantum only where benchmarking shows it adds value.

Powered by the Qatalyst Decision Solver Engine: classical-first, solver-agnostic and quantum-ready.

Aerial view of a freight depot at night

From Constraints To Decisions.

A shared four-stage workflow for Sola and logistics: capture the decision, formulate the constraints, compare solver routes and deliver a traceable recommendation for human review.

Products Built On Evidence.

Decision tools presented alongside the research and challenge results behind them. The evidence shown here is benchmark evidence, not a claim of customer deployment.

Isometric illustration of battery storage, wind generation and a substation feeding a transmission line
Product · Energy

SOLA decision tool.

The SOLA decision tool helps identify where to place BESS, where targeted grid expansion may unlock value, and which locations remain robust as demand, curtailment and connection conditions change.

View product evidence

On the IEEE 118-bus robust BESS siting objective, D-Wave's Stride nonlinear hybrid solver won five of five equal-time comparisons under the stated time budget.

The study used weather scenarios, N-1 screening and PTDF-based DC power flow. Smaller instances were checked against an exact reference. This supports a hybrid benchmarking approach; it is not a claim of quantum advantage.

EVIDENCE: Global Industry Challenge 2026, DoE track, Phase 3 finalist. Compute provided by D-Wave. Peer-reviewed paper accepted at IEEE Quantum Week 2026, workshop WKS40. Results hold under the stated robustness assumptions and time budget.

Isometric illustration of distribution centres linked by routes to order destinations
Product · Logistics and supply chain

Logistics Allocation And Planning.

Allocate orders and capacity across facilities, with the same decision architecture extending to scheduling, routing and disruption response.

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Built for the WISER Quantum Challenge 2026: 1,109 orders, eight distribution centres and 25,193 order lines. The exact MILP reference identified an uplift of roughly 530,000 over the do-nothing baseline under the stated assumptions, with an alternative planning rule reaching 633,000. The optimum is near-certified.

In a separate routing experiment run on IonQ Forte during a paid IonQ Qollab Creative Challenge engagement, classical simulated annealing beat the quantum method. We publish adverse results because solver selection only has value when losses are visible too.

EVIDENCE: open challenge track. Benchmark work on supplied data, not a customer deployment. Freight routing under disruption is an active research extension and is not yet published.

Send Us A Message.

Have an energy portfolio or logistics decision you want modelled? Or do you want to explore our SOLA decision tool?

Discuss a pilot

If you are choosing energy investments or allocating logistics capacity through spreadsheets and disconnected rules, we can model one decision and benchmark the result against your current approach. Classical first, with the comparison made visible either way.

Research collaboration

We work with academic and industrial partners on optimisation under uncertainty, in energy networks and freight. Get in touch if you have a benchmark, a dataset, or a problem you think is hard.