Status
By the end of July 2026, the feasibility study and the Proof-of-Concept had been completed. The research team is working on scaling up to the size of a modern warehouse with tens of thousands of goods, while the sales team is in contact with potentially interested customers.
FLAGSHIP PROGRAMME
Warehouse inventory placement
Summary
Quantum-computing powered optimisation algorithms have been developed to minimise the risks and maximise retrieval efficiency inside a warehouse by providing the most suitable inventory allocations. This is a flagship quantum application discovery programme partially sponsored by the National Quantum Computing Centre and the Science and Technology Facilities Council under the SparQ programme.
The problem
Warehouse operators face two challenges — increasing operational efficiency and reducing risks. Operational efficiency involves optimising inventory allocation to minimise travelling around the warehouse. Managing risks inside a warehouse includes risks of damaging goods due to unforeseeable circumstances, such as natural disasters, and operational and safety-related risks, such as human injuries.
Right now, inventory allocations are assigned manually or assisted by logistics software, which are either suboptimal for efficiency or not risk aware.
Solution
A Proof-of-Concept was conducted under the consideration of these two challenges. The quantum research team used quadratic binary or integer combinatorial optimisation and developed quantum, classical and hybrid optimisation algorithms that incorporate spatially heterogeneous risk into the operational efficiency objective function, maximising efficiency while minimising risk of losses.
The algorithms were tested with synthetically generated data on quantum and classical hardware respectively. The results show that the cost combining expected loss due to risks and operation costs reduced by at least half over time when using an optimisation algorithm compared to a random allocation. Quantum-classical hybrid solvers outperform all other solvers 99% of the time by being the fastest and finding the best solutions.