Quantum Computing

Quantum Computing for Optimization

The most complex optimization problems have no classical solution. Or they do, but it takes days.

Robot fleet routing, multi-constraint scheduling, resource allocation at scale: these are combinatorial problems that grow exponentially with the number of variables. Classical solvers work — up to a certain scale. Beyond that threshold, computation time explodes and the optimal solution becomes unreachable.

Quantum computing — and its hybrid and quantum-inspired approaches — opens a different path. We are exploring it in the field, on real cases, with measurable benchmarks.

Problems we solve

Blocked knowledge, critical dependencies and inefficiencies holding back growth

Optimization problems classical solvers cannot scale

Increasing the number of variables means exponentially increasing complexity. Traditional methods get stuck before finding the optimal solution.

Multi-constraint routing and scheduling in dynamic environments

Coordinating robot fleets in a warehouse, managing priorities, collisions, times and resources simultaneously.

Insufficient benchmarks between different algorithmic approaches

Choosing between classical solvers, quantum-inspired and deep learning without a systematic evaluation framework means deciding blindly.

Lack of integrated operational tools

Optimization does not live in a vacuum — it must integrate with real processes, field operators, control systems.

Use Cases

Real projects, measurable results

We are developing a plug-and-play software platform for optimizing warehouse logistics processes, w…

Warehouse logistics — Optimization and coordination of robot fleets

We are developing a plug-and-play software platform for optimizing warehouse logistics processes, with a focus on managing and coordinating robot fleets. The platform integrates an optimization engine for multi-constraint routing and scheduling problems, a digital twin of the warehouse for simulation and benchmarking, and a voice operational assistant.

The project experiments with and compares quantum, quantum-inspired, classical and deep learning approaches on measurable benchmarks.

Warehouse logistics — Optimization and coordination of robot fleets

Technology Corner

The architecture and technologies that make the difference

Our projects don't rely on off-the-shelf solutions. Every system is designed on the right architecture for the specific problem — and the technology choice makes the difference between a tool that works in demo and one that holds up in production.

Quantum and quantum-inspired optimization

✓ QUBO formulation
Quadratic Unconstrained Binary Optimization for routing and scheduling
✓ Quantum annealing
on simulators and hybrid classical-quantum systems
✓ Quantum-inspired algorithms
for optimal solution approximation on classical hardware
✓ Neutral atom quantum architectures (QuEra)
under exploration for combinatorial optimization

Benchmarking, simulation and operational interface

✓ Warehouse digital twin
for faithful simulation of the operational environment
✓ Systematic benchmarking framework
between quantum, quantum-inspired, classical and deep learning approaches
✓ Integrated voice assistant
STT/NLP/LLM/TTS for operator interaction in warehouse environment

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