Where it applies
Classical solvers struggle as combinatorial problems grow. Quantum annealing maps discrete decisions onto a physical energy landscape and samples its lowest-energy states, which makes it a strong candidate for the problems below.
01
Fleet routing that raises utilisation and cuts empty miles, multi-modal network balancing, and re-planning of warehousing, inventory and distribution when supply is disrupted. Traffic-flow models for congested city grids and autonomous fleets.
02
Shift rotations that respect skills, certifications, agreements and sudden absences; production lines sequenced for less idle time and fewer tool changes; coordinated paths for robots and AGVs without deadlocks or collisions.
03
Asset selection, index tracking, rebalancing and tax-loss harvesting formulated as QUBO models, with transaction costs, lot sizes and liquidity constraints built into the model rather than added afterwards.
04
Magnetic and spin-glass models, phase transitions and other many-body systems simulated natively on the annealer, to narrow down what is worth making before anything goes to the lab.
05
Conformer search and candidate screening for drug discovery, and quantum sampling inside machine-learning pipelines, such as feature selection and training generative models over rugged search spaces.
How it runs
We map each discrete decision to the qubit biases and couplings of a QUBO or Ising model. Which part runs on the quantum processor, which on classical hardware, is decided by benchmark on your data, not by preference.
Scheduling, routing and allocation problems that fit the processor are embedded directly on the Advantage2 QPU, which returns low-energy candidate solutions in milliseconds.
Enterprise-scale problems run on D-Wave’s hybrid solvers, which pair classical algorithms with QPU sampling. The nonlinear hybrid solver handles up to two million variables and constraints.
Fast-anneal control keeps the anneal coherent for research into non-equilibrium physics and spin-glass models, and for rapid sampling.
Every problem is also solved with the strongest classical method we can find: mixed-integer programming, constraint programming, heuristics or GPU solvers. You see both results side by side.
The platform
Our default stack is D-Wave’s Advantage2 annealer, reached in real time through the Leap cloud service and programmed in Python with the open-source Ocean SDK. You need no cryogenics lab and no hardware on site. Because we are vendor-neutral, your models stay portable: if another annealer, a gate-model machine or a classical solver gives the better answer, that is what we deploy.
D-Wave, Advantage2, Leap and Ocean are trademarks of D-Wave Quantum Inc. We use D-Wave’s services as a customer; Algorithmic BrAIn is not affiliated with or endorsed by D-Wave.
Evidence
Our clients are protected by our strict, standard non-disclosure agreements, so we are unable to disclose them publicly, even because of our anti-phishing cybersecurity policies. Each result is therefore described by sector and scale only, and benchmarked against the classical baseline agreed with each respective client at the start.
Mid-cap investment bank, EU
Portfolio construction under real constraints such as transaction costs, lot sizes and liquidity, formulated as a quadratic model and solved with hybrid quantum-classical solvers alongside the bank’s existing optimiser.
Result
[CLIENT-SUPPLIED]
Logistics operator, Malta
Daily fleet route planning across a dense island network, formulated so delivery windows and vehicle limits are enforced, and run as a hybrid workflow against the existing planning baseline.
Result
[CLIENT-SUPPLIED]
Small car-parts manufacturer
Production scheduling and line sequencing to cut idle time and tool changes, modelled as a constrained optimisation problem and benchmarked against the plant’s current planning method.
Result
[CLIENT-SUPPLIED]
How we engage
Every engagement starts with a problem and a classical baseline, and moves forward only when the numbers justify it.
Our quantum applications team works with your data scientists to find the bottlenecks worth solving and formulates them as QUBO, Ising or constrained quadratic models.
You receive
We run the model on Leap with your real data and benchmark it side by side against your current classical and GPU solvers.
You receive
We integrate quantum calls into your operational software for automated, continuous optimisation, and monitor quality and cost.
You receive
