Solutions
Quantum Applications
Quantum algorithms, applied to the problems that outgrow classical computing — in a hybrid workflow that runs on simulators and GPUs today and is built for quantum hardware.
Talk to the teamWhat a quantum application is
A quantum application is a specific computational task where a quantum algorithm is expected to do better than the best classical method — and where the result matters to someone outside a physics lab. We are not interested in quantum for its own sake. We are interested in the questions a nuclear engineer, a clinical statistician or a security analyst already asks, and in whether a quantum algorithm changes the cost of answering them.
The problems that qualify share a shape. Too many interacting variables for exhaustive search. Probability distributions that are expensive to sample. Datasets whose structure hides in high dimensions, where averages conceal the signal. In each case the cost of the classical approach grows faster than the value it returns.
Why simulation, sampling and complex data
Three problem families are the natural first targets.
Simulation
Physical processes such as radiation transport are modelled by following enormous numbers of particle histories. Precision is paid for with compute: halving the statistical error costs four times the runtime.
Sampling
Many analyses reduce to drawing samples from a complex probability landscape. The cost of each independent sample sets the cost of the whole study. Quantum sampling methods aim at that cost directly.
Complex data
Clinical and biomedical datasets combine many variables per patient. Linear statistics average away the non-linear, multi-factor effects that decide whether a therapy works for a subgroup.
How it fits together
We work in a hybrid quantum-classical model. Classical systems own the workflow: they prepare and enrich the data, run the system-level models and present the results. The quantum algorithm is applied to the one step where classical cost explodes — the transport calculation, the sampling, the search for latent structure — and its output is translated back into terms a domain expert can act on.
Today that quantum step runs on quantum simulators and on GPU-accelerated platforms such as NVIDIA CUDA-Q. Execution on quantum hardware is what the work is built toward, and the same workflow is designed to carry over as the hardware matures.
The same hybrid structure appears in our own quantum computing work: a neutral-atom processor, an AI-assisted error-correction decoder running beside it, and classical control in between.
How to read our results
Results we publish come from simulators and GPU-accelerated workflows. They validate methods and workflows. They are not a demonstration of quantum advantage, and they are not evidence of clinical efficacy. We say which is which, every time.
Where we work
Two application domains, each with its own page and its own stage of development.
Nuclear engineering · Medical imaging
Nuclear Quantum
A quantum algorithm for particle transport — the Monte Carlo workload behind radiation shielding, reactor design and medical imaging. The first prototype follows gamma-ray photons through a 2-D SPECT collimator and is designed to become the oracle for quantum amplitude estimation.
Algorithm prototype · validated on a quantum simulatorExplore Nuclear Quantum →
Clinical trials · Drug discovery
CliniQuantum
Quantum-enabled analysis of clinical trial data: responder discovery, patient stratification and adaptive trial guidance, so that small responder groups are not dismissed as noise.
End-to-end proof-of-concept on real clinical data · continuous-data quantum sampling validated with GPU acceleration (NVIDIA CUDA-Q)Explore CliniQuantum →
Looking for QuantumQ Security? Quantum cybersecurity is its own solution area: see QuantumQ Security.
Have a problem with this shape?
Tell us about the computation that is slowing you down. We will tell you honestly whether a quantum approach could change its cost.
Talk to the team