QNilaya: Quantum-Enhanced AI Platform
QNilaya combines quantum computing with machine learning for the problems where classical AI runs out of road: complex signals, scarce data, hard structure.
Product
What QNilaya does
QNilaya uses quantum systems as learning machines. Quantum reservoirs and quantum kernels turn a small processor into a feature space that classical networks cannot reach cheaply; a classical readout does the rest.
Built for teams in science and health who work with complex time series and too little data.
Today
What exists today
Research base
QDaria has run quantum reservoir computing on IBM superconducting processors, from 4 to 156 qubits, and published the results, including where the hardware still trails classical baselines. QNilaya builds on that work.
Method
Fixed quantum dynamics as the reservoir, a trained classical readout, and a classical baseline beside every result.
Plan
MVP: the quantum reservoir forecaster
The quantum reservoir forecaster is live below. Pick a signal, set the qubits and the horizon, and press Train and forecast: a simulated quantum reservoir learns the series and is scored on unseen data against a classical echo-state network with the same number of features.
Try the chaotic Mackey–Glass series at a horizon of 5, then add qubits and watch both scores move.
Team
Team
QNilaya is a product of QDaria AS, Oslo.
Daniel Mo Houshmand
Founder & CEO, QDaria AS
Core Values
- Authenticity
- We show the work as it is: what runs today, what is planned and what is still an open question.
- Integrity
- Claims follow evidence. What we cannot source, we do not say.
- Innovation
- We work on problems where a new idea can change what is possible.
- Collaboration
- Hard physics needs many hands.
- Excellence
- We want our science, code and writing to stand up to review by others.
- Impact
- Technology should reach the people it is built for.
We show the work as it is: what runs today, what is planned and what is still an open question.