At Query Machines, we are pioneering quantum-native AI architectures, based in Delft, Netherlands.
We map tokens into Hilbert space, gaining access to exponentially high dimensions. Through quantum entanglement, our models capture complex, long-range relations between tokens that classical architectures fundamentally cannot represent.
Where classical transformers are limited to pairwise attention in fixed-dimensional space, quantum states explore the full combinatorial structure of language simultaneously.
We can directly measure and observe how quantum states evolve in Hilbert space. Every computation leaves a visible trace. It is not a black box, but a transparent process you can follow step by step.
Quantum measurements give us native access to the internal decision-making of our models, enabling a level of interpretability that is structurally impossible in classical deep learning.
Quantum gates are essentially free. While classical AI demands massive GPU clusters consuming megawatts of power, quantum operations achieve computation through physical state evolution at a fraction of the energy cost.
As quantum hardware scales, the energy advantage grows exponentially and not linearly. This is the path to sustainable AI at scale.