250.00 

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  • Quantum processing units developed by french startups !
  • Payments every 6 months !
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Quantum processing units are made of hundreds of atomic qubits in 2D and 3D arrays, and their programming environment to address customers’ needs in computing and simulation of quantum systems. An heir to the French academic excellence in cold atoms physics.

A scalable, reliable and energy efficient solution to solve the most complex computational problems in science and industry.

A powerful platform for quantum simulation

The devices enable the creation and engineering of synthetic quantum systems, which in turn provides valuable insight into complex many-body problems.

Through the manipulation of the atoms’ positions and of their interactions, quantum processing units are particularly suited for the simulation and study of spin systems, as the different types of interactions between atoms allow a natural mapping onto various quantum spin models.

Moreover, this unprecedented level of control over quantum many-body systems empowers the discovery of new properties and phenomena.

Accelerating High-Performance Computing

Through collaborations, the aim is to incorporate quantum processors into high-performance computing environments, thereby setting the stage for an era of hybrid quantum-HPC systems with potential for short-term, real-world applications.

Technology and applications

Neutral atom device architectures are unique in many ways, not only in comparison with classical devices in general but also with respect to its quantum counterparts. For example, in relation to other quantum devices, neutral atom platforms can reach quantum registers with a larger number of qubits and higher connectivity with relative ease.

The aim is to improve the performance and capabilities of quantum devices, while simultaneously searching for ways to leverage their full potential. This approach results in the development of applications that are specifically tailored for neutral atom devices. Many more unique solutions are still to come and are always open to collaborations with academic and industrial partners.

Exploring quantum simulation

The most promising application of QPU is Quantum Simulation, where the quantum processor is used to gain knowledge over a quantum system of interest. As Richard Feynman already pointed out in the last century, it seems natural to use a quantum system as a computational resource for quantum problems. Pure science discovery will benefit from neutral atom quantum processors, and fields of applications are numerous at the industrial level, including for example the engineering of new materials for energy storage and transport, or chemistry calculations for drug discovery.

Surpassing the Classical Simulation Threshold

Recent advancements have allowed to reach unprecedented system sizes, with quantum registers of 100+ atoms. This number of interacting quantum particles enables the simulation of a many-body quantum system’s dynamics well beyond the capabilities of state-of-the art classical methods.

Solving Hard Optimization Problems

Beyond the simulation of scientific processes, quantum processors can already be used to solve hard computational problems, for which classical computers are inefficient. One important example is the native resolution of a well-known graph problem, Maximum Independent Set (MIS). This problem, which has various direct applications in network design or finance, becomes hard to solve on a classical computer when the size of the graph grows.
In an undirected graph composed of a set of vertices connected by unweighted edges, an independent set is a subset of vertices where no pair is connected by an edge. The objective of the MIS problem is to find the largest of such subsets.
The MIS problem can be tackled by using an ensemble of interacting cold neutral atoms as a quantum resource, where each atom represents a vertex of the graph under study. Interestingly, the physical interactions encoded in the Hamiltonian constrain the dynamics to only explore independent sets of the graph under study, then leading to an efficient search in the set of possible solutions.

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