High Performance Computing Solutions
Quside QRNGs empower a broad range of randomized algorithms
Some of the most relevant simulation and optimization workloads rely on stochastic processes, which require an ever-increasing source of high-quality, high-speed random numbers.
Current means to generate random numbers may introduce artifacts in highly parallel simulations, while also consuming valuable computing resources. In contrast to these pseudo-random generators, physical sources of randomness may also be used. However, today’s physical RNG devices are typically slow and unavailable in HPC environments.
The Quside Randomness Acceleration cards deliver high performance and efficient randomness generation for a broad class of randomized algorithms.
Our libraries include a modified python NumPy library to facilitate adoptions for analysts and developers in multiple industries. With this functionality, users can benefit from quantum randomness without changing their codes.
Use cases
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Quside was an industrial partner in the European project QRANGE, which gathered several research groups and quantum companies around Europe to advance the field of quantum random number generation. Quside aims at integrating new discoveries into the established Randomness Metrology Suite
With the continued transition to the cloud the duty of managing the data security becomes more complex. the responsibility of data protection in the cloud still resides with the data’s owner. Keep reading to learn more about the Data Cloud protection.
What do we mean by "protect" the data or data security? There is a complete set of processes and practices created to protect the data for all the types of data security and all of them apply to the three core components of data security: Confidentiality, Integrity and Availability.
