London/Barcelona – Quside Technologies S.L. (“Quside”), a startup delivering quantum technologies for safer connectivity and advanced computation, announces the availability of an optimized version of NumPy to accelerate randomized workloads in a broad range of fields, including finance, science, logistics, and pharmaceuticals.

Accelerated version of NumPy

The forked version of NumPy is built on top of the Quside Randomness Acceleration Platform, which combines the company’s phase-diffusion quantum random number generation (QRNG) products, with advanced randomness processing tools.

The solution released by Quside today allows developers, data analysts, and AI researchers to improve their randomized workloads in one of Python’s most widely used environments with zero changes in code required.

Flexible Access & Future Outlook

The acceleration capabilities can be obtained in any compute instance powered by the Quside™ Randomness Acceleration Platform, and can be accessed in private cloud deployments and remote virtual environments supported by Quside.

New features will be added in future releases, offering improved acceleration performance, and additional advanced features and tools for Monte Carlo simulations, risk assessment, and randomized workloads in general. The technology has been successfully validated in several customer implementations and is available for testing today.

“Making the integration of our products easy to our customers is a constant effort for everyone at Quside. I’m thrilled for this forked, adapted version of the NumPy library, which provides the simplest possible form for end users to start benefiting from Quside’s new randomness generation and acceleration capabilities,” said Carlos Abellan, CEO of Quside.