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.”
Carlos Abellan, CEO Quside
Quside delivers quantum technologies for safer connectivity and advanced computation. A spin-off of ICFO, and operating out from Barcelona, Madrid, and London, Quside commercializes innovative quantum random number generators and quantum-enhanced solutions today. Quside is a founding member of the Quantum Industry Consortium (QuIC) and partner of European Quantum Flagship projects CiViQ and QRANGE
Co-founder & CEO
PhD in quantum technologies at ICFO, where he developed the quantum randomness technologies that were transferred to Quside. 10 years of experience in quantum and photonics technologies, co-inventor of multiple patent families and co-author of 15+ papers in top scientific journals. Received the award MIT Innovators Under 35 Europe.
A research collaboration between Quside, ICFO, and others, has shown how using quantum random number generators provide the required quality and efficiency for safely running even the most complex stochastic simulations.
Quantum random numbers for physics-inspired optimization problems
Making decisions is commonly a challenge due to the uncertainty and overwhelming information needed to deal with a problem: from every engineering design, data analysis or most business decisions to...