In our latest fireside chat below, BIOS IT General Manager Glenn Rosenberg sat down with SECQAI CEO Rahul Tyagi to talk about their latest product, the QAI API, enabling our customers to accelerate their existing compute, without making costly infrastructure changes.

By using Truly Random numbers from a Quantum source, they're able to help speed up the training and deployment of your Artificial Intelligence, Simulation and Modelling use cases.

Across the interview they touch on exciting use cases across different industries, i.e financial services where pseudorandom numbers are currently used throughout calibration and back testing of models (e.g. Hamiltonian Markov Chains and Martingale functions, such as Hull and White, Vasicek etc), Automotive (e.g. a whole range of CFD modelling) and Healthcare (e.g. driving ahead novel drug discovery).

By changing only a couple of lines of code, you can inject True Random Numbers into your applications, unlocking a significant uptick in efficiency and performance, speeding up development and cutting the carbon output of your existing compute infrastructure.

Pseudorandom numbers are used everywhere - swap them out for True Random Numbers to see a huge impact on efficiency.

Interested to know more? We are excited to be working with SECQAI and there are plenty of Proof of Concept opportunities to test out the difference yourself.

Want to know more about the use cases? Pseudorandom numbers are used everywhere - swap them out for True Random Numbers to see a huge impact on efficiency.

• Financial Services: Pseudorandom numbers are currently used throughout calibration and back testing of models across Financial Services (e.g. Hamiltonian Markov Chains and Martingale functions, such as Hull and White, Vasicek etc).
• Automotive: using OpenFOAM to model the airflow over vehicle parts during the design uses significant volumes of pseudorandom numbers, often in ways you least expect it! All the while the explosion of electric vehicles means   understanding electrolyte fluid flow around batteries using CFD tools is incredibly important for manufacturers
• Healthcare & Pharma: advanced modelling for drug discovery (e.g. Grand Canonical Monte Carlo & Molecular dynamics), as well as more advanced AI methodologies, consume Pseudorandom numbers at scale. Advanced AI imaging and diagnosis tools require similar inputs, helping to improve model generalisation

But it's not restricted to these verticals, with all industries leveraging AI, simulations and modelling. There are many other opportunities ranging from large language models like chat GPT, to the Generative Adversarial Network's used for image recognition.

As our models become more complex they'll require even more compute... accelerate your existing compute with SECQAI's Truly Random Numbers.

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