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Quantum computing has the potential to help industrial chemists answer important questions and make game-changing scientific breakthroughs.
Chemists at BMW, for instance, have been using quantum chemistry to simulate electrode reactions in hydrogen fuel cells, with the ultimate aim of designing catalytic converters that aren’t based on platinum. Meanwhile, the oil and gas company TotalEnergies has used quantum chemistry to model metal organic frameworks for carbon capture — an important part of the industry’s response to climate change.
To tackle their respective challenges, both BMW and TotalEnergies worked with Quantinuum, the quantum computing company formed just about six months ago when Honeywell Quantum Solutions merged with Cambridge Quantum. Quantinuum worked with these and other industry partners to develop InQuanto, the quantum chemistry software platform that’s now available.
The standalone platform is built around a core set of quantum algorithms and has a series of tools and capabilities to help chemists get past the challenges that currently come with quantum computing. It’s designed for industrial chemists — whether they are just starting to investigate what quantum can do for their business, or they already have a specific use case in mind.
“The aim of the tools is to really help them start to develop their quantum use cases,” Jenni Strabley, Quantinuum’s senior director of offering management, said to ZDNet. “It’ll be some time before the quantum hardware is capable of solving these problems better than a classical computer, but it’s important for industries to get started early. They need to start investigating and using these tools early and iterative to understand their quantum use cases and how quantum is going to bring value to their company.”
InQuanto is a Python-based platform with four main capabilities. First, it lets users easily mix and match quantum algorithms with different subroutines and noise mitigation techniques. By putting these elements together, chemists can create custom quantum workflows tailored to specific use cases.
The platform also offers advanced noise mitigation techniques, developed in partnership with customers, that are chemistry-specific. This is key for using current quantum machines.
Next, InQuanto uses fragmentation techniques to break down large, industrially-relevant systems into smaller fragments that can run on today’s small-scale quantum machines. This can help customers like TotalEnergies, for instance, break down metal organic frameworks into smaller systems that can be simulated with quantum machines. It could also help pharmaceutical companies break down complex drug protein interactions.
Lastly, the platform uses Quantinuum’s open-source Python toolkit TKET to reduce the computational requirements for electronic structure simulations. TKET can take a customer’s algorithm or code and target it to different backend systems. In other words, customers can use Quantinuum’s H-series hardware, quantum machines from other providers, or simulation platforms from providers like Microsoft or AWS.
“You can seamlessly target these different backends by changing one line of code, which makes InQuanto very attractive for users,” Simon McAdams, Quantinuum’s quantum chemistry product lead, said to ZDNet.
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