Enterprise quantum software company Zapata Computing has partnered with the UK’s University of Hull to leverage each other’s expertise to detect signatures of life in deep space.
The partnership will support research to repurpose Zapata’s quantum workflow platform Orquestra in order to assist in the development of highly accurate astrophysical models and applications. Dr David Benoit, senior lecturer in Molecular Physics and Astrochemistry at the University of Hull, commented:
“Although quantum computers are an emergent technology and cannot yet outperform classical hardware, Zapata has made it possible to generate valuable insights from the Noisy Intermediate-Scale Quantum (NISQ) devices currently available.”
Dr Benoit added that Orquestra enables the researchers to build future-proof applications that don’t just work with NISQ devices today but are also capable of leveraging the more powerful quantum computing devices of the future.
Improving model accuracy
Sharing details about hope the researchers plan to leverage Zapata’s quantum expertise, the researchers explain that they want to build on top of the work of MIT researchers who in 2016 drew up a list of over 14,000 molecules that could indicate signs of life in exoplanets’ atmospheres.
The University of Hull researchers now aim to generate a database of detectable biological signatures of these molecules by using new computational models of molecular rotations and vibrations. However, little is currently known about how these molecules vibrate and rotate in response to infrared radiation generated by nearby stars.
In order to detect them, the researchers need to build highly accurate models based on extremely accurate calculations, which is touted as one of the fortes of quantum computing. Christopher Savoie, CEO and co-founder of Zapata Computing, said:
“The research being done by Dr Benoit and his colleagues has the potential to redefine our place in the universe, and we’re humbled that Orquestra will have a supporting role.”
Orquestra evaluation for the research is currently scheduled to run for eight weeks before the team publishes an analysis of the research.