Quantum software company Zapata Computing company has partnered with the UK Hull University to take advantage of their respective skills to detect life signatures in deep space.

The partnership will support research to reuse the workflow platform Quantata Zapata Orquestra to assist in the development of very accurate models and applications of astrophysics.

“Even though the quantum computer is a technology that appears and has not been able to outperform classic hardware, Zapata has made it possible to produce valuable insights from the noisy (NISQ) (NISQ) available,” Dr. David Benoit, senior lecturer in molecular and astrochemical physics at the University of Hull.

Dr. Benoit added that Orquestra allowed researchers to build future evidence applications that did not only work with NISQ devices at this time, but were also able to utilize stronger quantum computing devices in the future.

Increase model accuracy

Sharing details about the expectations of the researchers plan to take advantage of the quantum expertise of Zapata, the researchers explained that they wanted to build on the work of MIT researchers who in 2016 compiled a list of more than 14,000 molecules that could show signs of life in the exoplanets atmosphere.

Hull University researchers are now aimed at producing a biological signature database that can be detected from these molecules by using a new computing model of molecular rotation and vibration. However, a little now known about how these molecules vibrate and rotate in response to infrared radiation produced by the closest stars.

To detect them, the researchers need to build a very accurate model based on very accurate calculations, which are mentioned as one of the quantum computations of Forte.

“Research conducted by Dr. Benoit and his colleagues have the potential to redefine our place in the universe, and we feel humble that Orquestra will have a supporting role,” said Christopher Savoie, CEO and one of the founders of Zapata Computing.

Orquestra evaluation for this study is currently scheduled to run for eight weeks before the team publishes research analysis.