Google Performs the Largest Chemical Simulation on a Quantum Computer Until Now

A team of scientists collaborating with Google’s AI Quantum team and some other unspecified partners has carried out the largest chemical simulation on a quantum computer until now.

In their paper issued by the journal Science, the team details their work and why they believe it was an advancement in quantum computing. Xiao Yuan of Standford University has written a Perspective piece emphasizing the potential advantages of quantum computer​ ability to carry out chemical simulations and the work by the team at AI Quantum, published by the same scientific journal.

Building an ability to predict chemical processes by simulating them on computers would reportedly be an incredible benefit to chemists, as they currently do most of it via trial and error. Prediction would create the possibility of the development of an extended range of new materials with still unknown characteristics.

Google’s Sycamore processor mounted in a cryostat. [Credit: Rocco Ceselin]
Sadly, today’s computers do not have the exponential scaling that would be necessary for such work. Because of that, chemists have been expecting quantum computer​s to someday step in and take on the role.

Validation was the Real Achievement

Today’s quantum computer technology is not yet prepared to take on such a challenge, but computer scientists are determined to get them there sometime soon. Meanwhile, giant companies like Google are investing in research focused on using quantum computer​s once they mature.

In this new attempt, the team at AI Quantum tried to simulate a simple chemical process – the Hartree-Fock approximation of a real chemical system – in this case, a diazene molecule facing a reaction with hydrogen atoms, ending up in an altered configuration.

Energy predictions of molecular geometries by the Hartree-Fock model simulated on 10 qubits of the Sycamore processor. [Credit: Google]
Understanding how to program Google’s Sycamore quantum system was not hard at all – the difficult part was figuring out how to ensure the outcome was accurate, as quantum computer​s are terribly prone to errors.

Validation was the real success of the AI Quantum team; they managed to do it by pairing the quantum system with a regular computer. It was then used to assay the results given by the Sycamore machine and come up with new parameters.

This process was repeated until the quantum computer​ managed to offer minimum value. The team also used two other verifying systems, both focused on calculating results to notice and address errors.

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