Qubit Number to Simulate Molecules Reportedly Reduced by the Sorbonne and Qubit Pharmaceuticals

Officials at Qubit Pharmaceuticals report that the company has drastically reduced the number of qubits needed to compute the properties of small molecules with its Hyperion-1 emulator, a device that uses a classical computer and software to execute a quantum algorithm designed for a quantum computer, developed in partnership with Sorbonne University.

This achievement and other advances, which Qubit says raise hopes of a near-term practical application of hybrid high performance computing (HPC)-quantum computing to drug discovery, has led to the company and the Sorbonne receiving €8 ($8.7) million in funding under the France 2030 national plan for the further development of Hyperion-1.

By developing new hybrid HPC and quantum algorithms to leverage the computing power of quantum computers in the field of chemistry and drug discovery, Sorbonne Université and Qubit Pharmaceuticals state that they have succeeded, with just 32 logic qubits, in predicting the physico-chemical properties of nitrogen (N2), hydrogen fluoride (HF), lithium hydride and water, molecules that would normally require more than 250 perfect qubits. The Hyperion-1 emulator uses Genci supercomputers, Nvidia’s SuperPod EOS, and one of Scaleway’s GPU clusters.

With this proof of concept, the teams note that they have demonstrated that the routine use of quantum computers coupled with high-performance computing platforms for chemistry and drug discovery is much closer than previously thought. Nearly five years could be gained, they add, bringing researchers significantly closer to the era when quantum computers (noisy or perfect) could be used in production within hybrid supercomputers combining HPC, AI, and quantum. The use of these new computing powers will improve the precision, speed, and carbon footprint of calculations, the researchers point out.

Soon to be deployed on noisy machines

To achieve this breakthrough, teams from Qubit Pharmaceuticals and Sorbonne University developed new algorithms that break down a quantum calculation into its various components, some of which can be calculated precisely on conventional hardware. This strategy enables calculations to be distributed using the best hardware (quantum or classical), while automatically improving the complexity of the algorithms needed to calculate the molecules’ properties. In this way, explain the researchers, all calculations not enhanced by quantum computers are performed on classical GPUs.

As the physics used allows the number of qubits required for the calculations, the team, by optimizing the approach to the extreme, has managed to limit GPU requirements to a single card in some cases, according to the scientists. As this hybrid classical/quantum approach is generalist, it can be applied to any type of quantum chemistry calculation, and is not restricted to molecules of pharmaceutical interest, but also to catalysts (chemistry, energy) or materials, notes Robert Marino, PhD, CEO of Qubit Pharmaceuticals.

Next steps include deploying these algorithms on existing noisy machines to quantify the impact of noise and compare performance with recent calculations by IBM and Google and predicting the properties of molecules of pharmaceutical interest. To achieve this, the teams will deploy new software acceleration methods to reach regimes that would require more than 400 qubits with purely quantum approaches. In the short term, this hybrid approach will reduce the need for physical qubits on quantum machines, states the team.

“This work clearly demonstrates the need to progress simultaneously on hardware and software development,” says Jean-Philip Piquemal, PhD, professor at Sorbonne University and director of the theoretical chemistry laboratory (Sorbonne University/CNRS), co-founder and CSO of Qubit Pharmaceuticals. “It is by making breakthroughs on both fronts that we will be able to enter the era of quantum utility for drug discovery in the very short term.”