Ten miles in from Lengthy Island’s Atlantic coast, Shinjae Yoo is revving his engine.

The computational scientist and machine studying group lead on the U.S. Division of Power’s Brookhaven Nationwide Laboratory is certainly one of many researchers gearing as much as run quantum computing simulations on a supercomputer for the primary time, due to new software program.

Yoo’s engine, the Perlmutter supercomputer on the Nationwide Power Analysis Scientific Computing Heart (NERSC), is utilizing the newest model of PennyLane, a quantum programming framework from Toronto-based Xanadu. The open-source software program, which builds on the NVIDIA cuQuantum software program growth package, lets simulations run on high-performance clusters of NVIDIA GPUs.

The efficiency is vital as a result of researchers like Yoo must course of ocean-size datasets. He’ll run his applications throughout as many as 256 NVIDIA A100 Tensor Core GPUs on Perlmutter to simulate about three dozen qubits — the highly effective calculators quantum computer systems use.

That’s about twice the variety of qubits most researchers can mannequin as of late.

Highly effective, But Straightforward to Use

The so-called multi-node model of PennyLane, utilized in tandem with the NVIDIA cuQuantum SDK, simplifies the complicated job of accelerating large simulations of quantum methods.

“This opens the door to letting even my interns run among the largest simulations — that’s why I’m so excited,” stated Yoo, whose workforce has six tasks utilizing PennyLane within the pipeline.

His work goals to advance high-energy physics and machine studying. Different researchers use quantum simulations to take chemistry and supplies science to new ranges.

Quantum computing is alive in company R&D facilities, too.

For instance, Xanadu helps corporations like Rolls-Royce develop quantum algorithms to design state-of-the-art jet engines for sustainable aviation and Volkswagen Group invent extra highly effective batteries for electrical vehicles.

4 Extra Tasks on Perlmutter

In the meantime, at NERSC, no less than 4 different tasks are within the works this yr utilizing multi-node Pennylane, in response to Katherine Klymko, who leads the quantum computing program there. They embrace efforts from NASA Ames and the College of Alabama.

“Researchers in my discipline of chemistry wish to examine molecular complexes too giant for classical computer systems to deal with,” she stated. “Instruments like Pennylane allow them to prolong what they’ll at the moment do classically to organize for finally working algorithms on large-scale quantum computer systems.”

Mixing AI, Quantum Ideas

PennyLane is the product of a novel concept. It adapts common deep studying strategies like backpropagation and instruments like PyTorch to programming quantum computer systems.

Xanadu designed the code to run throughout as many forms of quantum computer systems as doable, so the software program acquired traction within the quantum group quickly after its introduction in a 2018 paper.

“There was engagement with our content material, making cutting-edge analysis accessible, and folks acquired excited,” recalled Josh Izaac, director of product at Xanadu and a quantum physicist who was an creator of the paper and a developer of PennyLane.

Requires Extra Qubits

A standard touch upon the PennyLane discussion board as of late is, “I need extra qubits,” stated Lee J. O’Riordan, a senior quantum software program developer at Xanadu, chargeable for PennyLane’s efficiency.

“After we began work in 2022 with cuQuantum on a single GPU, we acquired 10x speedups just about throughout the board … we hope to scale by the tip of the yr to 1,000 nodes — that’s 4,000 GPUs — and that would imply simulating greater than 40 qubits,” O’Riordan stated.

Scientists are nonetheless formulating the questions they’ll deal with with that efficiency — the sort of drawback they prefer to have.

Firms designing quantum computer systems will use the enhance to check concepts for constructing higher methods. Their work feeds a virtuous circle, enabling new software program options in PennyLane that, in flip, allow extra system efficiency.

Scaling Effectively With GPUs

O’Riordan noticed early on that GPUs have been the most effective car for scaling PennyLane’s efficiency. He co-authored final yr a paper on a technique for splitting a quantum program throughout greater than 100 GPUs to simulate greater than 60 qubits, cut up into many 30 qubit sub-circuits.

“We needed to increase our work to even bigger workloads, so once we heard NVIDIA was including multi-node functionality to cuQuantum, we needed to help it as quickly as doable,” he stated.

Inside 4 months, multi-node PennyLane was born.

“For a giant, distributed GPU undertaking, that was a fantastic turnaround time. Everybody engaged on cuQuantum helped make the mixing as straightforward as doable,” O’Riordan stated.

A Xanadu weblog particulars how builders can simulate large-scale methods with greater than 30 qubits utilizing PennyLane and cuQuantum.

The workforce remains to be accumulating knowledge, however to this point on “sample-based workloads, we see virtually linear scaling,” he stated.

Or, as NVIDIA founder and CEO Jensen Huang may say, “The extra you purchase, the extra you save.”