Summary: Researchers at Brookhaven National Laboratory are utilizing the Perlmutter supercomputer and the open-source quantum programming framework PennyLane, developed by Xanadu, to simulate large-scale quantum systems with more than 30 qubits, enabling breakthroughs in high-energy physics, machine learning, chemistry, and materials science.
- PennyLane is an open-source software that simplifies the complex task of accelerating massive simulations of quantum systems.
- PennyLane is designed to run on various types of quantum computers, making it widely adopted within the quantum community.
- NVIDIA's cuQuantum allows developers to simulate large-scale systems with more than 30 qubits using PennyLane.
In the world of Quantum Computing, researchers at the U.S. Department of Energy’s Brookhaven National Laboratory are gearing up to run simulations on a supercomputer for the first time. Shinjae Yoo, a computational scientist and machine learning group lead, is excited to utilize the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC) for this work.
Yoo will be using the latest version of PennyLane, a quantum programming framework developed by Xanadu, a Toronto-based company. This open-source software, which builds on nVidia’s cuQuantum software development kit, allows simulations to run on high-performance clusters of NVIDIA GPUs. The performance of the Perlmutter supercomputer is crucial for processing large datasets, as Yoo needs to simulate about three dozen qubits, which are the building blocks of quantum computers.
What makes PennyLane particularly appealing is its ease of use. The multi-node version of the software, in conjunction with the NVIDIA cuQuantum SDK, simplifies the complex task of accelerating massive simulations of quantum systems. Yoo is thrilled that even his interns will be able to run these large simulations, as his team has six projects in the pipeline utilizing PennyLane. Their work aims to advance high-energy physics and machine learning, while other researchers are using quantum simulations to push the boundaries of chemistry and materials science.
Quantum computing is not limited to research laboratories; it has also found its way into corporate R&D centers. Xanadu, for example, is collaborating with companies like Rolls-Royce and Volkswagen Group to develop quantum algorithms for designing jet engines and more powerful batteries for electric cars.
Meanwhile, NERSC has several other projects lined up this year that will utilize the multi-node version of PennyLane. Katherine Klymko, who leads the quantum computing program at NERSC, explains that researchers in fields like chemistry are eager to study molecular complexes that are too large for classical computers to handle. Tools like PennyLane allow them to extend their capabilities and prepare for running algorithms on large-scale quantum computers in the future.
PennyLane is a unique quantum programming framework that combines popular deep learning techniques with quantum computing concepts. It was designed to run on various types of quantum computers, making it widely adopted within the quantum community since its introduction in 2018.
One common request from users on the PennyLane forum is for more qubits. Lee J. O’Riordan, a senior quantum software developer at Xanadu, responsible for PennyLane’s performance, explains that they have been working on scaling up the software’s capabilities. With the help of NVIDIA’s cuQuantum, they hope to scale up to 1,000 nodes and simulate more than 40 qubits by the end of the year.
The performance improvements in PennyLane not only benefit researchers but also companies designing quantum computers. The feedback loop between researchers and hardware developers enables new software features in PennyLane, which, in turn, enable better system performance.
O’Riordan recognized early on that GPUs were the best way to scale PennyLane’s performance. In a paper co-authored last year, he outlined a method for splitting a quantum program across multiple GPUs to simulate more than 60 qubits. When NVIDIA announced multi-node capability for cuQuantum, Xanadu quickly integrated it into PennyLane, resulting in the birth of multi-node PennyLane within just four months.
Developers can now simulate large-scale systems with more than 30 qubits using PennyLane and cuQuantum. Initial data suggests that the scaling is nearly linear for sample-based workloads. As NVIDIA founder and CEO Jensen Huang might say, “The more you buy, the more you save.”
About nVidia: NVIDIA has firmly established itself as a leader in the realm of client computing, continuously pushing the boundaries of innovation in graphics and AI technologies. With a deep commitment to enhancing user experiences, NVIDIA's client computing business focuses on delivering solutions that power everything from gaming and creative workloads to enterprise applications. for its GeForce graphics cards, the company has redefined high-performance gaming, setting industry standards for realistic visuals, fluid frame rates, and immersive experiences. Complementing its gaming expertise, NVIDIA's Quadro and NVIDIA RTX graphics cards cater to professionals in design, content creation, and scientific fields, enabling real-time ray tracing and AI-driven workflows that elevate productivity and creativity to unprecedented heights. By seamlessly integrating graphics, AI, and software, NVIDIA continues to shape the landscape of client computing, fostering innovation and immersive interactions in a rapidly evolving digital world.nVidia Website: https://www.nvidia.com
nVidia LinkedIn: https://www.linkedin.com/company/nvidia/
Quantum Computing: Quantum computing is a type of advanced computing that takes advantage of the strange behaviors of very small particles. It's like having a supercharged computer that can solve incredibly complex problems much faster than regular computers. It does this by using special "bits" that can be both 0 and 1 at the same time, which allows it to process information in a very unique way. This technology has the potential to make a big impact in areas like data security and solving really tough scientific challenges, but there are still some technical hurdles to overcome before it becomes widely useful.
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