By Katie Elyce Jones, Managing Editor, PillarQ
As the high-performance computing (HPC) community looks beyond Moore’s Law for solutions to speed up future systems, one of the technologies at the forefront is quantum computing, which accumulates every billions of dollars in global funding for R&D every year.
It’s perhaps unsurprising that HPC centers – including the Oak Ridge Leadership Computing Facility (OLCF), home to the world’s first exascale supercomputer, Frontier – are finding ways to operate and advance systems quantum.
Located at Oak Ridge National Laboratory (ORNL) in Tennessee and funded by the US Department of Energy (DOE), the OLCF’s Quantum Computing User Program (QCUP) provides scientific users with remote access to major computing systems commercial quantum. Currently, the program provides access to various superconducting architectures from IBM Quantum Services and Rigetti Quantum Cloud Services, as well as Quantinuum trapped ion computers and emulators. The program also prepares access to an IonQ trapped ion system.
In a new initiative this year, OLCF and QCUP are linking quantum and HPC through a hybrid allocation program that provides dual access to QCUP’s quantum providers and OLCF’s supercomputers.
“The goal of QCUP is to help us understand how the [quantum] the technology is developing and helping us predict when we would want this technology to be part of the next HPC system,” said QCUP Director Travis Humble.
Humble is also director of ORNL’s Quantum Science Center, which is funded by another DOE program — the National Quantum Information Science Research Centers — but shares overlapping interests in quantum research and development. He will be a panelist for “Quantum Computing: A Future for HPC Acceleration?” at SC22 (The International Conference for High Performance Computing, Networking, Storage, and Analysis) on Friday, November 18.
Humble said QCUP offers a range of quantum computing systems to explore what works best for certain problems and classical computing is part of that exploration. “We don’t yet know the best hardware and how the apps will match up. Quantum computing, as a theory, gives us a whole new playground in which to test computation, inform scientific discovery, so that it changes the kinds of problems we can actually compute. A supercomputer is powerful, but it is also limited. Hybrid takes the best of both worlds.
However, he cautioned that few apps currently make good use of both devices, and the intention of QCUP’s new quantum-classical hybrid allocations is to find apps that perform well on both.
QCUP has about 250 users and has evolved since 2016 from an internal laboratory program to the current user program. Sponsored by the DOE’s Advanced Scientific Computing Research (ASCR) program, the Quantum User Program has adopted the same HPC user model as ASCR’s advanced computing facilities, which review scientific proposals for their potential impact and merit. allocate time to computer systems.
“We’re looking for feasibility — are they trying to solve a problem that will hold even on a quantum computer — and technical preparation and application,” Humble said.
QCUP user support includes a science engagement team that helps researchers port their code, though in the past many users have been “expert quantum users,” he said. “They have written programs and are ready to go.”
Many users come from scientific programs related to quantum research, such as high energy and nuclear physics and fusion energy. For example, a team led by Lawrence Berkeley National Laboratory used QCUP resources to simulate part of two colliding protons, breaking down physics calculations into those best suited for classical computing vs. quantum to include quantum effects that a classical computer would otherwise approximate.
“By far, physics is the most present. Second, there’s probably computing, which includes building tools that enable better performance of a quantum computer,” Humble said.
In another QCUP project, a team led by researchers from the University of Chicago and Argonne National Laboratory simulated quantum spin defects, with applications for encoding information in quantum computers. In this case, they used classical calculations to validate and reduce errors in their quantum calculations.
Artificial intelligence (AI) is also emerging at the interface of classical and quantum computing. Humble said the goal of some computing projects is to use quantum computing to accelerate AI and machine learning workflows or to uncover quantum-specific insights in AI-generated data.
Although the program provides access to quantum computers through an HPC user installation, these computers are not integrated with HPC systems. One of QCUP’s ultimate goals is to connect quantum and HPC systems, but there are short-term hurdles.
“Part of the barrier now is that quantum computing is so early. If you look at what a quantum computer is today, in 6 months it will be replaced by something new,” Humble said.
From a technical point of view, quantum computers still require special maintenance and cannot yet compete with the performance of HPC. From the user’s perspective, training barriers have mostly relegated quantum computing to quantum experts.
“The training materials you need to start using quantum computing are also in their infancy,” Humble said. “For the vast majority of HPC users who want to adopt quantum, we need to create training resources for them.”
Although many HPC-quantum collaborations are still in their early stages, experiences from programs such as QCUP and quantum projects at other HPC centers could help set the stage for future HPC-quantum integration.
Katie Elyce Jones is the founder and editor of research news publication PillarQ.
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