#nersc

NERSC News

QuEra Computing was selected for the National Energy Research Scientific Computing Center (NERSC) 2026 Research Access Program, a key quantum computing industry development. This agreement is a major step toward bringing commercial-grade neutral-atom quantum computing devices to the DOE's demanding, realistic scientific research environments.

New National Laboratories Era

Key research organizations and government laboratories are trusting next-generation quantum hardware more. After years of being considered experimental curiosity, quantum systems are now being recognized as useful tools that can solve computational problems that even the most advanced classical HPC infrastructure cannot.

The NERSC 2026 Call for Proposals requests hardware-aware, HPC-integrated processes. These procedures focus on materials science, quantum chemistry, energy system modeling, and fundamental physics to fit with DOE Office of Science strategic priorities. This appeal from NERSC allows scientific firms, national labs, and university institutions to migrate their experimental workflows to cutting-edge quantum systems.

Technology: Neutral Atom Power

This partnership relies on QuEra's quantum processing technique. QuEra uses neutral atoms instead of superconducting circuits or trapped-ion systems, which dominated early quantum discussions. Programmable forms are created by carefully controlling and organizing these atoms with laser-based optical tweezers.

Researchers can use this method to induce Rydberg states in atoms, allowing them to simulate complex quantum processes or perform specialized computational tasks.

This technique has two key benefits for the NERSC mission:

Neutral-atom platforms can simulate chemical systems and materials science concerns at unprecedented scale by arranging hundreds of atoms.

Infrastructure Compatibility: Running at ambient temperature is one of these systems' main benefits. Neutral-atom technology eliminates the need for superconducting computers' huge, energy-intensive cryogenic cooling systems, making it more appealing for HPC facilities like NERSC.

Different Scientific Needs, Two Platforms

Researchers selected for the initiative will have access to two QuEra platforms, each with a unique computational ecosystem function:

Aquila: This analog quantum simulator was designed for large-scale optimization and complex many-body physics problems.

Gemini: A gate-based quantum system executes programmable quantum circuits in this alternative algorithm creation method.

For optimal scientific output, the program uses simulation-first validation. Researchers must demonstrate that their algorithms can run on quantum devices rather than classical simulations before receiving live hardware.

Structured Discovery Path

The research programme uses a two-stage evaluation procedure to maximise QPU hours.

Stage A: Proof of Feasibility Teams selected in April 2026 will undertake a three-month preparatory phase. Aquila teams can use hardware for 12.5 QPU-hours. In this early stage, Gemini teams will focus on simulation workflows and algorithm enhancement without direct hardware interaction. Stage A's major goal is to determine if an application can generate a large quantum advantage.

Stage B: Full Hardware Deployment: Projects that pass the first stage's feasibility test will receive far more hardware in Stage B. Gemini projects receive 10 QPU-hours of live hardware time, whereas Aquila-based projects receive 25 (for a total of 37.5 hours). This systematic approach ensures that the most promising scientific studies receive the limited and crucial QPU time. According to NERSC's open laboratory mission, all research must be completed by December 2026 and published in public forums or peer-reviewed journals.

Strategic and Economic Implications

QuEra's investor and industry outlook changed with this alliance. QuEra is moving away from “exploratory trials” and toward production-grade scientific experimentation to make its gear a sophisticated tool for worldwide study. QuEra's link with NERSC, a facility known for its statistically rigorous and HPC-integrated workloads, boosts its credibility in government, academic, and industrial sectors. Credibility is expected to boost demand, alliances, and finance in the fast-growing hybrid QC HPC solutions sector.

Partnership does not start immediately. Since 2023, QuEra and NERSC have collaborated on quantum dynamics simulations and optimization, showing that these devices can handle workloads that are difficult to prove using classical methods.

The Future of Hybrid Supercomputing

The goal of NERSC 2026 goes beyond individual discoveries. It aims to speed up the design of quantum-ready algorithms that can grow as technology gets more error-tolerant. In computational science, hybrid quantum-classical workflows using quantum processors for complex simulations and classical supercomputers for data preparation are the norm. Adding quantum hardware to HPC ecosystems is a turning point.

A decade from now, quantum computers will be used in American science, according to NERSC.

According to the Department of Energy's major computing centre, quantum computers could become practical within ten years, affecting important scientific work. Rapid algorithmic advancements and ambitious industry roadmaps suggest that quantum systems may soon be able to meet federal science's rigorous computational requirements, according to LBNL and NERSC researchers.

Quantum hardware advances meet the increasingly scarce resources needed to solve scientific problems. NERSC, which supports over 12,000 DOE researchers, was examined for its scientific burden. Over half of NERSC's Perlmutter system activity is already dedicated to “quantum relevant” problems, which are essential to quantum physics and cannot be solved by classical methods.

NERSC computes almost half of its cycles on high-energy physics, quantum chemistry, and materials science. Quantum devices can succeed in sectors where exponentially complicated calculations fail.

Resource Reduction Is Meeting Hardware Scaling

Importantly, estimates of quantum resources, as measured by necessary qubits and quantum gate operations, have dropped dramatically over the previous five years. A better grasp of issue design and algorithmic enhancements caused this drop. They observed that gates were reduced by hundreds or thousands and qubits by a factor of five in benchmark cases like the FeMoco molecule. In contrast to broad theoretical discoveries, constant-factor advancements have already led to significant savings, and these gains are expected to progressively shrink the gap between hardware capabilities and application needs.

Public roadmaps for superconducting circuits, trapped ions, and neutral atoms from ten prominent quantum computing companies were studied simultaneously. Most roadmaps expect a considerable rise in machine performance over the next decade. Some predictions predict nine-order-of-magnitude performance gains in ten years. When hardware estimations are compared to algorithmic needs, large-scale quantum computers may meet scientific objectives in five to ten years.

Roadmaps foresee modest error-corrected systems in five years, larger fault-tolerant machines in ten, and enormously huge systems in the years that follow. Materials science and chemistry may have an early edge in 2025–2027 when vendors anticipate systems to give “quantum utility” on a few problems. High-energy physics, condensed matter models, and chemical standards will be enabled by fault-tolerant systems that can process millions of logical operations by the mid-2030s, according to the most ambitious roadmap

The Three Main Impact Areas

According to the NERSC study, the three key domains, which require massive computational resources, have varied effect dates.

Science of Materials: Resources Simulations of spin or lattice systems are qubit-friendly science problems. The "quantum advantage," when quantum technology outperform classical methods, seems closest to these challenges These challenges overwhelm ordinary computers because strongly interacting electrons create topological phases, magnetism, and high-temperature superconductivity.

Quantum Chemistry: Due to algorithm advancement, quantum chemistry is a mature testbed with the highest resource prediction decline. Future gear may calculate benchmark molecular ground-state energies now unachievable. Molecular behaviour models could be revolutionised, improving battery, photovoltaic, quantum information, industrial catalyst, and sustainable fuel production.

High-Energy Physics: Lattice gauge theory and neutrino dynamics remain the hardest concepts. Classical methods cannot handle real-time dynamics or systems far from equilibrium, hence the strong force, which keeps quarks and gluons together in matter, is unknown. Quantum chromodynamics, the theory of this force, is preferred by the DOE for quantum computing research. Before quantum devices to compete, error-correction and encoding must improve since recording fermions and gauge fields increases gate counts.

Introduction to SQSP

Although qubits and gate counts dominate metrics, the NERSC team warns that execution time will become the deciding factor as quantum technology progresses. Because of their wide clock speed range from kilohertz to gigahertz, quantum processors can execute for seconds to years. Depending on processing speed, two devices with similar qubit counts could produce quite different scientific values.

Sustained Quantum System Performance (SQSP) is the researchers' new metric for quantifying this key component. SQSP determines practical throughput by calculating the number of full scientific procedures a system can run annually across different applications. This elevates usefulness over component counts. Throughput could range from one run to tens of millions per year, depending on system architecture and design, according to preliminary calculations.

Scale and speed will be equally important obviously. Quantum computers with qubit and gate requirements for complex physics or chemistry problems are impracticable if the run time is months. DOE, which schedules computing procurements on five-year cycles, may use SQSP to determine if quantum hardware is ready to complement traditional supercomputers.

Our Nobel Prize-Winning Supercomputer Accelerates Science

Dell Technologies will build NERSC 10, the next flagship supercomputer of the National Energy Research Scientific Computing Centre (NERSC), under a new DOE contract. DOE user site Lawrence Berkeley National Laboratory houses NERSC. Chris Wright, Secretary of Energy, made the announcement at Berkeley Lab.

The DOE Office of Science user facility Nation Energy Research Scientific Computer Centre (NERSC) houses the 10th-generation computer system NERSC 10. HPC facilities at NERSC enable physics, climate, energy, biology, and materials science research.

Overview of NERSC 10

In 2024, NERSC 10 will replace Perlmutter, its flagship supercomputer.

The implementation of NERSC 10 is expected in 2026 or later.

Developed for DOE Office of Science's growing data and computational demands until 2030.

NERSC 10 returns to LBNL in California.

Main features

Main goals of NERSC 10 Extreme Performance:

Near-exascale or exascale computer power.

Support for increasingly complex workloads.

Balanced Architecture:

High memory bandwidth and capacity.

I/O for rapid storage.

Complex interconnects for low-latency communication.

Integration of HPC and AI:

We support AI and ML workloads in addition to simulations.

built for hybrid workflows.

Energy Efficiency:

Boosting FLOP power efficiency.

Green technology like liquid cooling may be investigated.

User-centred design:

Maintaining the NERSC user experience and software stack.

A focus on usability and productivity for many scientists.

Purchase and Development

DOE normally issues an RFP years before system delivery.

Community Engagement: NERSC solicits scientific user community comments throughout system design to ensure practical research needs are met.

Strategic Importance

Supports hundreds of research projects and over 9,000 users throughout DOE mission areas.

Leadership Role: Unlike experimental exascale systems, NERSC systems are easy to use by many scientists.

The 2020 Nobel Prize for Chemistry winner for developing CRISPR, Berkeley Lab biologist Jennifer Doudna, will name the new system “Doudna” in 2026. Secretary Wright was surprised and pleased by Doudna's nomination, praising his biological results and the potential for computational powers to speed illness and tumour cures.

The Dell Technologies Doudna supercomputer will run on NVIDIA's next-generation Vera Rubin platform. Large-scale HPC workloads including high-energy physics, molecular dynamics, and AI training and inference are its focus. Simulation, data, and AI on one platform stabilise cutting-edge science workflows and expedite findings.

The system “represents DOE’s commitment to advancing American leadership in science, AI, and high-performance computing,” Secretary Wright said. He called Doudna a “powerhouse for rapid innovation” that will revolutionise quantum computing and supply cheap, abundant energy. Wright called AI “the Manhattan Project of the time,” emphasising that Doudna will assist American scientists compete globally in AI.

Doudna supercomputer would speed up numerous scientific operations, said NERSC Director Sudip Dosanjh. NERSC is collaborating with NVIDIA and Dell to prepare its 11,000 users for the system's improved workflow. Doudna will be connected to DOE observational and experimental facilities via the Energy Sciences Network (ESnet), allowing scientists to evaluate and stream data in real time. Because of this integration, the supercomputer is no longer a passive workflow player.

Doudna may boost innovation in several areas. Doudna can accelerate the finding of plentiful, usable energy because NERSC finances fusion research. Its strong GPUs will let DOE-funded researchers swiftly integrate large-scale AI into their workflows, speeding up basic physics, biomolecular modelling, and advanced materials design research. The system will support modern quantum simulation tools including NVIDIA's CUDA-Q platform for co-designing next integrated quantum-HPC systems and scalable quantum algorithms.

The Vera-Rubin CPU-GPU platform and Dell's latest ORv3 direct liquid-cooled server technologies are used, according to NVIDIA. It will use Dell Integrated Rack Scalable Systems and PowerEdge servers with NVIDIA accelerators, NVIDIA Quantum-X800 InfiniBand networking, and high-performance data management and storage.

Doudna is expected to exceed NERSC's flagship supercomputer, Perlmutter, by over ten times in scientific output. Two to three times the power of Perlmutter is expected, boosting performance by three to five times per watt. The goal is to substantially reduce the time needed for major scientific breakthroughs.

 Nvidia and the National Energy Research Scientific Computing Center (NERSC) on Thursday flipped the “on” switch for Perlmutter, charged as the world’s quickest supercomputer for AI jobs. Named for astrophysicist Saul Perlmutter, the new supercomputer brags 6,144 NVIDIA A100 Tensor Core GPUs and will be entrusted with sewing together the biggest ever 3D guide of the noticeable universe, among different undertakings.

Perlmutter is “the quickest framework on earth” at preparing responsibilities with the 16-bit and 32-bit blended exactness math utilized in computerized reasoning (AI) applications, said Nvidia worldwide HPC/AI item advertising lead Dion Harris during press instructions recently. Not long from now, a subsequent stage will add considerably more AI supercomputing capacity to Perlmutter, which is housed at NERSC at the Lawrence Berkeley National Laboratory.

“In one venture, the supercomputer will help gather the biggest 3D map of the noticeable universe to date. It will handle information from the Dark Energy Spectroscopic Instrument (DESI), a sort of infinite camera that can catch upwards of 5,000 cosmic systems in a solitary openness,” Harris wrote in a blog entry reporting the news.

“Specialists need the speed of Perlmutter’s GPUs to catch many openings from one night to realize where to point DESI the following evening. Setting up a year of the information for distribution would require weeks or months on earlier frameworks, however, Perlmutter should assist them with achieving the assignment in as little as a couple of days,” he composed.

Supercharging HPC with AI and machine learning Starting up an AI-streamlined supercomputer “addresses an undeniable achievement,” said Wahid Bhimji, acting lead for NERSC’s information and examination administrations bunch.

Perlmutter with its A100 GPUs, all-streak document framework, and streaming information capacities are all around planned to address this issue for AI,” Bhimji added.

Perlmutter will give NERSC’s around 7,000 upheld analysts admittance to four exaflops of blended exactness registering execution for AI-helped logical activities. Notwithstanding the DESI planning project, specialists are teeing uptime with the supercomputer for work in fields like environmental science, where Perlmutter will help with examining subatomic cooperations to find efficient power fuel sources.

That venture, which will create recreations of molecules associating, requires the extraordinary mix of AI and superior registering (HPC) that Perlmutter conveys, Harris said. “Conventional supercomputers can scarcely deal with the math needed to create reproductions of a couple of atoms over a couple of nanoseconds with projects like Quantum Espresso. However, by consolidating their exceptionally exact reenactments with AI, researchers can concentrate more atoms throughout longer time frames,” he said.

The capacity to use AI in supercomputing likewise has scientists hopeful about the DESI project. As well as planning the known universe, the venture “expects to reveal insight into dull energy, the strange material science behind the speeding up an extension of the universe,” NERSC information engineer Rollin Thomas said. Framework namesake Saul Perlmutter, who stays a functioning astrophysicist at Berkeley Lab, was granted the 2011 Nobel Prize for Physics for his commitments to the disclosure of dull energy.

He added that in preliminary work with analysts to prepare code for Perlmutter supercomputer responsibilities, NERSC was at that point seeing 20x quicker GPU handling execution than in the beforehand accessible system.

Meta: The Nvidia and NERSC claim Perlmutter is the world’s quickest AI supercomputer and also found out the quicker execution. Learn everything you need to know about the venture.

Hello, I am Blanche Harris. Being an online security expert, I love to make people aware of cyber threats and share helpful information to them regarding them. Download, install and activate your office setup at office.com/setup.