ORNL Researchers Advance Quantum Computing, Science Through Six DOE Awards

October 3, 2018

OAK RIDGE, Tenn., Oct. 3, 2018—The Department of Energy’s Oak Ridge National Laboratory is the recipient of six awards from DOE’s Office of Science aimed at accelerating quantum information science (QIS), a burgeoning field of research increasingly seen as vital to scientific innovation and national security.

The awards, which were made in conjunction with the White House Summit on Advancing American Leadership in QIS, will leverage and strengthen ORNL’s established programs in quantum information processing and quantum computing.

The application of quantum mechanics to computing and the processing of information has enormous potential for innovation across the scientific spectrum. Quantum technologies use units known as qubits to greatly increase the threshold at which information can be transmitted and processed. Whereas traditional “bits” have a value of either 0 or 1, qubits are encoded with values of both 0 and 1, or any combination thereof, at the same time, allowing for a vast number of possibilities for storing data.

While in its infancy, the technology is being harnessed to develop computers that, when mature, will be exponentially more powerful than today’s leading systems. Beyond computing, however, quantum information science shows great promise to advance a vast array of research domains, from encryption to artificial intelligence to cosmology.

The ORNL awards represent three Office of Science programs.

“Software Stack and Algorithms for Automating Quantum-Classical Computing,” a new project supported by the Office of Advanced Scientific Computing Research, will develop methods for programming quantum computers. Led by ORNL’s Pavel Lougovski, the team of researchers from ORNL, Johns Hopkins University Applied Physics Lab, University of Southern California, University of Maryland, Georgetown University, and Microsoft, will tackle translating scientific applications into functional quantum programs that return accurate results when executed on real-world faulty quantum hardware. The team will develop an open-source algorithm and software stack that will automate the process of designing, executing, and analyzing the results of quantum algorithms, thus enabling new discovery across many scientific domains with an emphasis on applications in quantum field theory, nuclear physics, condensed matter, and quantum machine learning.

ORNL’s Christopher M. Rouleau will lead the “Thin Film Platform for Rapid Prototyping Novel Materials with Entangled States for Quantum Information Science” project, funded by Basic Energy Sciences. The project aims to establish an agile AI-guided synthesis platform coupling reactive pulsed laser deposition with quick decision-making diagnostics to enable the rapid exploration of a wide spectrum of candidate thin-film materials for QIS; understand the dynamics of photonic states by combining a novel cathodoluminescence scanning electron microscopy platform with ultrafast laser spectroscopy; and enable understanding of entangled spin states for topological quantum computing by developing a novel scanning tunneling microscopy platform.

ORNL’s Stephen Jesse will lead the “Understanding and Controlling Entangled and Correlated Quantum States in Confined Solid-State Systems Created via Atomic Scale Manipulation,” a new project supported by Basic Energy Sciences that includes collaborators from Harvard and MIT.  The goal of the project is to use advanced electron microscopes to engineer novel materials on an atom-by-atom basis for use in QIS. These microscopes, along with other powerful instrumentation, will also be used to assess emerging quantum properties in-situ to aid the assembly process. Collaborators from Harvard will provide theoretical and computational effort to design quantum properties on demand using ORNL’s high-performance computing resources.

ORNL is also partnering with Pacific Northwest National Laboratory, Berkeley Laboratory, and the University of Michigan on a project funded by the Office of Basic Energy Sciences titled “Embedding Quantum Computing into Many-Body Frameworks for Strongly-Correlated Molecular and Materials Systems.” The research team will develop methods for solving problems in computational chemistry for highly correlated electronic states. ORNL’s contribution, led by Travis Humble, will support this collaboration by translating applications of computational chemistry into the language needed for running on quantum computers and testing these ideas on experimental hardware.

ORNL will support multiple projects awarded by the Office of High Energy Physics to develop methods for detecting high-energy particles using quantum information science. They include:

  • “Quantum-Enhanced Detection of Dark Matter and Neutrinos,” in collaboration with the University of Wisconsin, Tufts, and San Diego State University. This project will use quantum simulation to calculate detector responses to dark matter particles and neutrinos. A new simulation technique under development will require extensive work in error mitigation strategies to correctly evaluate scattering cross sections and other physical quantities. ORNL’s effort, led by Raphael Pooser, will help develop these simulation techniques and error mitigation strategies for the new quantum simulator device, thus ensuring successful detector calculations.

  • “Particle Track Pattern Recognition via Content Addressable Memory and Adiabatic Quantum Optimization: OLYMPUS Experiment Revisited,” a collaboration with John Hopkins Applied Physics Laboratory aimed at identifying rare events found in the data generated by experiments at particle colliders. ORNL principal investigator Travis Humble will apply new ideas for data analysis using experimental quantum computers that target faster response times and greater memory capacity for tracking signatures of high-energy particles.

  • “HEP ML and Optimization Go Quantum,” in collaboration with Fermi National Accelerator Laboratory and Lockheed Martin Corporation, which will investigate how quantum machine learning methods may be applied to solving key challenges in optimization and data analysis. Advances in training machine learning networks using quantum computer promise greater accuracy and faster response times for data analysis. ORNL principal investigators Travis Humble and Alex McCaskey will help to develop these new methods for quantum machine learning for existing quantum computers by using the XACC programming tools, which offer a flexible framework by which to integrate quantum computing into scientific software.

UT-Battelle manages ORNL for DOE’s Office of Science. The single largest supporter of basic research in the physical sciences in the United States, the Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit https://science.energy.gov/.


Source: ORNL

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire