Researchers Using HLRS Supercomputing Resources to Take Quantum Dots for a Spin

March 4, 2021

March 4, 2021 — Modern computing technologies enable researchers across many scientific domains to simulate phenomena that are too large, small, dangerous, or difficult to observe through experiment. In fact, supercomputing initiated a new golden era of particle physics research, playing an indispensable role in helping illuminate interactions at the atomic level and below.

Since the 1920s, researchers have documented the laws that govern the atomic and subatomic world in painstaking detail. The quantum world, as it became known, operates according to a different set of laws than those first posited by Isaac Newton. As modern-day researchers have gained a deeper understanding of quantum mechanics, they have identified opportunities to manipulate and control subatomic particles in ways that could lead to new kinds of electronics or other technologies. Among these promising advances, quantum computing—which, through the application of quantum mechanics, processes information in a fundamentally different way than traditional computers—promises to accelerate certain kinds of research and the development of new applications.

While traditional computers rely on extensive patterns of 1s and 0s to transmit information in bits, quantum computers utilize so-called qubits, particles operating in accordance with the laws of quantum mechanics. Each qubit can be facing up, down, or in its “superposition,” meaning it simultaneously represents both positions. Although the existence of this kind of state—where electrons can represent multiple positions simultaneously—could benefit data-intensive modelling and simulation, further understanding is needed of these counterintuitive phenomena. Scientists must also develop methods for reliably manipulating and controlling quantum particles, specifically with regard to how individual electrons “spin,” or orient themselves under specific conditions.

In order to better understand how subatomic particles behave and interact at a fundamental level, a multidisciplinary research collaboration based at the TU Dortmund University is using high-performance computing (HPC) resources at the High-Performance Computing Center Stuttgart (HLRS) to simulate some of these complex interactions. The team is partnered with experimentalists in the international collaborative research center 160 (ICRC 160) established at St. Petersburg and Dortmund to study how electrons’ spins (and the spins of larger nuclei nearby in a given system) interact under certain conditions, and how laser technologies could help manipulate these systems.

In its most recent work, the team has focused on quantum dots. Developed within the context of semiconductor technologies, researchers found that quantum dots could control electrons’ spins under the right conditions, making them good candidates to serve as qubits in future quantum computers.

“A quantum dot can be seen as a trap for a single electron and therefore for its spin,” said Prof. Dr. Götz Uhrig, Professor at TU Dortmund and lead researcher on the project. “If we are looking at a solid state device, there are as many as 1020 electrons and their spins behave in such a way that no net effect can be seen for the outside; the spin of an excess electron, however, can be detected and manipulated.”

Turning toward a better understanding of spins in quantum dots

When physicists study atomic systems, one approach involves strategically adding or removing an electron to determine the properties that it imparts on the system. As they learned more about how to modify these systems, scientists and engineers came up with relatively inexpensive ways to create silicon-based semiconductors that today are nearly ubiquitous in consumer electronics.

Now, as scientists turn their attention to developing new electronics such as quantum computers, they have employed specialized semiconductor nanostructures—so-called quantum dots—in order to better direct electrons and their spins as is needed in order to follow certain algorithms.

Within a quantum dot, researchers can use lasers or other technologies fix the positions of excess electrons in space, making it easier to manipulate their spins. Experimentally, researchers must account for the strange, novel physics that govern quantum spins of electrons, in their “up” and “down” states, as mentioned previously.

Accounting for superpositions ultimately means that researchers must calculate millions of possible states of electron spins for each quantum dot. And while simulating the spin of an individual quantum dot interacting with roughly thousands of nuclear spins may not be too computationally demanding, a meaningful simulation or experiment will have to deal with thousands of quantum dots at once.

One common experimental approach is to use laser pulses to “train” quantum dots so that their spins act synchronously. “My colleagues send pulses to the system that orient a quantum dot’s spin,” Uhrig said. “When they do that for quite a long time, they get some response before the next pulse. So you see some polarization, then it dies out, but when you have done that for a long time, there is already a signal before the next pulse comes—that shows that the system has been trained successfully.”

In experiments, researchers will shoot a laser pulse every few nanoseconds and training the dots can take seconds or even minutes. This means that a realistic simulation must cover an extremely wide range of time scales. Performing the necessary calculations in a reasonable amount of time would not be possible without large-scale supercomputing resources.

This image shows the evolution of electronic spin polarization in time. Each pulse generates a finite signal which strongly oscillates since the spin precesses around the applied magnetic field. Due to the interaction with the disordered nuclear spins, the signal quickly dies out (upper panel). After long series of pulses, the nuclear spins are trained and a coherent revival of the signal occurs. Image credit: Philipp Schering, TU Dortmund

Advancement through iteration

Uhrig and his collaborators use HPC primarily to help understand the data seen in experiments quantitatively. This approach allows the team not only to efficiently verify how well a given quantum system has been trained, but also to make predictions using the physical model they develop. If the team has designed the model correctly, new phenomena shown in models should then also be found in experiment.

“This back-and-forth is a general feature in the dynamics between theory and experiment,” Uhrig said. “When theory tries to be close to experiment, many parameters need to be taken into account, meaning you have to do heavy numerics, and that’s where HPC comes in.”

Using this iterative approach, the team uncovered strategies for amplifying signals in the data that indicate how electron spins respond to laser pulses. Ultimately, this knowledge could improve researchers’ ability to predict and rely on quantum-scale behavior when executing tasks in quantum computers and other advanced electronics.

The team was able to access large core counts to run larger calculations on HLRS’s Hawk supercomputer during its acceptance phase last year. “On short notice, we were offered short queues for large core counts, and that made a big difference on how quickly we were able to do our research,” Uhrig said. Now that the team has been able to further optimize its code on Hawk, the researchers feel confident that its future allocations will enable them to take on more complex calculations for larger systems.


Source: Eric Gedenk, HLRS

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!

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

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from its predecessors, including the red-hot H100 and A100 GPUs. 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. While Nvidia may not spring to mind when thinking of the quant Read more…

2024 Winter Classic: Meet the HPE Mentors

March 18, 2024

The latest installment of the 2024 Winter Classic Studio Update Show features our interview with the HPE mentor team who introduced our student teams to the joys (and potential sorrows) of the HPL (LINPACK) and accompany Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the field was normalized for boys in 1969 when the Apollo 11 missi Read more…

Apple Buys DarwinAI Deepening its AI Push According to Report

March 14, 2024

Apple has purchased Canadian AI startup DarwinAI according to a Bloomberg report today. Apparently the deal was done early this year but still hasn’t been publicly announced according to the report. Apple is preparing Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimization algorithms to iteratively refine their parameters until Read more…

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

March 18, 2024

Nvidia's latest and fastest GPU, code-named 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…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimizat Read more…

PASQAL Issues Roadmap to 10,000 Qubits in 2026 and Fault Tolerance in 2028

March 13, 2024

Paris-based PASQAL, a developer of neutral atom-based quantum computers, yesterday issued a roadmap for delivering systems with 10,000 physical qubits in 2026 a Read more…

India Is an AI Powerhouse Waiting to Happen, but Challenges Await

March 12, 2024

The Indian government is pushing full speed ahead to make the country an attractive technology base, especially in the hot fields of AI and semiconductors, but Read more…

Charles Tahan Exits National Quantum Coordination Office

March 12, 2024

(March 1, 2024) My first official day at the White House Office of Science and Technology Policy (OSTP) was June 15, 2020, during the depths of the COVID-19 loc Read more…

AI Bias In the Spotlight On International Women’s Day

March 11, 2024

What impact does AI bias have on women and girls? What can people do to increase female participation in the AI field? These are some of the questions the tech 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…

Analyst Panel Says Take the Quantum Computing Plunge Now…

November 27, 2023

Should you start exploring quantum computing? Yes, said a panel of analysts convened at Tabor Communications HPC and AI on Wall Street conference earlier this y 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…

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…

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…

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

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…

Training of 1-Trillion Parameter Scientific AI Begins

November 13, 2023

A US national lab has started training a massive AI brain that could ultimately become the must-have computing resource for scientific researchers. Argonne N 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…

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…

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…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire