A new scientific breakthrough has reignited one of the biggest debates in modern computing after researchers announced results suggesting that a quantum computer outperformed a classical supercomputer on a highly specialized task. The findings have fueled discussions about whether the era of \.
Category: supercomputing
China Takes Supercomputer Crown From U.S. For First Time Since 2017
China took back a coveted computing crown from the United States on Tuesday, ratcheting up a fierce technological competition that has implications for science, national security and geopolitics.
LineShine, a massive computing system in Shenzhen, China, was declared the world’s fastest by a group of researchers using a set of standard tests for supercomputers. Besides raw speed, the system stood out because it uses only standard microprocessors and not the special-purpose chips called graphics processing units, which most high-end supercomputers rely on for heavy number crunching.
That underlying design could point to a better way to blend artificial intelligence with traditional scientific tasks, said Jack Dongarra, an organizer of the so-called Top500 list of the world’s most powerful supercomputers.
Chinese supercomputer displaces US machines as world’s fastest for first time since 2017
A supercomputer in China now outranks its U.S. counterparts as the world’s most powerful, marking the first time since 2017 that a Chinese computer has topped a list sometimes viewed as a measure of a nation’s technological prowess.
The LineShine computer in Shenzhen, China, displaced top-ranked U.S. computer El Capitan in the latest version of the TOP500 ranking announced Tuesday. It was the Chinese computer’s debut on the list.
Scientists behind the TOP500 project said the LineShine computer at China’s National Supercomputing Center achieved 2.198 exaflops, meaning it can perform more than 2 quintillion calculations per second.
Supercomputer illuminates subatomic particle that helps hold matter together
A team of researchers has leveraged a supercomputer at the U.S. Department of Energy’s (DOE) Argonne National Laboratory to reveal the internal structure of a pion in unprecedented detail. The findings are published in the Journal of High Energy Physics.
Pions are subatomic particles that help bind matter at some of the smallest scales in nature. They are closely connected to the strong nuclear force, the fundamental force that holds protons and neutrons together inside atomic nuclei. Understanding how pions work can help scientists explain how matter forms at its most fundamental level.
“Pions mediate the strong force that binds nucleons—that is, the protons and neutrons that account for an atom’s mass,” said Yong Zhao, an Argonne physicist and principal investigator on the project.
AI helps reveal large-scale quantum effects hidden in stacked atomic sheets
Quantum materials are a class of exotic materials with special properties that are governed by quantum mechanics rather than classical physics. Those properties—like superconductivity, entanglement and unusual forms of magnetism—often originate in the tiny repeating patterns of atoms inside crystals, but through clever engineering, they can be observed and controlled at a more human scale. Quantum materials are helping to power the quickly growing field of quantum computing and could find their way into future generations of energy-efficient electronics.
Designing new materials from the atomic scale up, however, requires intense modeling and simulation. Some materials may appear ordinary when viewed as small clusters of atoms, yet reveal new and useful properties when their atomic building blocks repeat and interact over larger distances. Researchers must be able to accurately predict behaviors at large scales in order to find materials with practical applications—otherwise, designing new materials is a slow and costly trial-and-error process.
In the past 50 years, supercomputers have helped materials scientists solve some of those thorny prediction problems, but two recent studies from the University of Washington demonstrate how newer computing techniques can help researchers sniff out promising quantum materials to pursue.
Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem
We study the scaling of QAOA TTS with the problem size on the low autocorrelation binary sequences (LABS) problem (15, 16), also known as the Bernasconi model in statistical physics (17, 18). The LABS problem has applications in communications engineering, where the low autocorrelation sequences are used for designing radar pulses (15, 19). To solve LABS, one has to produce a sequence of N bits that minimizes a specific quartic objective.
We choose LABS to study the scaling of QAOA TTS for the following three reasons. First, the complexity of LABS grows rapidly, with optimal solutions known only for N ≤ 66 and the best heuristics producing approximate solutions of quality decaying with N for N 200 (20, 21). This makes it a promising candidate problem, since only a few hundred qubits are required to tackle classically intractable instances. Second, the performance of classical solvers for LABS has been benchmarked (20, 21) in terms of the scaling of their TTS with problem size. Since optimal solutions are only known for N ≤ 66, the scaling of TTS for all classical solvers is obtained by fitting results for N ≤ 66. We reproduce these results and observe that that the scaling of classical solvers at N ≤ 40 matches the behavior for N up to 66 reported in the literature. This provides evidence that the scaling we observe for QAOA at N ≤ 40 will similarly extrapolate to larger N. Third, LABS has only one instance per problem size N. Combined with the hardness of LABS, this makes it possible to reliably study the scaling of QAOA at large problem sizes, where simulating tens or hundreds of random instances would be computationally infeasible.
We obtain the scaling by performing noiseless exact simulation of QAOA with fixed schedules. Our results are enabled by a custom algorithm-specific graphics processing unit (GPU) simulator (22), which we execute using up to 1,024 GPUs per simulation on the Polaris supercomputer accessed through the Argonne Leadership Computing Facility. We find that the TTS of QAOA with number of layers p = 12 grows as 1.46N, which is improved to 1.21N if combined with quantum minimum finding. This scaling is better than that of the best classical heuristic, which has a TTS that grows as 1.34N. We note that we do not propose any new quantum algorithms in this work. Instead, we study a general quantum optimization heuristic with broad applicability (namely, QAOA) and make no specific modifications to adapt it to the LABS problem.
Quantum-centric supercomputing simulates 12,635-atom protein
The scale of chemistry simulations with quantum computing has increased dramatically in just the last few months. In the latest milestone for the field, researchers from Cleveland Clinic, RIKEN, and IBM used a quantum-centric supercomputing (QCSC) framework to calculate the electronic structure of a pair of large protein-ligand complexes, reaching a scale of 12,635 atoms in the largest simulation.
The molecules were T4-Lysozyme, a protein from a family of proteins involved in the immune system degradation of peptidoglycans in bacterial membranes, and Trypsin, produced in the pancreas and used in digestion. The team simulated these proteins binding to molecules they interact with in nature and immersed in a liquid water solution, at scales of 11,608 atoms and 12,635 atoms respectively. Bringing together an international team of researchers from across the United States and Japan made it possible to develop the necessary algorithm and workflow enhancements to reach this milestone.
The researchers achieved this scale just four months after modeling the 303-atom miniprotein Trp-cage using quantum computing for the first time. Today’s new result not only demonstrates a 40-fold increase in system size compared to the Trp-cage result, it represents a 210-times improvement in accuracy from previous state-of-the-art QCSC approaches in a specific step of the workflow.
Prototype sets record for optical quantum information technology
Chinese scientists have developed a programmable quantum computing prototype called Jiuzhang 4.0 that has set a new world record for optical quantum information technology, according to a study published May 13 in the journal Nature.
Led by the University of Science and Technology of China (USTC), the team used the prototype to solve the Gaussian boson sampling problem at a speed more than 1054 times that of the world’s most powerful supercomputer, the study said.
The researchers said they manipulated and detected quantum states of up to 3,050 photons —a significant leap from the 255 photons achieved with the previous Jiuzhang 3.0.
