We're about to simulate a human brain on a supercomputer The world's most powerful supercomputers can now run simulations of billions of neurons, and researchers hope such models will offer unprecedented insights into how our brains work What would it mean to simulate a human brain? Today's most powerful computing systems now contain enough computational firepower to run simulations of billions of neurons, comparable to the sophistication of real brains. We increasingly understand how these neurons are wired together, too, leading to brain simulations that researchers hope will reveal secrets of brain function that were previously hidden. Researchers have long tried to isolate specific parts of the brain, modelling smaller regions with a computer to explain particular brain functions. But "we have never been able to bring them all together into one place, into one larger brain model where we can check whether these ideas are at all consistent", says Markus Diesmann at the Jülich Research Centre in Germany.

Could 2026 be the year we start using quantum computers for chemistry? Whether quantum computers can actually solve practical problems is one of the biggest unanswered questions of this growing industry - and one that might be answered by researchers in industrial and medical chemistry in 2026. Calculating the structure, reactivity and other chemical properties of a molecule is an intrinsically quantum problem because it involves its electrons, which are quantum particles. But the more complex a molecule is, the harder these calculations become, in some cases posing a real challenge even for traditional supercomputers. On the other hand, because quantum computers are also intrinsically quantum, they should have an advantage when it comes to tackling these chemical calculations.

Google has announced plans to team up with the UK to invite researchers to come up with uses for the tech giant's state-of-the-art quantum chip Willow. It is one of several firms competing to develop a powerful quantum computer - which is seen as an exciting new frontier in the future of computing. Researchers hope they will be able to crack problems in fields such as chemistry and medicine which are impossible for current computers to solve. Professor Paul Stevenson of the University of Surrey - who had no involvement with the agreement - told the BBC it was great news for UK researchers. The collaboration between Google and the UK's national lab for quantum computing means more researchers will get access to the technology.

A vital ingredient for making quantum computers truly useful just might be conventional computers. That was the message from a gathering of researchers this month, which explained that classical computers are vital for controlling quantum computers, decoding the results of their calculations and even developing new techniques for manufacturing quantum computers in future. Quantum computers are made from qubits - quantum objects that may come in the form of extremely cold atoms or tiny superconducting circuits. The more qubits that a quantum computer has, the more computationally powerful it gets. But qubits are fragile, so they must be carefully calibrated, monitored and controlled.

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Nvidia, the artificial intelligence (AI) chip leader, will build seven new supercomputers for the United States Department of Energy (DOE), CEO Jensen Huang has said. The company has $500bn in bookings for its AI chips, Huang said on Tuesday in a keynote address at the company's GTC event in Washington, DC, the US capital. It is striking deals around the world while also navigating a US-China trade war that could determine which country's technology is most used across the globe. Investors are looking for clarity on what chips the tech company will be able to sell to the vast Chinese market, but Huang in his keynote speech praised policies by US President Donald Trump while announcing new products and deals. These included network technology that will let Nvidia AI chips work with quantum computers.

The United States has formed a $1bn partnership with Advanced Micro Devices (AMD) to construct two supercomputers that will tackle large scientific problems ranging from nuclear power to cancer treatments to national security. The Reuters news agency first reported the new partnership, citing Energy Secretary Chris Wright and AMD CEO Lisa Su. The machines can accelerate the process of making scientific discoveries in areas the US is focused on. Energy Secretary Wright said the systems would "supercharge" advances in nuclear power and fusion energy, technologies for defence and national security, and the development of drugs. Scientists and companies are trying to replicate fusion, the reaction that fuels the sun, by jamming light atoms in a plasma gas under intense heat and pressure to release massive amounts of energy.

When you purchase through links in our articles, we may earn a small commission. Google's latest quantum breakthrough is 13,000x faster than supercomputers Google unveiled its Quantum Echoes algorithm that blows past previous quantum developments with verifiable results. Google has taken a decisive step towards practical quantum computers. A research team from Google Quantum AI has reported the first "verifiable quantum advantage" --in other words, a measurable computing advantage over conventional supercomputers that can be reproduced by other systems. At the center of it all is a new algorithm called "Quantum Echoes," which ran on Google's new "Willow" quantum processor that was unveiled in December 2024.

We propose VibeCodeHPC, an automatic tuning system for HPC programs based on multi-agent LLMs for code generation. VibeCodeHPC tunes programs through multi-agent role allocation and iterative prompt refinement. We describe the system configuration with four roles: Project Manager (PM), System Engineer (SE), Programmer (PG), and Continuous Delivery (CD). We introduce dynamic agent deployment and activity monitoring functions to facilitate effective multi-agent collaboration. In our case study, we convert and optimize CPU-based matrix-matrix multiplication code written in C to GPU code using CUDA. The multi-agent configuration of VibeCodeHPC achieved higher-quality code generation per unit time compared to a solo-agent configuration. Additionally, the dynamic agent deployment and activity monitoring capabilities facilitated more effective identification of requirement violations and other issues.

In this work, we develop machine learning (ML) based strategies to predict resources (costs) required for massively parallel chemistry computations, such as coupled-cluster methods, to guide application users before they commit to running expensive experiments on a supercomputer. By predicting application execution time, we determine the optimal runtime parameter values such as number of nodes and tile sizes. Two key questions of interest to users are addressed. The first is the shortest-time question, where the user is interested in knowing the parameter configurations (number of nodes and tile sizes) to achieve the shortest execution time for a given problem size and a target supercomputer. The second is the cheapest-run question in which the user is interested in minimizing resource usage, i.e., finding the number of nodes and tile size that minimizes the number of node-hours for a given problem size. We evaluate a rich family of ML models and strategies, developed based on the collections of runtime parameter values for the CCSD (Coupled Cluster with Singles and Doubles) application executed on the Department of Energy (DOE) Frontier and Aurora supercomputers. Our experiments show that when predicting the total execution time of a CCSD iteration, a Gradient Boosting (GB) ML model achieves a Mean Absolute Percentage Error (MAPE) of 0.023 and 0.073 for Aurora and Frontier, respectively. In the case where it is expensive to run experiments just to collect data points, we show that active learning can achieve a MAPE of about 0.2 with just around 450 experiments collected from Aurora and Frontier.