Lifeboat Foundation

Now, scientists have a mathematical model that closely matches how the human brain processes visual information.

Scientists have confirmed that human brains are naturally wired to perform advanced calculations, much like a high-powered computer, to make sense of the world through a process known as Bayesian inference.

In a study published in the journal Nature Communications, researchers from the University of Sydney, University of Queensland and University of Cambridge developed a specific mathematical model that closely matches how human brains work when it comes to reading vision. The model contained everything needed to carry out Bayesian inference.

A complete quantum computing system could be as large as a two-car garage when one factors in all the paraphernalia required for smooth operation. But the entire processing unit, made of qubits, would barely cover the tip of your finger.

Today’s smartphones, laptops and supercomputers contain billions of tiny electronic processing elements called transistors that are either switched on or off, signifying a 1 or 0, the binary language computers use to express and calculate all information. Qubits are essentially quantum transistors. They can exist in two well-defined states—say, up and down—which represent the 1 and 0. But they can also occupy both of those states at the same time, which adds to their computing prowess. And two—or more—qubits can be entangled, a strange quantum phenomenon where particles’ states correlate even if the particles lie across the universe from each other. This ability completely changes how computations can be carried out, and it is part of what makes quantum computers so powerful, says Nathalie de Leon, a quantum physicist at Princeton University. Furthermore, simply observing a qubit can change its behavior, a feature that de Leon says might create even more of a quantum benefit. “Qubits are pretty strange. But we can exploit that strangeness to develop new kinds of algorithms that do things classical computers can’t do,” she says.

Scientists are trying a variety of materials to make qubits. They range from nanosized crystals to defects in diamond to particles that are their own antiparticles. Each comes with pros and cons. “It’s too early to call which one is the best,” says Marina Radulaski of the University of California, Davis. De Leon agrees. Let’s take a look.

Today, we are living in the midst of a race to develop a quantum computer, one that could be used for practical applications. This device, built on the principles of quantum mechanics, holds the potential to perform computing tasks far beyond the capabilities of today’s fastest supercomputers. Quantum computers and other quantum-enabled technologies could foster significant advances in areas such as cybersecurity and molecular simulation, impacting and even revolutionizing fields such as online security, drug discovery and material fabrication.

An offshoot of this technological race is building what is known in scientific and engineering circles as a “quantum simulator”—a special type of quantum computer, constructed to solve one equation model for a specific purpose beyond the computing power of a standard computer. For example, in medical research, a quantum simulator could theoretically be built to help scientists simulate a specific, complex molecular interaction for closer study, deepening scientific understanding and speeding up drug development.

But just like building a practical, usable quantum computer, constructing a useful quantum simulator has proven to be a daunting challenge. The idea was first proposed by mathematician Yuri Manin in 1980. Since then, researchers have attempted to employ trapped ions, cold atoms and superconducting qubits to build a quantum simulator capable of real-world applications, but to date, these methods are all still a work in progress.

In the future, quantum computers may be able to solve problems that are far too complex for today’s most powerful supercomputers. To realize this promise, quantum versions of error correction codes must be able to account for computational errors faster than they occur.

However, today’s quantum computers are not yet robust enough to realize such error correction at commercially relevant scales.

On the way to overcoming this roadblock, MIT researchers demonstrated a novel superconducting qubit architecture that can perform operations between qubits—the building blocks of a quantum computer—with much greater accuracy than scientists have previously been able to achieve.

The team achieved 99.99 percent accuracy with a single-qubit gate and 99.9 percent accuracy with a two-qubit gate.

Researchers at the Massachusetts Institute of Technology (MIT) have developed a new circuit that can do quantum computation with a high degree of accuracy. The researchers used a new type of superconducting qubit called the fluxonium, a press release said.

Quantum computers are considered the next frontier of computing since they can perform calculations at speeds that are decades ahead of supercomputers being used today. The flip side of such high speeds is that they can accumulate errors equally fast.

Tesla is reportedly increasing the orders for its Dojo D1 supercomputer chips. The D1 is a custom Tesla application-specific integrated circuit (ASIC) that’s designed for the Dojo supercomputer, and it is reportedly ordered from Taiwan Semiconductor Manufacturing Company (TSMC).

Citing a source reportedly familiar with the matter, Taiwanese publication Economic Daily noted that Tesla will be doubling its Dojo D1 chip to 10,000 units for the coming year. Considering the Dojo supercomputer’s scalability, expectations are high that the volume of D1 chip orders from TSMC will continue to increase until 2025.

Dojo, after all, is expected to be used by Tesla for the training of its driver-assist systems and self-driving AI models. With the rollout of projects like FSD, the dedicated robotaxi, and Optimus, Dojo’s contributions to the company’s operations would likely be more substantial.

Computer performance is measured in FLOPS, or floating-point operations per second. The first supercomputer, which was developed in 1964, could run 3,000,000 FLOPS, i.e., 3 megaFLOPS. Exa means 18 zeros, meaning 1,000,000,000,000,000,000 FLOPS. An exascale computer can perform that many operations — something that is almost impossible to imagine.

Now, there is a huge advantage to commanding that kind of computing power in today’s world. Here is what the same McKinsey report says: “Exascale computing could allow scientists to solve problems that have until now been impossible. With exascale, exponential increases in memory, storage, and compute power may drive breakthroughs in several industries: energy production, storage, transmission, materials science, heavy industry, chemical design, AI and machine learning, cancer research and treatment, earthquake risk assessment, and many more.”

Put simply, China now may have the computing power at its disposal to match, or even overtake, technology leaders like the United States in several areas that could be key to becoming the dominant economic and military power in the world. China could also pair its advances in artificial intelligence with this mind-boggling computering power and achieve technological and military dominance quite quickly.

Here are some more specs: Frontier uses approximately 50,000 processors, compared with the most powerful laptop’s 16 or 24. It consumes 20 million watts, compared with a laptop’s 65 or so. It cost $600 million to build.

When Frontier came online, it marked the dawn of so-called exascale computing, with machines that can execute an exaflop—or a quintillion (10¹⁸) floating point operations a second. Since then, scientists have geared up to make more of these blazingly fast computers: several exascale machines are due to come online in the US and Europe in 2024.

The Association for Computing Machinery has just put out the finalists for the Gordon Bell Prize award that will be given out at the SC23 supercomputing conference in Denver, and as you might expect, some of the biggest iron assembled in the world are driving the advanced applications that have their eyes on the prize.

The ACM warns that the final system sizes and final results of the simulations and models run are not yet completed, but we have a look at one of them because the researchers in China’s National Supercomputing Center in Wuxi actually published a paper they will formally released in November ahead of the SC23 conference. That paper, Towards Exascale Computation for Turbomachinery Flows, was run on the “Oceanlite” supercomputing system, which we first wrote about way back in February 2021, that won a Gorden Bell prize in November 2021 for a quantum simulation across 41.9 million cores, and that we speculated the configuration of back in March 2022 when Alibaba Group, Tsinghua University, DAMO Academy, Zhejiang Lab, and Beijing Academy of Artificial Intelligence ran a pretrained machine learning model called BaGuaLu, across more than 37 million cores and 14.5 trillion parameters in the Oceanlite machine.

NASA tossed down a grand challenge nearly a decade ago to do a time-dependent simulation of a complete jet engine, with aerodynamic and heat transfer simulated, and the Wuxi team, with the help of engineering researchers at a number of universities in China, the United States, m and the United Kingdom have picked up the gauntlet. What we found interesting about the paper is that it confirmed many of our speculations about the Oceanlite machine.