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Category: supercomputing

The race toward the first practical quantum computer is in full stride. Companies, countries, collaborators, and competitors worldwide are vying for quantum supremacy. Google says it’s already there. But what does that mean? How will the world know when it’s been achieved?

Using , at PNNL have set a mark that a quantum system would need to surpass to establish quantum supremacy in the realm of chemistry.

That’s because the fastest classical computers available today are getting better and better at simulating what a quantum computer will eventually be expected to do. To prove itself in the real world, a quantum computer will need to be able to outdo what a fast supercomputer can do. And that’s where the PNNL-led team have set a benchmark for quantum computers to beat.

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Circa 2019

Google claims it has designed a machine that needs only 200 seconds to solve a problem that would take the world’s fastest supercomputer 10,000 years to figure out.

The speed achieved by the computer represents a breakthrough called “quantum supremacy,” according to a blog post from the company and an accompanying article in the scientific journal Nature.

The results announced Wednesday herald the rise of quantum computers, which can store and process much more information than their classical cousins by tapping into the powerful forces contained in the field of physics known as quantum mechanics.

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Circa 2015

University of Utah engineers have taken a step forward in creating the next generation of computers and mobile devices capable of speeds millions of times faster than current machines.

The Utah engineers have developed an ultracompact beamsplitter—the smallest on record—for dividing light waves into two separate channels of information. The device brings researchers closer to producing silicon photonic chips that compute and shuttle data with light instead of electrons. Electrical and computer engineering associate professor Rajesh Menon and colleagues describe their invention today in the journal Nature Photonics.

Silicon photonics could significantly increase the power and speed of machines such as supercomputers, data center servers and the specialized computers that direct autonomous cars and drones with collision detection. Eventually, the technology could reach home computers and mobile devices and improve applications from gaming to video streaming.

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Researchers in Europe and the UK have managed to connect biological and artificial neurons together – and allow them to communicate long distances through the internet. The biological neurons were grown in one country, sent signals through an artificial synapse located in another to electronic neurons in a third country.

As advanced as supercomputers get, the human brain still utterly leaves them in the dust. It’s made up of neurons that communicate with each other through pulses of electrical signals, passed across tiny gaps known as synapses. These neurons can both process and store information, unlike computers that require separate types of memory for each task.

Artificial versions of neurons and synapses have shown to be far more powerful than traditional computer chip designs, but they’re still in the experimental stage. And now, a team of researchers has taken the next step and connected the artificial and biological versions between three different countries.

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QuTech has resolved a major issue on the road toward a working large-scale quantum computer. QuTech, a collaboration of TU Delft and TNO, and Intel have designed and fabricated an integrated circuit that can controlling qubits at extremely low temperatures. This paves the way for the crucial integration of qubits and their controlling electronics in the same chip. The scientists have presented their research during the ISSCC Conference in San Francisco.

Quantum computers

“This result brings us closer to a large-scale quantum computer which can solve problems that are intractable by even the most powerful supercomputers. Solutions to those problems can make a strong impact on , for instance in the fields of medicine and energy,” said team lead Fabio Sebastiano from QuTech and the Faculty of Electrical Engineering, Mathematics and Computer Science.

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Moving ever closer to the Web v.5.0 – an immersive virtual playground of the Metaverse – would signify a paramount convergent moment that MIT’s Rizwan Virk calls ‘The Simulation Point’ and I prefer to call the ‘Simulation Singularity’. Those future virtual worlds could be wholly devised and “fine-tuned” with a possibility to encode different sets of “physical laws and constants” for our enjoyment and exploration.

We are in the “kindergarten of godlings” right now. One could easily envision that with exponential development of AI-powered multisensory immersive technologies, by the mid-2030s most of us could immerse in “real virtualities” akin to lifestyles of today’s billionaires. Give it another couple of decades, each of us might opt to create and run their own virtual universe with [simulated] physics indistinguishable from the physics of our world. Or, you can always “fine-tune” the rule set, or tweak historical scenarios at will.

How can we be so certain about the Simulation Singularity circa 2035? By our very nature, we humans are linear thinkers. We evolved to estimate a distance from the predator or to the prey, and advanced mathematics is only a recent evolutionary addition. This is why it’s so difficult even for a modern man to grasp the power of exponentials. 40 steps in linear progression is just 40 steps away; 40 steps in exponential progression is a cool trillion (with a T) – it will take you 3 times from Earth to the Sun and back to Earth.

This illustrates the power of exponential growth and this is how the progress in information and communication technologies is now literally exploding – by double-improving price-to-performance ratio roughly once a year. This is why you can see memory cards jumping regularly from 32MB to 64MB, then to 128MB, 256MB and 512MB. This is why your smartphone is as capable as a supercomputer 25 years ago. This is why telecommunication carriers are actively deploying 5G wireless networks, as you read this article.

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This story begins in 1985 when at age 22, I became the World Chess Champion after beating Anatoly Karpov.

We must face our fears if we want to get the most out of technology — and we must conquer those fears if we want to get the best out of humanity, says Garry Kasparov. One of the greatest chess players in history, Kasparov lost a memorable match to IBM supercomputer Deep Blue in 1997. Now he shares his vision for a future where intelligent machines help us turn our grandest dreams into reality.

This story begins in 1985, when at age 22, I became the World Chess Champion after beating Anatoly Karpov. Earlier that year, I played what is called simultaneous exhibition against 32 of the world’s best chess-playing machines in Hamburg, Germany. I won all the games, and then it was not considered much of a surprise that I could beat 32 computers at the same time. To me, that was the golden age.

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Amid mounting concern about a novel coronavirus spreading from China, Lawrence Livermore National Laboratory (LLNL) researchers have developed a preliminary set of predictive 3D protein structures of the virus to aid research efforts to combat the disease.

The models are based on the genomic sequence of the novel coronavirus and a protein found in the virus that causes Severe Acute Respiratory Syndrome (SARS), which closely resembles the new virus.

The researchers plan to use the models to accelerate countermeasure design, using a combination of machine learning, biological experiments and simulation on supercomputers.

As global concern continues to rise about a novel coronavirus spreading from China, a team of Lawrence Livermore National Laboratory (LLNL) researchers has developed a preliminary set of predictive 3D protein structures of the virus to aid research efforts to combat the disease.

The team’s predicted 3D models, developed over the past week using a previously peer-reviewed modeling process, are based on the genomic sequence of the novel coronavirus and the known structure of a protein found in the virus that causes Severe Acute Respiratory Syndrome (SARS), also a coronavirus that closely resembles the new virus.

“A major part of the value of these new structural models is that they present the predicted protein in complex with SARS-neutralizing antibodies,” said Adam Zemla, an LLNL structural biologist and mathematician. “This can be thought of as the first step for the global research community to identify and model how therapeutic antibodies can be designed to fight the novel coronavirus.”

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“Think what we can do if we teach a quantum computer to do statistical mechanics,” posed Michael McGuigan, a computational scientist with the Computational Science Initiative at the U.S. Department of Energy’s Brookhaven National Laboratory.

At the time, McGuigan was reflecting on Ludwig Boltzmann and how the renowned physicist had to vigorously defend his theories of . Boltzmann, who proffered his ideas about how atomic properties determine physical properties of matter in the late 19th century, had one extraordinarily huge hurdle: atoms were not even proven to exist at the time. Fatigue and discouragement stemming from his peers not accepting his views on atoms and physics forever haunted Boltzmann.

Today, Boltzmann’s factor, which calculates the probability that a system of particles can be found in a specific energy state relative to zero energy, is widely used in physics. For example, Boltzmann’s factor is used to perform calculations on the world’s largest supercomputers to study the behavior of atoms, molecules, and the quark “soup” discovered using facilities such as the Relativistic Heavy Ion Collider located at Brookhaven Lab and the Large Hadron Collider at CERN.

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Circa 2017

Thousands of years of human knowledge has been learned and surpassed by the world’s smartest computer in just 40 days, a breakthrough hailed as one of the greatest advances ever in artificial intelligence.

Google DeepMind amazed the world last year when its AI programme AlphaGo beat world champion Lee Sedol at Go, an ancient and complex game of strategy and intuition which many believed could never be cracked by a machine.

AlphaGo was so effective because it had been programmed with millions of moves of past masters, and could predict its own chances of winning, adjusting its game-plan accordingly.

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