How to design materials with reprogrammable shape and function
Harvard researchers have developed a general framework to design reconfigurable metamaterials that is scale independent, meaning it can be applied to everything from meter-scale architectures to reconfigurable nano-scale systems (Image courtesy of Johannes Overvelde/Harvard SEAS).
Metamaterials — materials whose function is determined by structure, not composition — have been designed to
bend light and sound,
transform from soft to stiff, and
even dampen seismic waves from earthquakes.
But each of these functions requires a unique mechanical structure, making these materials great for specific tasks, but difficult to implement broadly.
But what if a material could contain within its structure, multiple functions and easily and autonomously switch between them?
“In terms of reconfigurable metamaterials, the design space is
incredibly large and so the challenge is to come up with smart
strategies to explore it,” said Katia Bertoldi,
John L. Loeb Associate Professor of the Natural Sciences at SEAS and
senior author of the paper. “Through a collaboration with designers and
mathematicians, we found a way to generalize these rules and quickly
generate a lot of interesting designs.”
Bertoldi and former graduate student Johannes Overvelde, who is the first author of the paper, collaborated with Chuck Hoberman, of the Harvard Graduate School of Design (GSD) and associate faculty at the Wyss and James Weaver, a senior research scientist at the Wyss, to design the metamaterial.
The research began in 2014, when Hoberman showed Bertoldi his original designs for a family of foldable structures, including a prototype of an extruded cube. “We were amazed by how easily it could fold and change shape,” said Bertoldi. “We realized that these simple geometries could be used as building blocks to form a new class of reconfigurable metamaterials but it took us a long time to identify a robust design strategy to achieve this.”
The interdisciplinary team realized thatassemblies of polyhedra can be used as a template to design extruded reconfigurable thin-walled structures, dramatically simplifying the design process.
“By combining design and computational modeling, we were able to identify a wide range of different rearrangements and create a blueprint or DNA for building these materials in the future, ” said Overvelde, now scientific group leader of the Soft Robotic Matter group at FOM Institute AMOLF in the Netherlands.
The same computational models can also be used to quantify all the different ways in which the material could bend and how that affected effective material properties like stiffness. This way they could quickly scan close to a million different designs, and select those with the preferred response.
Once a specific design was selected, the team constructed working prototypes of each 3D metamaterial both using laser-cut cardboard and double-sided tape, and multimaterial 3D printing. Like origami, the resulting structure can be folded along their edges to change shape.
“Now that we’ve solved the problem of formalizing the design, we can start to think about new ways to fabricate and reconfigure these metamaterials at smaller scales, for example through the development of 3D-printed self actuating environmentally responsive prototypes,” said Weaver.
This formalized design framework could be useful for
structural and aerospace engineers,
material scientists,
physicists,
robotic engineers,
biomedical engineers,
designers and
architects.
“This framework is like a toolkit to build reconfigurable materials,” said Hoberman. “These building blocks and design space are incredibly rich and we’ve only begun to explore all the things you can build with them.”
This work was supported by the Materials Research Science and Engineering Center and the National Science Foundation. ORIGINAL:Harvard SEAS
By Leah Burrows
January 18, 2017
Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics.
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players.
But there is a problem. There is no mathematical reason why networks arranged in layers should be so good at these challenges. Mathematicians are flummoxed. Despite the huge success of deep neural networks, nobody is quite sure how they achieve their success.
Today that changes thanks to the work of Henry Lin at Harvard University and Max Tegmark at MIT. These guys say the reason why mathematicians have been so embarrassed is that the answer depends on the nature of the universe. In other words, the answer lies in the regime of physics rather than mathematics.
First, let’s set up the problem using the example of classifying a megabit grayscale image to determine whether it shows a cat or a dog.
Such an image consists of a million pixels that can each take one of 256 grayscale values. So in theory, there can be 2561000000 possible images, and for each one it is necessary to compute whether it shows a cat or dog. And yet neural networks, with merely thousands or millions of parameters, somehow manage this classification task with ease.
In the language of mathematics, neural networks work by approximating complex mathematical functions with simpler ones. When it comes to classifying images of cats and dogs, the neural network must implement a function that takes as an input a million grayscale pixels and outputs the probability distribution of what it might represent.
The problem is that there are orders of magnitude more mathematical functions than possible networks to approximate them. And yet deep neural networks somehow get the right answer.
Now Lin and Tegmark say they’ve worked out why. The answer is that the universe is governed by a tiny subset of all possible functions. In other words, when the laws of physics are written down mathematically, they can all be described by functions that have a remarkable set of simple properties.
So deep neural networks don’t have to approximate any possible mathematical function, only a tiny subset of them.
To put this in perspective, consider the order of a polynomial function, which is the size of its highest exponent. So a quadratic equation like y=x2 has order 2, the equation y=x24 has order 24, and so on.
Obviously, the number of orders is infinite and yet only a tiny subset of polynomials appear in the laws of physics. “For reasons that are still not fully understood, our universe can be accurately described by polynomial Hamiltonians of low order,” say Lin and Tegmark. Typically, the polynomials that describe laws of physics have orders ranging from 2 to 4.
The laws of physics have other important properties. For example, they are usually symmetrical when it comes to rotation and translation. Rotate a cat or dog through 360 degrees and it looks the same; translate it by 10 meters or 100 meters or a kilometer and it will look the same. That also simplifies the task of approximating the process of cat or dog recognition.
These properties mean that neural networks do not need to approximate an infinitude of possible mathematical functions but only a tiny subset of the simplest ones.
There is another property of the universe that neural networks exploit. This is the hierarchy of its structure. “Elementary particles form atoms which in turn form molecules, cells, organisms, planets, solar systems, galaxies, etc.,” say Lin and Tegmark. And complex structures are often formed through a sequence of simpler steps.
This is why the structure of neural networks is important too: the layers in these networks can approximate each step in the causal sequence.
Lin and Tegmark give the example of the cosmic microwave background radiation, the echo of the Big Bang that permeates the universe. In recent years, various spacecraft have mapped this radiation in ever higher resolution. And of course, physicists have puzzled over why these maps take the form they do.
Tegmark and Lin point out that whatever the reason, it is undoubtedly the result of a causal hierarchy. “A set of cosmological parameters (the density of dark matter, etc.) determines the power spectrum of density fluctuations in our universe, which in turn determines the pattern of cosmic microwave background radiation reaching us from our early universe, which gets combined with foreground radio noise from our galaxy to produce the frequency-dependent sky maps that are recorded by a satellite-based telescope,” they say.
Each of these causal layers contains progressively more data. There are only a handful of cosmological parameters but the maps and the noise they contain are made up of billions of numbers. The goal of physics is to analyze the big numbers in a way that reveals the smaller ones.
And when phenomena have this hierarchical structure, neural networks make the process of analyzing it significantly easier.
“We have shown that the success of deep and cheap learning depends not only on mathematics but also on physics, which favors certain classes of exceptionally simple probability distributions that deep learning is uniquely suited to model,” conclude Lin and Tegmark.
That’s interesting and important work with significant implications. Artificial neural networks are famously based on biological ones. So not only do Lin and Tegmark’s ideas explain why deep learning machines work so well, they also explain why human brains can make sense of the universe. Evolution has somehow settled on a brain structure that is ideally suited to teasing apart the complexity of the universe.
This work opens the way for significant progress in artificial intelligence. Now that we finally understand why deep neural networks work so well, mathematicians can get to work exploring the specific mathematical properties that allow them to perform so well. “Strengthening the analytic understanding of deep learning may suggest ways of improving it,” say Lin and Tegmark.
Deep learning has taken giant strides in recent years. With this improved understanding, the rate of advancement is bound to accelerate.
Researchers have just discovered evidence of a mysterious new state of matter in a real material. The state is known as 'quantum spin liquid' and it causes electrons - one of the fundamental, indivisible building blocks of matter - to break down into smaller quasiparticles.
Scientists had first predicted the existence of this state of matter in certain magnetic materials 40 years ago, but despite multiple hints of its existence, they've never been able to detect evidence of it in nature. So it's pretty exciting that they've now caught a glimpse of quantum spin liquid, and the bizarre fermions that accompany it, in a two-dimensional, graphene-like material.
"This is a new quantum state of matter, which has been predicted but hasn't been seen before," said one of the researchers, Johannes Knolle, from the University of Cambridge in the UK.
They were able to spot evidence of quantum spin liquid in the material by observing one of its most intriguing properties - electron fractionalisation - and the resulting Majorana fermions, which occur when electrons in a quantum spin state split apart. These Majorana fermions are exciting because they could be used as building blocks of quantum computers.
To be clear, the electrons aren't actually splitting down into smaller physical particles - which of course would be an even bigger deal (that would mean brand new particles!). What's happening instead is the new state of matter is breaking electrons down into quasiparticles.These aren't actually real particles, but are concepts used by physicists to explain and calculate the strange behaviour of particles.
And the quantum spin liquid state is definitely making electrons act weirdly - in a typical magnetic material, electrons behave like tiny bar magnets. So when the material is cooled to a low enough temperature, these magnet-like electrons order themselves over long ranges, so that all the north magnetic poles point in the same direction.
But in a material containing a quantum spin liquid state, even if a magnetic material is cooled to absolute zero, the electrons don't align, but instead form an entangled soup caused by quantum fluctuations.
"Until recently, we didn't even know what the experimental fingerprints of a quantum spin liquid would look like," said one of the researchers, Dmitry Kovrizhin. "One thing we've done in previous work is to ask, if I were performing experiments on a possible quantum spin liquid, what would I observe?"
To figure out what was going on, the researchers worked alongside a team from Oak Ridge National Laboratory in Tennessee and used neutron scattering techniques to look for evidence of electron fractionalisation in alpha-ruthenium chloride - a material that's structurally similar to graphene.
This also allowed them to measure the signatures of Majorana fermions for the first time by illuminating the material with neutrons, and then observing the pattern of ripples that the neutrons produced when scattered from the sample.
These patterns were exactly what they'd expect to see based on the main theoretical model of quantum spin liquid, confirming for the first time that they'd seen evidence of it happening in a material.
"This is a new addition to a short list of known quantum states of matter," said Knolle.
"It's an important step for our understanding of quantum matter," added Kovrizhin. "It's fun to have another new quantum state that we've never seen before - it presents us with new possibilities to try new things."
Some of those new things involve quantum computers - which would be exponentially faster than regular computers - so even though all of this sounds pretty theoretical, they could actually have some really exciting potential applications.
Chinese scientists were able to heat plasma to three times the temperature of the core of our sun for a record-breaking 102 seconds as they progressed the search to derive energy from nuclear fusion. Photo: Wikipedia
In a doughnut-shaped chamber in eastern China, scientists have been able to produce hydrogen gas more than three times hotter than the core of the Sun using nuclear fusion - and maintain this temperature for 102 seconds.
The breakthrough puts China one step ahead in the global race to harness a new, artificial kind of solar energy for clean and unlimited energy, the researchers claim. This has become a pressing concern as more of the earth’s natural reserves are rapidly depleting.
The experiment was conducted last week on a magnetic fusion reactor at the Institute of Physical Science in Hefei, capital of Jiangsu province, according to a statement on the institute’s website on Wednesday.
The reactor, officially known as the Experimental Advanced Superconducting Tokamak (EAST), was able to heat a hydrogen gas - a hot ionised gas called a plasma - to about 50 million Kelvins (49.999 million degrees Celsius).The interior of our sun is calculated to be around 15 million Kelvins.
According to this thermodynamic scale, absolute zero occurs at zero degrees (equivalent to minus 273.15 degrees Celsius), a point at which all molecular movement stops.
The temperature reached in Hefei was at the other end of the scale, and roughly the same as a mid-sized thermonuclear explosion. The goal of the experiment was to approximate the nuclear fusion conditions that occur deep inside the sun.
Although at least one other experiment in the last decade claims to have produced a hotter temperatures than this, it has never been duplicated and was unable to match the endurance - over one and a half minutes - of the Chinese test.
Meanwhile, physicists in Japan and Europe have been able to reach the same temperature as the Chinese team, but not for longer than a minute due to concerns of provoking a reactor meltdown.
The EAST was invented by Soviet scientists to control nuclear fusion for power generation.
As a tokamak device, it uses a powerful magnetic field to confine plasma in the shape of a torus - imagine a large spinning doughnut -for safety reasons due to the phenomenally high temperatures being generated. The atoms are effectively held floating in place by superconducting magnets.
But controlling hydrogen gas in such a hot and volatile state is a formidable challenge, and one that most of the tokomak devices built over the last 60 years have not been able to sustain for more than 20 seconds.
The scientists in Hefei worked “day and night” to achieve the record level of endurance, according to the institute, which serves as a subsidiary of the Chinese Academy of Sciences.
The team claimed to have solved a number of scientific and engineering problems, such as precisely controlling the alignment of the magnet, and managing to capture the high-energy particles and heat escaping from the “doughnut”.
But they still missed their mark, which was to reach 100 million Kelvins for over 1,000 seconds (nearly 17 minutes), they said, adding that it would still take years to build a commercially viable plant that could operate in a stable manner for several decades.
Unlike the process of nuclear fission that fuels thermal power stations around the world today by splitting the atoms of fissile materials such as uranium, fusion reactions work by “fusing” two light atomic nuclei - for example, two hydrogen atoms - together to release a huge amount of heat.
This can produce levels of energy three to four times greater than the results of nuclear fission. It also generate almost no radioactive waste.
The problem is the amount of heat created. Whereas nuclear fission only generates a few hundred degrees Celsius, fusion requires at least 100 million degrees Celsius (212 million degrees Fahrenheit).
A researcher involved with the EAST project said data from their experiment may be of use to the International Thermonuclear Experimental Reactor (ITER) that is now under construction in France.
Meanwhile, another 1-billion-euro (US$1.12 billion) project in Germany dubbed the “stellarator” claimed last December to have achieved another milestone in the nuclear fusion quest by heating plasma to around 1 million degrees Celsius for one-tenth of a second.
China ranks as a member country of the ITER project, which aims to produce 500 megawatts of fusion power for 400 seconds. But Beijing has expressed frustration with the slow pace of development, according to the same researcher, who asked not be identified.
The multibillion US dollar project was initially scheduled to become operational this year. But due to a series of setbacks, many now suspect it will need at least another decade.
“Political infighting among different nations about the project’s budget, personnel appointments and other issues are hampering the pace of the project,” said the researcher.
“If this chaotic situation continues, other projects in countries like the United States and China may overtake this collective, international effort.”
photo credit: The experimental fusion reactor. Max Planck Institute
Scientists at the Max Planck Institute in Germany have successfully conducted a revolutionary nuclear fusion experiment. Using their experimental reactor, the Wendelstein 7-X(W7X) stellarator, they have managed to sustain a hydrogen plasma – a key step on the path to creating workable nuclear fusion. The German chancellor Angela Merkel, who herself has a doctorate in physics, switched on the device at 2:35 p.m. GMT (9:35 a.m. EST).
Published on Feb 3, 2016 Federal Chancellor Angela Merkel switched on the first hydrogen plasma on 3 February 2016 at a ceremony attended by numerous guests from the realms of science and politics. This will mark the start of scientific operation of Wendelstein 7-X.
As a clean, near-limitless source of energy, it’s no understatement to say that controlled nuclear fusion (replicating the process that powers the Sun) would change the world, and several nations are striving to make breakthroughs in this field. Germany is undoubtedly the frontrunner in one respect: This is the second time that it’s successfully fired up its experimental fusion reactor.
Last December, the team managed to suspend a helium plasma for the first time in history, and they’ve now achieved the same feat with hydrogen. Generating a hydrogen plasma is considerably more difficult than producing a helium one, so by producing and sustaining one in today’s experiment, even for just a few milliseconds, these researchers have achieved something truly remarkable.
Photo: The first hydrogen plasma in Wendelstein 7-X. Photo: (IPP) Max Planck Institute for Plasma Physics.
As a power source, hydrogen fusion releases far more energy than helium fusion, which is why sustaining a superheated hydrogen plasma represents such a huge step for nuclear fusion research.
John Jelonnek, a physicist at the Karlsruhe Institute of Technology, led a team that was responsible for installing the powerful heating components of the reactor. “We’re not doing this for us,” he told the Guardian, “but for our children and grandchildren.”
When Albert Einstein first predicted that light travels the same speed everywhere in our Universe, he essentially stamped a speed limit on it: 299,792 kilometres per second (186,282 miles per second) - fast enough to circle the entire Earth eight times every second. But that's not the whole story. In fact, it's just the beginning.
Before Einstein, mass - the atoms that make up you, me, and everything we see - and energy were treated as separate entities. But in 1905, Einstein forever changed the way physicists view the Universe.
Einstein's special theory of relativity permanently tied mass and energy together in the simple yet fundamental equation E = mc2. This little equation predicts that nothing with mass can move as fast as light, or faster. The closest humankind has ever come to reaching the speed of light is inside of powerful particle accelerators like the Large Hadron Collider and the Tevatron.
To do so would require an infinite amount of energy and, in the process, the object's mass would become infinite, which is impossible. (The reason particles of light, called photons, travel at light speeds is because they have no mass.)
Since Einstein, physicists have found that certain entities can reach superluminal (that means "faster-than-light") speeds and still follow the cosmic rules laid down by special relativity. While these do not disprove Einstein's theory, they give us insight into the peculiar behavior of light and the quantum realm.
The light equivalent of a sonic boom
When objects travel faster than the speed of sound, they generate a sonic boom. So, in theory, if something travels faster than the speed of light, it should produce something like a "luminal boom". In fact, this light boom happens on a daily basis in facilities around the world - you can see it with your own eyes. It's called Cherenkov radiation, and it shows up as a blue glow inside of nuclear reactors, like in the image above.
Cherenkov radiation is named for Soviet scientist Pavel Alekseyevich Cherenkov, who first measured it in 1934 and was awarded the Nobel Physics Prize in 1958 for his discovery.
Cherenkov radiation glows because the core of the Advanced Test Reactor is submerged in water to keep it cool. In water, light travels at 75 percent the speed it would in the vacuum of outer space, but the electrons created by the reaction inside of the core travel through the water faster than the light does.
Particles, like these electrons, that surpass the speed of light in water, or some other medium such as glass, create a shock wave similar to the shock wave from a sonic boom.
When a rocket, for example, travels through air, it generates pressure waves in front that move away from it at the speed of sound, and the closer the rocket reaches that sound barrier, the less time the waves have to move out of the object's path. Once it reaches the speed of sound, the waves bunch up creating a shock front that forms a loud sonic boom.
Similarly, when electrons travel through water at speeds faster than light speed in water, they generate a shock wave of light that sometimes shines as blue light, but can also shine in ultraviolet. While these particles are traveling faster than light does in water, they're not actually breaking the cosmic speed limit of 299,792 kilometres per second (186,282 miles per second).
When the rules don't apply
Keep in mind that Einstein's special theory of relativity states that nothing with mass can go faster than the speed of light, and as far as physicists can tell, the Universe abides by that rule. But what about something without mass?
Photons, by their very nature, cannot exceed the speed of light, but particles of light are not the only massless entity in the universe. Empty space contains no material substance and therefore, by definition, has no mass. "Since nothing is just empty space or vacuum, it can expand faster than light speed since no material object is breaking the light barrier," said theoretical astrophysicist Michio Kakuon Big Think. "Therefore, empty space can certainly expand faster than light."
This is exactly what physicists think happened immediately after the Big Bang during the epoch called inflation, which was first hypothesised by physicists Alan Guth and Andrei Linde in the 1980s. Within a trillionth of a trillionth of a second, the Universe repeatedly doubled in size and as a result, the outer edge of the universe expanded very quickly, much faster than the speed of light.
Quantum entanglement makes the cut
"If I have two electrons close together, they can vibrate in unison, according to the quantum theory," Kaku explains on Big Think. Now, separate those two electrons so that they're hundreds or even thousands of light years apart, and they will keep this instant communication bridge open. (Entanglement)
"If I jiggle one electron, the other electron 'senses' this vibration instantly, faster than the speed of light. Einstein thought that this therefore disproved the quantum theory, since nothing can go faster than light," Kaku wrote.
In fact, in 1935, Einstein, Boris Podolsky and Nathan Rosen, attempted to disprove quantum theory with a thought experiment on what Einstein referred to as "spooky action at a distance".
Ironically, their paper laid the foundation for what today is called the EPR (Einstein-Podolsky-Rosen) paradox, a paradox that describes this instantaneous communication of quantum entanglement - an integral part of some of the world's most cutting-edge technologies, like quantum cryptography.
Dreaming of wormholes
Since nothing with mass can travel faster than light, you can kiss interstellar travel goodbye - at least, in the classical sense of rocketships and flying.
Although Einstein trampled over our aspirations of deep-space roadtrips with his theory of special relativity, he gave us a new hope for interstellar travel with his general theory of relativity in 1915. While special relativity wed mass and energy, general relativity wove space and time together.
"The only viable way of breaking the light barrier may be through general relativity and the warping of space time," Kaku writes. This warping is what we colloquially call a wormhole, which theoretically would let something travel vast distances instantaneously, essentially enabling us to break the cosmic speed limit by traveling great distances in a very short amount of time.
In 1988, theoretical physicist Kip Thorne - the science consultant and executive producer for the recent film Interstellar - used Einstein's equations of general relativity to predict the possibility of wormholes that would forever be open for space travel. But in order to be traversable, these wormholes need some strange, exotic matter holding them open.
"Now it is an amazing fact that exotic matter can exist, thanks to weirdnesses in the laws of quantum physics," Thorne writes in his book The Science of Interstellar.
And this exotic matter has even been made in laboratories here on Earth, but in very tiny amounts. When Thorne proposed his theory of stable wormholes in 1988 he called upon the physics community to help him determine if enough exotic matter could exist in the Universe to support the possibility of a wormhole.
"This triggered a lot of research by a lot of physicists; but today, nearly 30 years later, the answer is still unknown." Thorne writes. At the moment, it's not looking good, "But we are still far from a final answer," he concludes.
Her research could change our understanding of the fundamentals as we know them.
One of the things the brilliant minds at MIT do — besides ponder the nature of the universe and build sci-fi gizmos, of course — is notarize aircraft airworthiness for the federal government. So when Sabrina Gonzalez Pasterski walked into the campus offices one cold January morning seeking the OK for a single-engine plane she had built, it might have been business as usual. Except that the shaggy-haired, wide-eyed plane builder before them was just 14 and had already flown solo.“I couldn’t believe it,” recalls Peggy Udden, an executive secretary at MIT, “not only because she was so young, but a girl.”
OK, it’s 2016, and gifted females are not exactly rare at MIT; nearly half the undergrads are women. But something about Pasterski led Udden not just to help get her plane approved, but to get the attention of the university’s top professors. Now, eight years later, the lanky, 22-year-old Pasterski is already an MIT graduate and Harvard Ph.D. candidate who has the world of physics abuzz. She’s exploring some of the most challenging and complex issues in physics, much as Stephen Hawking and Albert Einstein (whose theory of relativity just turned 100 years old) did early in their careers. Her research delves into black holes, the nature of gravity and spacetime. A particular focus is trying to better understand “quantum gravity,” which seeks to explain the phenomenon of gravity within the context of quantum mechanics. Discoveries in that area could dramatically change our understanding of the workings of the universe.
Among the many skills she lists on her no-frills website: “spotting elegance within the chaos.”
She’s also caught the attention of some of America’s brightest working at NASA. Also? Jeff Bezos, founder of Amazon.com and aerospace developer and manufacturer Blue Origin, who’s promised her a job whenever she’s ready. Asked by e-mail recently whether his offer still stands, Bezos told OZY: “God, yes!”
But unless you’re the kind of rabid physics fan who’s seen her papers on semiclassical Virasoro symmetry of the quantum gravity S-matrix and Low’s subleading soft theorem as a symmetry of QED (both on approaches to understanding the shape of space and gravity and the first two papers she ever authored), you may not have heard of Pasterski. A first-generation Cuban-American born and bred in the suburbs of Chicago, she’s not on Facebook, LinkedIn or Instagram and doesn’t own a smartphone. She does, however, regularly update a no-frills website called PhysicsGirl, which features a long catalog of achievements and proficiencies. Among them: “spotting elegance within the chaos.”
Pasterski stands out among a growing number of newly minted physics grads in the U.S. There were 7,329 in 2013, double the four-decade low of 3,178 in 1999, according to the American Institute of Physics. Nima Arkani-Hamed, a Princeton professor and winner of the inaugural $3 million Fundamental Physics Prize, told OZY he’s heard “terrific things” about Pasterski from her adviser, Harvard professor Andrew Strominger, who is about to publish a paper with physics rock star Hawking. She’s also received hundreds of thousands of dollars in grants from the Hertz Foundation, the Smith Foundation and the National Science Foundation.
Pasterski, who speaks in frenetic bursts, says she has always been drawn to challenging what’s possible. “Years of pushing the bounds of what I could achieve led me to physics,” she says from her dorm room at Harvard. Yet she doesn’t make it sound like work at all: She calls physics “elegant” but also full of “utility.”
Despite her impressive résumé, MIT wait-listed Pasterski when she first applied. Professors Allen Haggerty and Earll Murman were aghast. Thanks to Udden, the pair had seen a video of Pasterski building her airplane. “Our mouths were hanging open after we looked at it,” Haggerty said. “Her potential is off the charts.” The two went to bat for her, and she was ultimately accepted, later graduating with a grade average of 5.00, the school’s highest score possible.
An only child, Pasterski speaks with some awkwardness and punctuates her e-mails with smiley faces and exclamation marks. She says she has a handful of close friends but has never had a boyfriend, an alcoholic drink or a cigarette. Pasterski says: “I’d rather stay alert, and hopefully I’m known for what I do and not what I don’t do.”
While mentors offer predictions of physics fame, Pasterski appears well grounded. “A theorist saying he will figure out something in particular over a long time frame almost guarantees that he will not do it,” she says. And Bezos’s pledge notwithstanding, the big picture for science grads in the U.S. is challenging: The U.S. Census Bureau’s most recent American Community Survey shows that only about 26 percent of science grads in the U.S. had jobs in their chosen fields, while nearly 30 percent of physics and chemistry post-docs are unemployed. Pasterski seems unperturbed. “Physics itself is exciting enough,” she says. ”It’s not like a 9-to-5 thing. When you’re tired you sleep, and when you’re not, you do physics.”
Scientists are exploiting the laws of quantum mechanics to create computers with an exponential increase in computing power.
Quantum computing
Since their creation in the 1950s and 1960s, digital computers have become a mainstay of modern life. Originally taking up entire rooms and taking many hours to perform simple calculations, they have become both highly portable and extremely powerful. Computers can now be found in many people’s pockets, on their desks, in their watches, their televisions and their cars. Our demand for processing power continues to increase as more people connect to the internet and the integration of computing into our lives increases.
Video source: In a nutshell - Kurzgesagt / YouTube. .View video details.
When Moore’s Law meets quantum mechanics
In 1965, Gordon Moore, co-founder of Intel, one of the world’s largest computer companies, first described what has now become known as Moore’s Law. An observation rather than a physical law, Moore noticed that the number of components that could fit on a computer chip doubled roughly every two years, and this observation has proven to hold true over the decades. Accordingly, the processing power and memory capacity of computers has doubled every two years as well.
Starting from computer chips that held a few thousand components in the 1960s, chips today hold several billion components. There is a physical limit to how small these components can get, and as they get near the size of an atom, the quirky rules that govern quantum mechanics come into play. These rules that govern the quantum world are so different from those of the macro world that our traditional understanding of binary logic in a computer doesn’t really work effectively any more. Quantum laws are based on probabilities, so a computer on this scale no longer works in a ‘deterministic’ manner, which means it gives us a definite answer. Rather, it starts to behave in a ‘probablistic’ way—the answer the computer would give us is based on probabilities, each result could fluctuate and we would have to try several times to get a reliable answer.
So if we want to keep increasing computer power, we are going to have to find a new way. Instead of being stymied or trying to avoid the peculiarities of quantum mechanics, we must find ways to exploit them.
In the computer that sits on your desk, your smartphone, or the biggest supercomputer in the world, information, be it text, pictures or sound is stored very simply as a number. The computer does its job by performing arithmetic calculations upon all these numbers. For example, every pixel in a photo is assigned numbers that represents its colour or brightness, numbers that can then be used in calculations to change or alter the image.
The computer saves these numbers in binary form instead of the decimal form that we use every day. In binary, there are only two numberss: 0 and 1. In a computer, these are known as ‘bits’, short for ‘binary digits’. Every piece of information in your computer is stored as a string of these 0s and 1s. As there are only two options, the 1 or the 0, it’s easy to store these using a number of different methods—for example, as magnetic dots on a hard drive, where the bit is either magnetised one way (1) or another (0), or where the bit has a tiny amount of electrical charge (1) or no charge (0). These combinations of 0s and 1s can represent almost anything, including letters, sounds and commands that tell the computer what to do.
Instead of binary bits, a quantum computer uses qubits. These are particles, such as an atom, ion or photon, where the information is stored by manipulating the particles’ quantum properties, such as spin or polarisation states.
In a normal computer the many steps of a calculation are carried out one after the other. Even if the computer might work on several calculations in parallel, each calculation has to be done one step at a time. A quantum computer works differently. The qubits are programmed with a complex set of conditions, which formulates the question, and these conditions then evolve following the rules of the quantum world—Schrödinger’s wave equation—to find the answer. Each programmed qubit evolves simultaneously; all the steps of the calculation are taken at the same time. Mathematicians have found that this approach can solve a number of computational tasks that are very hard or time consuming on a classical computer. Thespeed advantage is enormous—and grows with the complexity we can program (i.e. the number of qubits the quantum computer has).
Superposition
Individually, each qubit has its own quantum properties, such as spin. This has two values +1 and -1, but can also be in what’s called a superposition: partly +1 and partly -1. If you think of a globe, you can point to the North Pole (+1) or the South Pole (-1) or any other point in between: London, or Sydney. A quantum particle can be a in a state that is part North Pole and part South Pole.
A qubit with superposition is in a much more complex state than the simple 1 or 0 of a binary bit. More parameters are required to describe that state, and this translates to the amount of information a qubit can hold and process.
Entanglement
Even more interesting is the fact that we can link many particles, each in their state of superposition, together. We can create a link, called entanglement, where all of these particles are dependent upon each other, all their properties exist at the same time. All the particles together are in one big state that evolves, according to the rules of quantum mechanics, as a single system. This is what gives quantum computers their power of parallel processing—the qubits all evolve, individual yet linked, simultaneously.
Imagine the complexity of all these combinations, all the superpositions. The number of parameters needed to fully describe N qubits grows as 2 to the power N. Basically, this means that for each qubit you add to the computer, the information required to describe the assembly of qubits doubles. Just 50 qubits would require more than a billion numbers to describe their collective states or contents. This is where the supreme power of a quantum computer lies, since the evolution in time of these qubits corresponds to a bigger calculation, without costing more time.
For the particular tasks suited to quantum computers, a quantum computer with 30 qubits would be more powerful than the world’s most powerful supercomputer, and a 300 qubit quantum computer would be more powerful than every computer in the world connected together.
A delicate operation
An important feature of these quantum rules is that they are very sensitive to outside interference. The qubits must be kept completely isolated, so they are only being controlled by the laws of quantum mechanics, and not influenced by any environmental factors. Any disturbance to the qubits will cause them to leave their state of superposition—this is called decoherence. If the qubits decohere, the computation will break down. Creating a totally quiet, isolated environment is one of the great challenges of building a quantum computer.
Another challenge is transferring information from the quantum processor to some sort of quantum memory system that can preserve the information so that we can then read the answer. Researchers are working on developing ‘non-demolition’ readouts—ways to read the output of a computation without breaking the computation.
What are quantum computers useful for?
A lot of coverage of the applications of quantum computers talk about the huge gains in processing power over classical computers. Many statements have been made about being able to effortlessly solve hard problems instantaneously but it’s not clear if all the promises will hold up. Rather than being able to solve all of the world’s financial, medical and scientific questions at the press of a button, it’s much more likely that, as with many major scientific projects, the knowledge gain that comes from building the computers will prove just as valuable as their potential applications.
The nearest term and most likely applications for quantum computers will be within quantum mechanics itself. Quantum computers will provide a useful new way of simulating and testing the workings of quantum theory, with implications for chemistry, biochemistry, nanotechnology and drug design. Search engine optimisation for internet searches, management of other types of big data and optimising other systems, such as fleet routing and manufacturing processes could also be impacted by quantum computing.
Another area where large scale quantum computers are predicted to have a big impact is that of data security. In a world where so much of our personal information is online, keeping our data—bank details or our medical records—secure is crucial. To keep it safe, our data is protected by encryption algorithms that the recipient needs to ‘unlock’ with a key. Prime number factoring is one method used to create encryption algorithms. The key is based on knowing the prime number factors of a large number. This sounds pretty basic, but it’s actually very difficult to figure out what the prime number factors of a large number are.
Classical computers can very easily multiply two prime numbers to find their product. But their only option when performing the operation in reverse is a repetitive process of checking one number after another. Even performing billions of calculations per second, this can take an extremely long time when the numbers get especially large. Once numbers reach over 1000 digits, figuring out its prime number factors is generally considered to take too long for a classical computer to calculate—the data encryption is ‘uncrackable’ and our data is kept safe and sound.
However, the superposed qubits of quantum computers change everything. In 1994, mathematician Peter Shor came up with an algorithm that would enable quantum computers to factor large prime numbers significantly faster than by classical methods. As quantum computing advances we may need to change the way we secure our data so that quantum computers can’t access it.
Beyond these applications that we can foretell, there will undoubtedly be many new applications appearing as the technology develops. With classical computers, it was impossible to predict the advances of the internet, voice recognition and touch interfaces that are today so commonplace. Similarly, the most important breakthroughs to come from quantum computing are likely still unknown.
Two teams from the University of NSW (UNSW) are using silicon to create extremely coherent qubits in new ways, which opens the door to creating quantum computers using easy to manufacture components. One team is focussing on using silicon transistors like those in our laptops and smartphones.
The other UNSW-led team is working to create qubits from phosphorus atoms embedded in silicon. In 2012, this team created atom-sized components ten years ahead of schedule by making the world’s smallest transistor.
They placed a single phosphorus atom on a sheet of silicon with all the necessary atomic-sized components that would be needed to apply a voltage to the phosphorus atom, giving it its spin state in order to function as a qubit.
The nuclear spins of single phosphorus atoms have been shown to have
the highest fidelity (>99%) and
longest coherence time (>35 seconds)
of any qubit in the solid state making them extremely attractive for a scalable system.
A team at the University of Queensland is working to develop quantum computing techniques using single photons as qubits. In 2010, this team has also conducted the first quantum chemistry simulation. This sort of a task involves computing the complex quantum interactions between electrons and requires such complicated equations that performing the calculations with a classical computer necessarily requires a trade-off between accuracy and computational feasibility. Qubits, being in a quantum state themselves, are much more capable of representing these systems, and so offer great potential to the field of quantum chemistry.
This group has also performed a demonstration of a quantum device using photons capable of performing a task that is factorially difficult – i.e. one of the specific tasks that classical computers get stuck with.
Large sums of money are being invested into Australian quantum computing research. In 2014, the Commonwealth Bank made an investment of $5 million towards the .Centre of Excellence for Quantum Computation and Communication Technology at the University of New South Wales. Microsoft has invested more than $10M in engineered quantum systems at University of Sydney, also in 2014.
It’s not very likely that in 20 years we’ll all be walking around with quantum devices in our pockets. Most likely, the first quantum computers will be servers that people will access to undertake complex calculations. However, it is not easy to predict the future, who would have thought fifty years ago, that we would enjoy the power and functionality of today’s computers, like the smartphones that so many of us now depend upon? Who can tell what technology will be at our beck and call if the power of quantum mechanics can be harvested?
