Periodic Table of Elements (International Union of Pure and Applied Chemistry)
Interactive Periodic Table of Elements (Los Alamos National Laboratory)
Periodic Table (American Chemical Society)
These are organized by a classification scheme developed exclusively for Cosma. More…
Periodic table is a tabular arrangement of the chemical elements, ordered by their atomic number, electron configuration, and recurring chemical properties, whose structure shows periodic trends. Generally, within one row (period) the elements are metals to the left, and non-metals to the right, with the elements having similar chemical behaviours placed in the same column. Table rows are commonly called periods and columns are called groups. Six groups have accepted names as well as assigned numbers: for example, group 17 elements are the halogens; and group 18 are the noble gases. Also displayed are four simple rectangular areas or blocks associated with the filling of different atomic orbitals.
The organization of the periodic table can be used to derive relationships between the various element properties, but also the predicted chemical properties and behaviours of undiscovered or newly synthesized elements. Russian chemist Dmitri Mendeleev was the first to publish a recognizable periodic table in 1869, developed mainly to illustrate periodic trends of the then-known elements. He also predicted some properties of unidentified elements that were expected to fill gaps within the table. Most of his forecasts proved to be correct. Mendeleev’s idea has been slowly expanded and refined with the discovery or synthesis of further new elements and the development of new theoretical models to explain chemical behaviour. The modern periodic table now provides a useful framework for analyzing chemical reactions, and continues to be widely used in chemistry, nuclear physics and other sciences.
All the elements from atomic numbers 1 (hydrogen) through 118 (oganesson) have been either discovered or synthesized, completing the first seven rows of the periodic table. The first 98 elements exist in nature, although some are found only in trace amounts and others were synthesized in laboratories before being found in nature. Elements 99 to 118 have only been synthesized in laboratories or nuclear reactors. The synthesis of elements having higher atomic numbers is currently being pursued: these elements would begin an eighth row, and theoretical work has been done to suggest possible candidates for this extension. Numerous synthetic radionuclides of naturally occurring elements have also been produced in laboratories. — Wikipedia
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Researchers test the way we understand forces in...
on April 1, 2020 at 5:50 pm
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on March 17, 2020 at 2:18 pm
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Scientists created an 'impossible'...
on March 3, 2020 at 1:41 pm
Scientists have created new superconducting compounds of hydrogen and praseodymium, a rare-earth metal, one substance being quite a surprise from the perspective of classical chemistry. The study helped find the optimal metals for room-temperature superconductors. The results were published in Science Advances.
Artificial atoms create stable qubits for quantum...
on February 11, 2020 at 9:00 am
Quantum engineers from UNSW Sydney have created artificial atoms in silicon chips that offer improved stability for quantum computing.
Quantum logic spectroscopy unlocks potential of...
on January 29, 2020 at 5:00 pm
Scientists from the PTB and the Max Planck Institute for Nuclear Physics (MPIK), both Germany, have carried out pioneering optical measurements of highly charged ions with unprecedented precision. To do this, they isolated a single Ar13 + ion from an extremely hot plasma and brought it practically to rest inside an ion trap together with a laser-cooled, singly charged ion. Employing quantum logic spectroscopy on the ion pair, they have increased the relative precision by a factor of a hundred […]
Current model for storing nuclear waste is...
on January 27, 2020 at 3:00 pm
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Stopping yellow spot fungus that attacks wheat...
on January 23, 2020 at 12:57 pm
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Making new catalysts from unique metallic alloys
on January 17, 2020 at 1:48 pm
Heusler alloys are magnetic materials made from three different metals that are not magnetic individually. The alloys are used broadly for their magnetic and thermoelectric properties, and their ability to regain their original shape after being deformed, known as shape memory. Investigations by Tohoku University's advanced materials scientist An-Pang Tsai and colleagues now show that these materials can also be fine-tuned to speed up chemical reactions. This catalytic capability is reviewed in […]
Potassium-driven rechargeable batteries: An...
on January 16, 2020 at 11:26 am
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Simulation of dwarf galaxy reveals different...
on January 10, 2020 at 11:40 am
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Quantum theory is the theoretical basis of modern physics that explains the nature and behavior of matter and energy on the atomic and subatomic level. The nature and behavior of matter and energy at that level is sometimes referred to as quantum physics and quantum mechanics.
In 1900, physicist Max Planck presented his quantum theory to the German Physical Society. Planck had sought to discover the reason that radiation from a glowing body changes in color from red, to orange, and, finally, to blue as its temperature rises. He found that by making the assumption that energy existed in individual units in the same way that matter does, rather than just as a constant electromagnetic wave - as had been formerly assumed - and was therefore quantifiable, he could find the answer to his question. The existence of these units became the first assumption of quantum theory.
Planck wrote a mathematical equation involving a figure to represent these individual units of energy, which he called quanta. The equation explained the phenomenon very well; Planck found that at certain discrete temperature levels (exact multiples of a basic minimum value), energy from a glowing body will occupy different areas of the color spectrum. Planck assumed there was a theory yet to emerge from the discovery of quanta, but, in fact, their very existence implied a completely new and fundamental understanding of the laws of nature. Planck won the Nobel Prize in Physics for his theory in 1918, but developments by various scientists over a thirty-year period all contributed to the modern understanding of quantum theory.
The Development of Quantum Theory
- In 1900, Planck made the assumption that energy was made of individual units, or quanta.
- In 1905, Albert Einstein theorized that not just the energy, but the radiation itself was quantized in the same manner.
- In 1924, Louis de Broglie proposed that there is no fundamental difference in the makeup and behavior of energy and matter; on the atomic and subatomic level either may behave as if made of either particles or waves. This theory became known as the principle of wave-particle duality: elementary particles of both energy and matter behave, depending on the conditions, like either particles or waves.
- In 1927, Werner Heisenberg proposed that precise, simultaneous measurement of two complementary values - such as the position and momentum of a subatomic particle - is impossible. Contrary to the principles of classical physics, their simultaneous measurement is inescapably flawed; the more precisely one value is measured, the more flawed will be the measurement of the other value. This theory became known as the uncertainty principle, which prompted Albert Einstein's famous comment, "God does not play dice."
The Copenhagen Interpretation and the Many-Worlds Theory
The two major interpretations of quantum theory's implications for the nature of reality are the Copenhagen interpretation and the many-worlds theory. Niels Bohr proposed the Copenhagen interpretation of quantum theory, which asserts that a particle is whatever it is measured to be (for example, a wave or a particle), but that it cannot be assumed to have specific properties, or even to exist, until it is measured. In short, Bohr was saying that objective reality does not exist. This translates to a principle called superposition that claims that while we do not know what the state of any object is, it is actually in all possible states simultaneously, as long as we don't look to check.
To illustrate this theory, we can use the famous and somewhat cruel analogy of Schrodinger's Cat. First, we have a living cat and place it in a thick lead box. At this stage, there is no question that the cat is alive. We then throw in a vial of cyanide and seal the box. We do not know if the cat is alive or if the cyanide capsule has broken and the cat has died. Since we do not know, the cat is both dead and alive, according to quantum law - in a superposition of states. It is only when we break open the box and see what condition the cat is that the superposition is lost, and the cat must be either alive or dead.
The second interpretation of quantum theory is the many-worlds (or multiverse theory. It holds that as soon as a potential exists for any object to be in any state, the universe of that object transmutes into a series of parallel universes equal to the number of possible states in which that the object can exist, with each universe containing a unique single possible state of that object. Furthermore, there is a mechanism for interaction between these universes that somehow permits all states to be accessible in some way and for all possible states to be affected in some manner. Stephen Hawking and the late Richard Feynman are among the scientists who have expressed a preference for the many-worlds theory.
Quantum Theory's Influence
Although scientists throughout the past century have balked at the implications of quantum theory - Planck and Einstein among them - the theory's principles have repeatedly been supported by experimentation, even when the scientists were trying to disprove them. Quantum theory and Einstein's theory of relativity form the basis for modern physics. The principles of quantum physics are being applied in an increasing number of areas, including quantum optics, quantum chemistry, quantum computing, and quantum cryptography.
See Brian Greene's introduction to quantum theory on Nova:


Donuts, math, and superdense teleportation of quantum information
Putting a hole in the center of the donut—a mid-nineteenth-century invention—allows the deep-fried pastry to cook evenly, inside and out. As it turns out, the hole in the center of the donut also holds answers for a type of more efficient and reliable quantum information teleportation, a critical goal for quantum information science.
Quantum teleportation is a method of communicating information from one location to another without moving the physical matter to which the information is attached. Instead, the sender (Alice) and the receiver (Bob) share a pair of entangled elementary particles—in this experiment, photons, the smallest units of light—that transmit information through their shared quantum state. In simplified terms, Alice encodes information in the form of the quantum state of her photon. She then sends a key to Bob over traditional communication channels, indicating what operation he must perform on his photon to prepare the same quantum state, thus teleporting the information. Quantum teleportation has been achieved by a number of research teams around the globe since it was first theorized in 1993, but current experimental methods require extensive resources and/or only work successfully a fraction of the time. Now, by taking advantage of the mathematical properties intrinsic to the shape of a donut—or torus, in mathematical terminology—a research team led by physicist Paul Kwiat of the University of Illinois at Urbana-Champaign has made great strides by realizing “superdense teleportation”. This new protocol, developed by coauthor physicist Herbert Bernstein of Hampshire College in Amherst, MA, effectively reduces the resources and effort required to teleport quantum information, while at the same time improving the reliability of the information transfer. With this new protocol, the researchers have experimentally achieved 88 percent transmission fidelity, twice the classical upper limit of 44 percent. The protocol uses pairs of photons that are “hyperentangled”—simultaneously entangled in more than one state variable, in this case in polarization and in orbital angular momentum—with a restricted number of possible states in each variable. In this way, each photon can carry more information than in earlier quantum teleportation experiments. At the same time, this method makes Alice’s measurements and Bob’s transformations far more efficient than their corresponding operations in quantum teleportation: the number of possible operations being sent to Bob as the key has been reduced, hence the term “superdense”. Kwiat explains, “In classical computing, a unit of information, called a bit, can have only one of two possible values—it’s either a zero or a one. A quantum bit, or qubit, can simultaneously hold many values, arbitrary superpositions of 0 and 1 at the same time, which makes faster, more powerful computing systems possible. “So a qubit could be represented as a point on a sphere, and to specify what state it is, one would need longitude and latitude. That’s a lot of information compared to just a 0 or a 1.” “What makes our new scheme work is a restrictive set of states. The analog would be, instead of using a sphere, we are going to use a torus, or donut shape. A sphere can only rotate on an axis, and there is no way to get an opposite point for every point on a sphere by rotating it—because the axis points, the north and the south, don’t move. With a donut, if you rotate it 180 degrees, every point becomes its opposite. Instead of axis points you have a donut hole. Another advantage, the donut shape actually has more surface area than the sphere, mathematically speaking—this means it has more distinct points that can be used as encoded information.” Lead author, Illinois physics doctoral candidate Trent Graham, comments, “We are constrained to sending a certain class of quantum states called ‘equimodular’ states. We can deterministically perform operations on this constrained set of states, which are impossible to perfectly perform with completely general quantum states. Deterministic describes a definite outcome, as opposed to one that is probabilistic. With existing technologies, previous photonic quantum teleportation schemes either cannot work every time or require extensive experimental resources. Our new scheme could work every time with simple measurements.” This research team is part of a broader collaboration that is working toward realizing quantum communication from a space platform, such as the International Space Station, to an optical telescope on Earth. The collaboration—Kwiat, Graham, Bernstein, physicist Jungsang Kim of Duke University in Durham, NC, and scientist Hamid Javadi of NASA’s Jet Propulsion Laboratory in Pasadena, CA—recently received funding from NASA Headquarter's Space Communication and Navigation program (with project directors Badri Younes and Barry Geldzahler) to explore the possibility. “It would be a stepping stone toward building a quantum communications network, a system of nodes on Earth and in space that would enable communication from any node to any other node,” Kwiat explains. “For this, we’re experimenting with different quantum state properties that would be less susceptible to air turbulence disruptions.” The team’s recent experimental findings are published in the May 28, 2015 issue of Nature Communications, and represent the collaborative effort Kwiat, Graham, and Bernstein, as well as physicist Tzu-Chieh Wei of State University of New York at Stony Brook, and mathematician Marius Junge of the University of Illinois. This research is funded by NSF Grant No. PHY-0903865, NASA NIAC Program, and NASA Grant No. NNX13AP35A. It is partially supported by National Science Foundation Grants DMS-1201886, No. PHY 1314748, and No. PHY 1333903. ______________________ Contact: Siv Schwink, communications coordinator, Department of Physics, 217/300-2201. Paul Kwiat, Department of Physics, University of Illinois at Urbana-Champaign. Image by Precision Graphics, copyright Paul Kwiat, University of Illinois at Urbana-Champaign. Source: http://engineering.illinois.edu/news/article/11151?


Particle accelerator technology could solve one of the most vexing problems in building quantum computers
Last year, researchers at Fermilab received over $3.5 million for projects that delve into the burgeoning field of quantum information science. Research funded by the grant runs the gamut, from building and modeling devices for possible use in the development of quantum computers to using ultracold atoms to look for dark matter.
For their quantum computer project, Fermilab particle physicist Adam Lyon and computer scientist Jim Kowalkowski are collaborating with researchers at Argonne National Laboratory, where they'll be running simulations on high-performance computers. Their work will help determine whether instruments called superconducting radio-frequency cavities, also used in particle accelerators, can solve one of the biggest problems facing the successful development of a quantum computer: the decoherence of qubits.
"Fermilab has pioneered making superconducting cavities that can accelerate particles to an extremely high degree in a short amount of space," said Lyon, one of the lead scientists on the project. "It turns out this is directly applicable to a qubit."
Researchers in the field have worked on developing successful quantum computing devices for the last several decades; so far, it's been difficult. This is primarily because quantum computers have to maintain very stable conditions to keep qubits in a quantum state called superposition.
Classical computers use a binary system of zeroes and ones—called bits—to store and analyze data. Eight bits combined make one byte of data, which can be strung together to encode even more information. (There are about 31.8 million bytes in the average three-minute digital song.) In contrast, quantum computers aren't constrained by a strict binary system. Rather, they operate on a system of qubits, each of which can take on a continuous range of states during computation. Just as an electron orbiting an atomic nucleus doesn't have a discrete location but rather occupies all positions in its orbit at once in an electron cloud, a qubit can be maintained in a superposition of both zero and one.
Since there are two possible states for any given qubit, a pair doubles the amount of information that can be manipulated: 22 = 4. Use four qubits, and that amount of information grows to 24 = 16. With this exponential increase, it would take only 300 entangled qubits to encode more information than there is matter in the universe.
Qubits don't represent data in the same way as bits. Because qubits in superposition are both zero and one at the same time, they can similarly represent all possible answers to a given problem simultaneously. This is called quantum parallelism, and it's one of the properties that makes quantum computers so much faster than classical systems.
The difference between classical computers and their quantum counterparts could be compared to a situation in which there is a book with some pages randomly printed in blue ink instead of black. The two computers are given the task of determining how many pages were printed in each color.
"A classical computer would go through every page," Lyon said. Each page would be marked, one at a time, as either being printed in black or in blue. "A quantum computer, instead of going through the pages sequentially, would go through them all at once."
Once the computation was complete, a classical computer would give you a definite, discrete answer. If the book had three pages printed in blue, that's the answer you'd get.
"But a quantum computer is inherently probabilistic," Kowalkowski said.
This means the data you get back isn't definite. In a book with 100 pages, the data from a quantum computer wouldn't be just three. It also could give you, for example, a one percent chance of having three blue pages or a one percent chance of 50 blue pages.
An obvious problem arises when trying to interpret this data. A quantum computer can perform incredibly fast calculations using parallel qubits, but it spits out only probabilities, which, of course, isn't very helpful—unless, that is, the right answer could somehow be given a higher probability.
Consider two water waves that approach each other. As they meet, they may constructively interfere, producing one wave with a higher crest. Or they may destructively interfere, canceling each other so that there's no longer any wave to speak of. Qubit states can also act as waves, exhibiting the same patterns of interference, a property researchers can exploit to identify the most likely answer to the problem they're given.
"If you can set up interference between the right answers and the wrong answers, you can increase the likelihood that the right answers pop up more than the wrong answers," Lyon said. "You're trying to find a quantum way to make the correct answers constructively interfere and the wrong answers destructively interfere."
When a calculation is run on a quantum computer, the same calculation is run multiple times, and the qubits are allowed to interfere with one another. The result is a distribution curve in which the correct answer is the most frequent response.
Listening for signals above the noise
In the last five years, researchers at universities, government facilities and large companies have made encouraging advancements toward the development of a useful quantum computer. Last year, Google announced that it had performed calculations on their quantum processor called Sycamore in a fraction of the time it would have taken the world's largest supercomputer to complete the same task.
Yet the quantum devices that we have today are still prototypes, akin to the first large vacuum tube computers of the 194zeroes.
"The machines we have now don't scale up much at all," Lyon said.
There's still a few hurdles researchers have to overcome before quantum computers become viable and competitive. One of the largest is finding a way to keep delicate qubit states isolated long enough for them to perform calculations.
If a stray photon—a particle of light—from outside the system were to interact with a qubit, its wave would interfere with the qubit's superposition, essentially turning the calculations into a jumbled mess—a process called decoherence. While the refrigerators do a moderately good job at keeping unwanted interactions to a minimum, they can do so only for a fraction of a second.
"Quantum systems like to be isolated," Lyon said, "and there's just no easy way to do that."
Which is where Lyon and Kowalkowski's simulation work comes in. If the qubits can't be kept cold enough to maintain an entangled superposition of states, perhaps the devices themselves can be constructed in a way that makes them less susceptible to noise.
It turns out that superconducting cavities made of niobium, normally used to propel particle beams in accelerators, could be the solution. These cavities need to be constructed very precisely and operate at very low temperatures to efficiently propagate the radio waves that accelerate particle beams. Researchers theorize that by placing quantum processors in these cavities, the qubits will be able to interact undisturbed for seconds rather than the current record of milliseconds, giving them enough time to perform complex calculations.
Qubits come in several different varieties. They can be created by trapping ions within a magnetic field or by using nitrogen atoms surrounded by the carbon lattice formed naturally in crystals. The research at Fermilab and Argonne will be focused on qubits made from photons.
Lyon and his team have taken on the job of simulating how well radio-frequency cavities are expected to perform. By carrying out their simulations on high-performance computers, known as HPCs, at Argonne National Laboratory, they can predict how long photon qubits can interact in this ultralow-noise environment and account for any unexpected interactions.
Researchers around the world have used open-source software for desktop computers to simulate different applications of quantum mechanics, providing developers with blueprints for how to incorporate the results into technology. The scope of these programs, however, is limited by the amount of memory available on personal computers. In order to simulate the exponential scaling of multiple qubits, researchers have to use HPCs.
"Going from one desktop to an HPC, you might be 10,000 times faster," said Matthew Otten, a fellow at Argonne National Laboratory and collaborator on the project.
Once the team has completed their simulations, the results will be used by Fermilab researchers to help improve and test the cavities for acting as computational devices.
"If we set up a simulation framework, we can ask very targeted questions on the best way to store quantum information and the best way to manipulate it," said Eric Holland, the deputy head of quantum technology at Fermilab. "We can use that to guide what we develop for quantum technologies."


Rachel Goldman’s lab is working to produce “designer alloys” with carefully tailored electrical and light-absorbing properties. These materials could one day be used to build solar cells with double the efficiency of the flat-panel silicon cells that dot rooftops today. The new cells, called concentrator photovoltaics, use gallium arsenide semiconductors instead of the silicon-based semiconductors used in today’s cells. Gallium arsenide could move us toward the utility-scale solar arrays we’ll need to make solar energy a large part of our electrical infrastructure.
In her most recent paper, Goldman and her collaborators moved forward the science by figuring out how incorporating small fractions of nitrogen and bismuth in gallium arsenide semiconductors affects their structure and light-absorbing properties, creating a new map for bandgap engineering of designer semiconductor alloys. The advance could accelerate the development of concentrator photovoltaics, and could also lead to advances in semiconductor lasers and quantum computing.
Goldman is a professor of materials science and engineering. We sat down with her recently to learn more about her work.
How is your “magic ratio” useful in solar cells?
Concentrator photovoltaics will depend on the development of alloys that are safer and less expensive than those currently used in gallium arsenide semiconductors. In our earlier research, we developed alloys that use a combination of nitrogen and bismuth. Since then, we’ve been working to develop a more complete understanding of exactly how the nitrogen-bismuth combination functions, and how changing the proportion of those two elements affects the alloy’s overall properties.
That research led us to the “magic ratio”—the precise proportion of bismuth to nitrogen that works best with a gallium arsenide substrate. We’ve found that by slightly tweaking that ratio within a certain range, we can control what bandwidth of light that the alloy absorbs.
What’s the main hurdle standing in the way of concentrator photovoltaics?
Turning “near-infrared” light into electricity is one big challenge—this is light that’s just outside the visible spectrum. A gallium arsenide solar cell consists of several thin layers of metal alloy sprayed onto a gallium arsenide substrate. It’s these thin layers that turn light into electrical charge. Each layer absorbs only a specific wavelength of light. A wavelength that slips through one layer can be caught by the next.
The “magic ratio” should help researchers dial in the exact mix of an alloy to absorb whatever bandwidth of light they choose.
How were you able to do what others couldn’t?
We had to start by acknowledging that the conventional way of thinking about alloy composition doesn’t work for bismuth-nitrogen alloys.
Making an alloy out of individual atoms is a little like filling a box with a mix of differently-sized marbles. If you know the sizes of the marbles and the size of the box, you can calculate the combination of marbles that will fill the box exactly. Researchers can calculate the composition of most alloys by using x-ray diffraction to measure the “box” and then calculating the combination of atoms that fits.
That doesn’t work with bismuth and nitrogen. Bismuth is very large and nitrogen is very small, so it’s more like mixing sand and marbles. It’s hard to measure the size of a single grain of sand and even harder to predict how it will flow around all those marbles.
So we worked with labs in New Mexico, Poland and Romania, as well as here at U-M, to develop a series of measurements that would each solve part of the puzzle. Then we brought them all together to precisely determine the ratio of nitrogen to bismuth in a wide range of sample alloys, and how that ratio affects light absorption properties.
Where else might these kinds of alloys be useful?
A better understanding of nitrogen-bismuth alloys could help us build more efficient infra-red lasers, which are widely used in fiber-optic communications and in the military. They could also be used in quantum computing, to build transistors that use the spin of electrons as a way to store information.
When will the results of this research go into widespread use?
There’s still a lot of progress to be made. But this research opens the door to a better understanding of exactly how these alloys work and how to make them do what we want, in solar power and elsewhere.
Goldman’s most recent paper is titled “Mapping the composition-dependence of the energy bandgap of GaAsNBi alloys.” It is published in the August 23, 2019 issue of Applied Physics Letters. U-M graduate researcher Jordan Occena, T. Jen and J.W. Mitchell are also authors on the paper.
An earlier, related paper is titled “Bi-enhanced N incorporation in GaAsNBi alloys.” It published in the June 15, 2017 issue of Applied Physics Letters.


The concept of quantum computing was proposed in the 1980s. By capturing the uncertainty of molecules at absolute zero (-273℃), scientists succeeded to create a computing apparatus that brings us to the next level. In 2017, such studies are about to bear fruit. D-Wave, the first company that aims for the commercial use of this technology, exhibited satisfying benchmark results that their quantum computer solved certain problems one million times faster than a conventional computer. In March, IBM declared the plan that they will bring a quantum computer, “IBM Q” system, to market in a few years. They note, “quantum computers will deliver solutions to important problems where patterns cannot be seen because the data doesn’t exist and the possibilities that you need to explore to get to the answer are too enormous to ever be processed by classical computers.” (IBM, 2017)
Conventional, or “classic” computers are based on transistors, in which each “bit,” the smallest unit of memory, has a binary state, a “0” or a “1.” Although computers have become significantly more powerful, smaller, and cheaper for the last several decades, they cannot flee from the fetters of the binary system. Quantum computers, in contrast, store information into a “qubit,” which can be represented by an atom in ambiguous states, say, a “0” or “1” or “0 and 1.” And, most importantly, those computers are able to calculate such different universes simultaneously, thus greatly compressing the computation time.
I enjoy Japanese chess —shogi— in my free time. It has more complex rules than Western chess does: in shogi, on a little larger nine-by-nine board, we can promote and strengthen a piece, capture an opponent’s piece, and use it under your control. Specifically, shogi has 10 to the 71st of possible states, whereas Western chess has 10 to the 47th. But what do these numbers mean in terms of computation?
I assume you have once played tic-tac-toe, which has 765 possibly different positions. If you have a computer that calculates the best move for each position respectively in one millisecond (a thousandth of a second), you can obtain the perfect answer immediately because 765 millisecond is shorter than a second. Chess and shogi, however, require an astronomical amount of time. In fact, it is more than astronomical. 1047 milliseconds is equivalent to 1036 (1000000000000000000000000000000000000) years. The earth is barely 4.5 billion years old. The whole universe is said to have the history of fewer than 1011 years. Our planets are babies compared to the time necessary to be omnipotent in the board game.
Like chess and shogi, a certain type of problem is theoretically able to be solved, but practically unsolvable because of time. Were you to purchase thousands of cutting-edge supercomputers, you would likely be able to remove some zeros in the number of “years for computing” but would never see the result while you are alive. Therefore, computer scientists have devoted their time to inventing algorithmic devices and estimating the result by employing statistical approaches. But what if a new technology shifts paradigms and changes the laws of the universe?
Quantum computing is, of course, not exclusive to board games. It will unravel the mystery of our DNA, helping invent more effective medicine. The technology will augment artificial intelligence, which would read our subtle nuances rather than “yes” or “no.” Quantum computers will also enable us to decipher any transactions on the Internet in a moment’s notice. All of the online encryption technology is underpinned by “practically” irreversible keys, so-named because of classic computing limitations. Once this assumption overturns, our privacy and a country’s cyber-security will be vulnerable. I am quite sure that this will become a fierce, controversial issue among politicians around the world.
Some take another view on this technology. In the Guardian’s article “Has the age of quantum computing arrived?” MIT professor Scott Aaronson, who has dubbed himself Chief D-Wave Sceptic,” says, “there was no reason to believe they played a casual role or that they were faster than a classical computer.” (Anthony, 2016) Quantum computing is still in the early stage of developing, and the company D-Wave has long been “accused of hype and exaggeration.” (Anthony, 2016)
Also, like the decryption issue, ethics matters. Quantum computing is so powerful that we should handle it properly, ethically, and openly. With no exception, technology is a double-edged sword. Is quantum computing a savior or a devil? Or something ambiguous between them like us, human beings? The answer is not a “0” or “1.”
Works CitedIBM. "IBM Unveils Roadmap for Commercial." IBM News Room - 2017-03-06 IBM Building First Universal Quantum Computers for Business and Science - United States. IBM, 06 Mar. 2017. Web. 07 Mar. 2017.
Anthony, Andrew. "Has the Age of Quantum Computing Arrived?" The Observer. Guardian News and Media, 22 May 2016. Web. 07 Mar. 2017.


Today’s date — 20 May 2019 — marks a major milestone in measurement history. For the first time, the definitions of the base units that comprise the International System of Units (SI) are entirely derived from constants of nature like the speed of light and Avogadro’s number instead of human-made artifacts. The kilogramme, the SI base unit that held out the longest, will now be defined in terms of the Planck constant rather than the platinum-iridium cylinder known as ‘Le Grand K’, or ‘The Big K’.
Physical constants, unlike physical objects, are inherently stable and do not experience minute fluctuations in their properties. As a result, the definitions of all seven SI base units — the second, the meter, the kilogramme, the ampere, the kelvin, the mole, and the candela — will remain accurate and unchanging for their countless applications in science, manufacturing, commerce, and other industries where near-perfect calibration is required.
The End of Big K
So how was Le Grand K, a fixture of scientific measurement for nearly 130 years, finally dethroned? The redefinition of the kilogramme only became possible with the groundbreaking discovery of the quantum Hall effect by German physicist Klaus von Klitzing in 1980. The scientific community immediately recognised the significance of his findings, and he went on to receive the Nobel Prize in Physics a mere five years later.
“At the time of the discovery of the effect, I never believed that this has some influence on the kilogramme,” said von Klitzing during his 2016 Lindau lecture, which focused on how his Nobel Prize-awarded work will contribute to the new and improved SI. The Nobel Laureate will also participate in the upcoming 2019 Lindau Meeting.
The quantum Hall effect is a quantum-mechanical version of the conventional Hall effect, first discovered by American physicist Edwin Hall in 1879. While completing his graduate studies at Johns Hopkins University, he noticed that when a magnetic field was applied perpendicularly to a thin metal sheet through which an electric current is flowing, a small voltage appeared from one side of the sheet to the other. The magnetic field exerts a force on the moving electric charges, and the accumulation of charge on one side of the conductor leaves the other side oppositely charged, leading to the observed potential difference.
Discovering the Quantum Hall Effect
Von Klitzing wanted to observe the Hall effect in more extreme conditions, at very low temperatures with a much stronger magnetic field. He performed experiments with two-dimensional electron systems, in which electrons are forced to move within an extremely thin layer, and smoothly varied the magnetic field. Surprisingly, he found that the observed Hall resistance — the ratio of the created voltage to the current — changed in discrete steps with exceptionally high accuracy.
In other words, the Hall resistance was exactly quantised. The resistance quantum, h/e2, where e is the electron charge and h is the Planck constant, is now known as the ‘von Klitzing constant’. Because of its extraordinarily high precision, the von Klitzing constant has been used in resistance calibrations worldwide since 1990.
In combination with the Josephson constant (KJ = 2e/h), which originates from another electrical phenomenon called the Josephson effect, the von Klitzing constant (RK = h/e2) can be used in experiments to connect mass to the Planck constant. In 1999, scientists Peter Mohr and Barry Taylor at the National Institute of Standards and Technology proposed the redefinition of the kilogramme with such a method, motivated by recent progress in the development of the Kibble balance. Also known as a ‘watt balance’, this device precisely measures mass through the use of electrical measurements
“These two constants were the origin of the change we expect in the future for our SI system,” said von Klitzing in 2016, before the revised definitions were formally accepted. “The Josephson effect and the quantum Hall effect are the driving force for the expected change in the SI system in 2018.”
The New and Improved SI
And as predicted, in November 2018, representatives from more than 60 countries voted to redefine the kilogramme in terms of the Planck constant during the 26th meeting of the General Conference on Weights and Measures in France. The new SI chosen to come into effect today, on World Metrology Day 2019, whose theme is “The International System of Units – Fundamentally better.” The date itself, 20th May, refers back to the signature of the Metre Convention in 1875 by representatives of 17 nations, which created the International Bureau of Weights and Measures (BIPM).
While most of us won’t notice a difference in everyday life, the new SI improves the precision of measurements for nanotechnology, communications, security, medicine, and emerging technologies such as quantum computing. In other words, the ‘fundamentally better’ SI might not impact you and me directly, but it will provide greater stability and accuracy to countless applications that have a significant effect on society.
Additional information: During a lecture at the upcoming 69th Lindau Nobel Laureate Meeting, Klaus von Klitzing will talk about the “Quantum Hall Effect and the New SI System”. Read an abstract of his lecture here.


Artificial intelligence (AI) is intelligence exhibited by machines. This is a cluster of topics in AI or related to AI.
Types of AI
Artificial Intelligence (AI) is classified into types based on the degree to which an AI system can replicate or go beyond human capabilities. One classification system uses four types: reactive machines, limited memory machines, theory of mind and self-aware AI. Another classification divides AI into two divisions: Weak AI or Narrow AI and Strong AI or General AI or Artificial General Intelligence. Different branches of AI are referred to by the method used to achieve AI.
- Weak or narrow AI or artificial narrow intelligence
- Artificial superintelligence (ASI)
- Reactive machines: These do not have past memory and cannot use past information for future actions
- Limited memory machines: These can use past experiences to inform future decisions
- Theory of Mind: In humans it is the ability to infer other people’s thoughts, desires and beliefs in others and to understand that they may be different from your own. Theory of mind level AI would be able to interact socially with people
- Self-aware AI (hypothetical, not realized)
Branches of AI
Machine learning is a technique for realizing AI and it is an application of AI where machines are given access to data from which they learn form themselves.
Machine learning tools
Tools, algorithms, libraries and interfaces for machine learning
Artificial neural network (ANN) processing devices can be algorithms or actual hardware that are loosely modeled after the neuronal structure of the mammalian cerebral cortex. Neural networks are used in the branch of machine learning called deep learning. The following are types of neural networks used in machine learning as well as topics associated with neural networks.
Deep learning frameworks
A Deep Learning Framework is an interface, library or a tool which allows users to build deep learning models more easily and quickly, without getting into the details of underlying algorithms. Libraries are useful for individuals who want to implement Deep Learning techniques but don’t have robust fluency in back-propagation, linear algebra or computer math. These libraries provide pre-written code for functions and modules that can be reused for deep learning training for different purposes.
- Deep Q-Learning : Algorithm in deep reinforcement learning
- Deep voice 1: Trains deep neural networks to learn from large amounts of data and simple features
Reinforcement learning is an area of machine learning focusing on how machines and software agents react in a specific context to maximize performance and achieve reward known as reinforcement signal. The following are algorithms, tools and research topics related to reinforcement learning.
Supervised learning is a type of machine learning in which data is fully labelled and algorithms learn to approximate a mapping function well enough that they can accurately predict output variables given new input data. This section contains supervised learning techniques. For example, Gradient Descent is a technique to optimize neural networks in supervised machine learning. Gradient descent optimization algorithms are used to speed up the learning process of deep neural networks. Another example, Support Vector Machine (SVM), is a type of algorithm that is a discriminative classifier formally defined by a separating hyperplane used for regression and classification tasks.
A decision tree is a simple representation for classifying samples. Decision tree algorithms are used in supervised machine learning where data is continuously split according to a parameter.
- Classification and regression trees (CART)
Unsupervised learning is a branch of machine learning that tries to make sense of data that has not been labeled, classified, or categorized by extracting features and patterns on its own. The following are methods used in unsupervised machine learning.
In unsupervised machine learning, clustering is the process of grouping similar entities together in order to find similarities in the data points and group similar data points together.
Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model. The purpose is to decrease variance (bagging), bias (boosting), or improve predictions (stacking).
In machine learning classification problems when there are too many factors or variables, also called features. When most of the features are correlated or redundant, dimensionality reduction algorithms are used to reduce the number of random variables. Certain features are selected and others are extracted.
Parameterized statistical models
Machine learning models are parameterized to tune their behavior for a given problem. Noise contrastive estimation (NCE) is an estimation principle for parameterized statistical models. NCE is a way of learning a data distribution by comparing it against a defined noise distribution. The technique is used to cast an unsupervised problem as a supervised logistic regression problem. NCE is often used to train neural language models in place of Maximum Likelihood Estimation.
- Noise-contrastive estimation
Computer vision is the ability of artificially intelligent systems to “see” like humans. In the computer vision field machines are developed that automate tasks that require visual cognition. Deep learning and artificial neural networks are used to develop computer vision. The following are topics related to computer vision as well as tools and libraries. Companies developing or selling computer vision products are under the Computer Vision subheading under the AI applications and companies section.
Natural language processing
Natural language processing is a branch of AI that helps computers understand, interpret and manipulate human language. The following are tools and topics related to NLP. NLP companies developing or selling NLP applications are found in the AI applications and companies section under Natural language processing.
Advances in deep learning are expected to increase understanding in quantum mechanics. It is thought that quantum computers will accelerate AI. Quantum computers have the potential to surpass conventional ones in machine learning tasks such as data pattern recognition. The following are topics, companies and technologies that link quantum computing and AI.
Semantic computing deals with the derivation, description, integration and use of semantics (meaning, context and intention) for resources including data, document, tool, device, process and people. Semantic computing includes analytics, semantics description languages, integration of data and services, interfaces and applications. In AI, semantic computing involves the creation of ontologies that are combined with machine learning to help computers create new knowledge. Semantic technology helps cognitive computing extract useful information from unstructured data in pattern recognition and natural-language processing.
- The IEEE Computer Society Technical Committee on Semantic Computing (TCSC)
IoT (Internet of Things)
The Internet of Things (IoT) refers to objects that connect and transfer data via the internet and the sharing of information between devices. IoT based smart systems generate a large volume of data including sensor data valuable to researchers in healthcare, bioinformatics, information sciences, policy and decision making, government and enterprises. AI can be combined with machine learning for analysis of data and prediction.
Artificial life and evolutionary computation
While some lines of AI research aim to simulate the human brain. Artificial life or animate approach is concerned with the conception and construction of artificial animals as simulations or actual robots. It aims to explain how certain faculties of the human brain might be inherited from the simplest adaptive abilities of animals. Evolutionary computation is a generic optimization technique that draws inspiration from the theory of evolution by natural selection.
AI applications and companies
The following are companies using AI to develop products or producing AI software for various applications. AI programs designed for a specific applications are also listed.
Medical, veterinary and pharmaceutical
- Enzbond - prediction programs for enzyme development
Computer vision, image recognition and generation
Computer vision has applications in healthcare, security, manufacturing and transportation.
Natural language processing
Art and music creation
Industry/Factory automation and monitoring
Employee Behavior Analytics
Social Media/Human Interaction/Recruitment
- Viv Labs
AI software and API development
Other AI companies
Documentaries, videos and podcasts
Digital education platform
San Antonio, US
Barry N. Perkins
San Francisco, California, US
Redwood City, California, US
Las Vegas, US
Direct Aviated Response (DAR) System
Automated digital advertising
Destin AI Chatbot
Digital risk analysis
Los Angeles, US
Customized AI conversation bots
Automated job candidate search
San Francisco, US
Automated, customizable chatbots
San Francisco, California, US
Mountain View, US
App for fashion recommendations
San Francisco, US
AI-enhanced acne treatment


Harvard scientists have taken a critical step toward building a quantum computer — a device that could someday harness, for example, the intrinsic properties of subatomic particles such as electrons to perform calculations far faster than the most powerful supercomputers.
As described in a paper published April 13 in Science, researchers have, for the first time, demonstrated a system in which two semiconducting spin quantum bits, or qubits, interact with each other in a process known as entanglement. Without that entanglement, quantum computers simply can’t exist.
“Entanglement is an essential component of quantum computing — it’s what gives you the ability to do generalized, universal quantum computation,” said Amir Yacoby, professor of physics and of applied physics, who led the research. “Without this kind of entanglement, there’s no way to get anywhere in this field.”
Quantum computers rely on quantum mechanical properties of particles to store data and perform computations. Unlike the transistors used in digital computers, which encode data “bits” as either zero or one, qubits can hold both values simultaneously. In theory, that inherently parallel nature allows quantum computers to be vastly more powerful than traditional computers, which perform operations in sequence.
As a first step toward making those parallel computations possible, researchers working in Yacoby’s lab have established a new method for creating an entangled state between two qubits. By taking advantage of the electrostatic interaction between the particles, Yacoby, in collaboration with postdoctoral researchers Oliver Dial and Hendrik Bluhm, and graduate students Michael Shulman and Shannon Harvey, was able to create pairs of qubits in a state that has no classical analog, known as an entangled state.
By entangling one qubit with another, researchers can control the state of one qubit by operating on the other. This interconnectedness gives quantum computers their advantage over their classical counterparts.
“There are two elements to this paper,” Yacoby explained. “The first is determining how to create these entangled states. We took advantage of the fact that our qubits are made of electrons, so we used their electrostatic interaction to create this conditional, or entangled, state between them. As a method for creating entanglement, that has not been demonstrated before.
“The second element in the paper is that the electrostatic interaction is weak, so it takes time to create that entanglement,” Yacoby continued. “But during that time, various elements are trying to interact with the individual qubits, which cause them to lose their information. It took some creative thinking to design a system that would allow their entanglement to accumulate, but would limit their interaction with the rest of their environment.”
As Shulman put it, “The trick is to keep them sensitive to each other, and to nothing else.”
The solution, Yacoby said, came in the form of an echo. Though subtle fluctuations in the evolution of each qubit cause the entangled qubits to get out of sync, researchers found a novel way to solve the problem. They allowed the qubits to interact for a precise amount of time, then flipped each qubit, causing them to return to their initial state.
The method has two benefits, Yacoby said. First, allowing a pair of qubits to interact builds the necessary entanglement between them. Second, bringing the bits back to their initial state preserves the data that had been coded into them.
“You can think of it like runners on parallel tracks,” Yacoby said. “They run a certain distance, and then on cue they turn and run back the same amount, so they wind up back where they began.”
Similar to traditional computers, Yacoby’s design for a quantum computer begins with a thin wafer of semiconducting material — in this case gallium arsenide — “grown” in the Weizmann Institute. Researchers then deposit nanometer-size wires onto the wafer to form metal “gates.” The entire device is then supercooled to a few hundredths of a degree above absolute zero to slow the motion of atoms in the wafer. When attached to an electric voltage, the gates trap electrons, allowing researchers to construct their quantum bits.
“Conceptually, people had laid out the idea that this type of entanglement was possible as early as 15 years ago, but the gap between being able to conceive of something and demonstrating it in a real system is huge,” Yacoby said. “It’s huge in the sense that, when people were laying out these concepts, they didn’t take into account all the problems that exist in a real system. Just because nature doesn’t fundamentally forbid it, doesn’t mean it can be done. But the fact is it can be done, it can be done today, and it can be done quite elegantly.”


Machine learning is the newest thing at BYU, thanks to the work of engineer Dah-Jye Lee, who has created an algorithm that allows computers to learn without human help. According to Lee, his algorithm differs from others in that it doesn’t specify for the computer what it should or shouldn’t look for. Instead, his program simply feeds images to the computer, letting it decide on its own what is what.
Similar to how children learn differences between objects in the world around them in an intuitive way, Lee uses object recognition to show the computer various images but doesn’t differentiate between them. Instead, the computer is tasked with doing this on its own. According to Lee:
“It’s very comparable to other object recognition algorithms for accuracy, but, we don’t need humans to be involved. You don’t have to reinvent the wheel each time. You just run it.”
Of course, computers can’t think, reason, or rationalize in quite the same way as humans, but researchers at Carnegie Mellon University are using Computer Vision and Machine Learning as ways of optimizing the capabilities of computers.
NEIL’s task isn’t so much to deal with hard data, like numbers, which is what computers have been doing since they first were created. Instead, NEIL goes a step further, translating the visual world into useful information by way of identifying colors and lighting, classifying materials, recognizing distinct objects, and more. This information then is used to make general observations, associations, and connections, much like the human mind does at an early age.
While computers aren’t capable of processing this information with an emotional response–a critical component that separates them from humans–there are countless tasks that NEIL can accomplish today or in the near future that will help transform the way we live. Think about it: how might Computer Vision and Machine Learning change the way you live, work, and interact with your environment?
While your smart device of today may appear to be multi-tasking with GPS, text messaging and music streaming all running at once, in reality, it’s cycling between these tasks, serially.
Computers have been operating this way since the computer age began.
Quantum computers, on the other hand, would address simultaneity from the ground up. They would perform many operations in parallel and be well-suited to machine learning where there’s a need to search instantly through a myriad of possibilities and choose the best solution.
One of the more controversial aspects of quantum computing’s massive potential is to render today’s data encryption technologies, obsolete.
(For a surprisingly easy-to-follow explanation of the difference between classical computing versus quantum computing, see this 1999 article by Lov K. Grover, inventor of what may be the fastest possible search algorithm that could run on a quantum computer.)
One focus of the lab will be to advance machine learning. Google Director of Engineering, Hartmut Neven blogs:
Machine learning is all about building better models of the world to make more accurate predictions.
And if we want to build a more useful search engine, we need to better understand spoken questions and what’s on the web so you get the best answer.
In venture capital circles, machine learning startups are about to catch fire. This makes sense as the size of data sets that companies and organizations need to utilize spirals beyond what the human brain can fathom.
As Derrick Harris at Gigaom reports, Skytree landed $18 million in Series A funding from US Venture Partners, United Parcel Service and Scott McNealy, the Sun Microsystems co-founder and former CEO. The company began just over a year earlier with $1.5 million in seed funding.
As big data gets bigger ever more quickly, machine learning makes it possible to identify meaningful patterns in real time that would elude sharp humans even with the best of query tools.
Still, there’s often a place for human judgment to flesh out the findings of machine learning algorithms.
The flagship Skytree product, Skytree Server, lets users run advanced machine learning algorithms against their own data sources at speeds much faster than current alternatives. The company claims such rapid and complete processing of large datasets yields extraordinary boosts in accuracy.
Skytree’s new beta product, Adviser, allows novice users to perform machine learning analysis of their data on a laptop and receive guidance about methods and findings.
As the machine learning space becomes more accessible to a wider audience, expect to see more startups get venture funding.
Writing for Mason Research at George Mason University, Michele McDonald reports on how machine learning is helping doctors determine the best course of treatment for their patients. What’s more, machine learning is improving efficiency in medical billing and even predicting patients’ future medical conditions.
Wojtusiak points out how current research and studies focus on the average patient whereas those being treated want personalized care at the lowest risk for the best outcome.
Machine learning can identify patterns in reams of data and place the patient’s conditions and symptoms in context to build an individualized treatment model.
As such, machine learning seeks to support the physician based on the history of the condition as well as the history of the patient.
The data to be mined is vast and detailed. It includes the lab tests, diagnoses, treatments, and qualitative notes of individual patients who, taken together, form large populations.
Machine learning uses algorithms that recognize the data, identify patterns in it and derive meaningful analyses.
For example, researchers at the Machine Learning and Inference Lab are comparing five different treatment options for patients with prostate cancer.
To determine the best treatment option, machine learning must first categorize prostate cancer patients on the basis of certain commonalities. When a new patient comes in, algorithms can figure out which group he is most similar to. In turn, this guides the direction of treatment for that patient.
Given the high stakes consequences involved with patient care, the complexity that must be sorted out when making diagnoses and the ongoing monitoring of interventions against outcomes, machine learning development in health care is risk-mitigating and cost-effective.
For more about The Machine Learning and Inference Lab and the health care pilot projects they are working on, see the original article here.
As the new frontier in computing. machine learning brings us software that can make sense of big data, act on its findings and draw insights from ambiguous information.
Spam filters, recommendation systems and driver assistance technology are some of today’s more mainstream uses of machine learning.
Like life on any frontier, creating new machine learning applications, even with the most talented of teams, can be difficult and slow for a lack of tools and infrastructure.
DARPA (The Defense Advanced Research Projects Agency) is tackling this problem head on by launching the Probabilistic Programming for Advanced Machine Learning Program (PPAML).
Probabilistic programming is a programming paradigm for dealing with uncertain information.
In much the same way that high level programming languages spared developers the need to deal with machine level issues, DARPA’s focus on probabilistic programming sets the stage for a quantum leap forward in machine learning.
More specifically, machine learning developers using new programming languages geared for probabilistic inference will be freed up to deliver applications faster that are more innovative, effective and efficient while relying less on big data, as is common today.
For details, see the DARPA Special Notice document describing the specific capabilities sought at http://go.usa.gov/2PhW.


Quantum supremacy is the experimental demonstration of a quantum computer's dominance and advantage over classical computers by performing calculations that were previously impossible at unmatched speeds. To confirm that quantum supremacy has been achieved, computer scientists must be able to show that a classical computer could never have solved the problem while also proving that the quantum computer can perform the calculation quickly.
Computer scientists hope that quantum supremacy will lead to the cracking of Shor's algorithm -- a currently impossible calculation that is the basis of most modern cryptography -- as well as advantages in drug development, weather forecasts, stock trades and material designs.
Quantum computing is consistently evolving; quantum computers have not yet reached a point where they can show their supremacy over classical computers. This is mostly due to the huge amount of quantum bits, or qubits, that are required to perform meaningful operations on quantum computers. As the amount of necessary gates and number of qubits increases, so does the error rate, and if the error rate gets too high, the quantum computer loses any advantage it had over the classical computer.
To successfully perform useful calculations -- such as determining the chemical properties of a substance -- a few million qubits would be necessary. Currently, the largest quantum computer design is Google's Bristlecone, with a 72-qubit quantum processor, which was released in March 2018.
Quantum computers vs. classical computers
The primary difference between quantum and classical computers is in the way they work. Classical computers process information as bits, with all computations performed in a binary language of 1s and 0s. The current in classical computers is either flowing through the transistor or not; there is no in between.
Conversely, quantum computers use quantum theory as the basis of their systems. Quantum theory focuses on the extraordinary interactions between particles on an invisible scale -- such as atoms, electrons and photons. Therefore, the binary states used in classical computers can no longer be applied to quantum computers.
Qubits can theoretically outperform the computation scale of binary bits by magnitudes. This is mostly due to quantum superposition -- or the ability for a subatomic particle to exist in two states at once. Superposition allows qubits to run specific computations on various possibilities simultaneously.
Trapped ions, photons and superconductors give quantum computers the ability to perform calculations at exceptionally fast speeds and take in massive amounts of data. However, the real value that quantum computers could provide is the ability to solve problems that are too complex for classical computers to address or that would take classical computers billions of years to answer. Quantum computers should be able to create a series of samples from a random quantum circuit that follow a specific, correct distribution.
While these advantages could lead to quantum supremacy, processors have not yet been built with all the capabilities. Classical computers continue to surprise computer scientists with computational power and their ability to solve certain types of problems. Until a quantum computer is built that solves a problem it has been proven a classical computer cannot solve, it continues to be possible that a better classical algorithm exists and quantum supremacy will not be achieved.
Applications of quantum supremacy
Some people believe a quantum computer that achieves quantum supremacy could be the most disruptive new technology since the Intel 4004 microprocessor was invented in 1971. Certain professions and areas of business will be significantly impacted by quantum supremacy. Examples include:
- The ability to perform more complex simulations on a larger scale will provide companies with improved efficiency, deeper insight and better forecasting, thus improving optimization processes.
- Enhanced simulations that model complex quantum systems, such as biological molecules, would be possible.
- Combining quantum computing with artificial intelligence (AI) could make AI immensely smarter than it is now.
- New customized drugs, chemicals and materials can be designed, modeled and modified to help cultivate new pharmaceutical, commercial or business products.
- The ability to factor extremely large numbers could break current, long-standing forms of encryption.
Overall, quantum supremacy could start a new market for devices that have the potential to boost AI, intricately model molecular interactions and financial systems, improve weather forecasts and crack previously impossible codes.
While most of these applications appear to provide nothing but benefits, quantum supremacy also has the ability to destabilize the math that underlies most current data encryption. Therefore, once quantum supremacy is achieved, computer scientists will have to completely reevaluate computer security and how to protect information and data. Unfortunately, this will become extremely difficult with the high speeds and large amounts of data that the quantum computers will be working with.
Examples of quantum supremacy
While the problem that first exemplifies quantum supremacy could be whatever computer scientists want, it is expected that they will use a problem known as random circuit sampling.
This problem requires a computer to correctly sample from the possible outputs of a random quantum circuit -- similar to a series of actions that can be performed on a set of qubits. Classical computers do not possess any fast algorithms to generate these samples; therefore, as the array of possible samples increases, classical computers become overwhelmed. If a quantum computer can efficiently pull samples in this instance, then it will prove quantum supremacy.
Importance of quantum supremacy
The first quantum algorithms were solved in the 1990s and, while the problems themselves were useless, the process provided the computer scientists who designed them with knowledge and insights they could use to develop more meaningful algorithms -- like Shor's algorithm -- which could potentially have large practical consequences.
Computer scientists hope that quantum supremacy will repeat this process and drive inventors to create a quantum computer that is capable of outperforming a classical computer -- even if it only solves a simple, useless problem -- because this work could be the key to building a beneficial and supreme quantum computer.
Some people also believe Moore's Law is ending soon. This would inhibit AI research because the necessary smarter applications, such as fully autonomous cars, require huge amounts of processing power. Once quantum supremacy is reached, then quantum computing should be able to resolve this problem as well as revolutionize machine learning (ML).
Finally, quantum supremacy would greatly affect the field of theoretical computer science. For decades, scientists in this field have believed in the extended Church-Turing thesis, which states that classical computers can efficiently complete any problem that any other type of computer can accomplish. Quantum supremacy totally violates that assumption. Scientists would be forced to open their minds to a whole new world of computer science.
The future of quantum supremacy
The final goal for quantum computing is to create a fully functional, universal fault-tolerant gate computer. However, before this machine can be built, computer scientists need to develop:
- Refined error correction that doesn't require huge amounts of hardware
- Advanced algorithms that can support the uniquely complex problems
- Enhanced noise
- Qubits with less noise sensitivity, longer coherence times and increased reliability
- Quantum processors that possess thousands of qubits
The U.S. and China have been the most focused on investing in quantum projects along with organizations and businesses such as Google, Microsoft, IBM, Lockheed Martin and Alibaba. Google has developed a 72-qubit quantum processor -- called Bristlecone -- which they claim will achieve quantum supremacy by the end of 2019.
Once quantum supremacy is displayed, quantum computers will provide superior use for crunching large data sets, such as those used in cancer research, drug design, genetic engineering particle physics and weather forecasting. Unfortunately, due to superposition, programmers working on developing tools to code quantum computers are unable to view the paths that their data takes from input to output, making the debugging process highly complicated.
Furthermore, while quantum supremacy can be extremely beneficial to various industries, the breakthrough could also lead to rogue states or actors using quantum computers for destructive purposes.


Building a better supercomputer is something many tech companies, research outfits, and government agencies have been trying to do over the decades. There’s one physical constraint they’ve been unable to avoid, though: conducting electricity for supercomputing is expensive.
Not in an economic sense—although, yes, in an economic sense, too—but in terms of energy. The more electricity you conduct, the more resistance you create (electricians and physics majors, forgive me), which means more wasted energy in the form of heat and vibration. And you can’t let things get too hot, so you have to expend more energy to cool down your circuits.
Any gamer or regular laptop user is familiar with overheating problems. Supercomputing deals with the same issues on an exponential scale, with energy use similarly enlarged and thus a significant cost concern (there’s the economic bit). That’s why consumer supercomputers that try to control these issues will run you at least $6,000, and why supercomputing as a whole has been reserved for code-cracking spy agencies, really Big Data crunching at various companies and governments, and mega-funded research institutions.
A new nanowire breakthrough could be the first in a wave of similar innovations set to change all that.
Superconductors Gone Miniature
One of the holy grails in supercomputing development—and in electronics/physics at large—has been developing a resource- and cost-effective superconductive material. Unlike regular electric conductors, superconductors transmit electrons (i.e. “electricity”) without any resistance. They output exactly the same amount of energy that was inputted and do not dissipate “waste” energy in terms of heat or sound. This means they require much less energy to run and no energy to cool…sort of.
While superconductors don’t generate any heat while conducting electricity, all of the superconductive materials yet discovered and developed have to be cooled below a critical temperature to gain their superconductive powers. This has made them more expensive and unwieldy in most applications, including supercomputers, than using more traditional materials and cooling systems. But researchers at the University of Illinois at Urbana-Champaign have just created a working superconductive nanowire memory cell that could prove to be a game changer.
“An SEM image of Device 7715s1. Two carbon nanotube templated Mo75Ge25 wires lay across a roughly 150 nm wide trench, 2.5 μm apart. The two wires have similar dimensions, but are not identical. (b) An SEM image of Device 82915s2. The Mo75Ge25 (dark) is patterned into two geometrically different nanowires sitting 150 nm apart. The right wire has a non-uniform width.”
Get all that? Good.
The cell consists of a nanowire loop and a couple of electrodes and is programmed simply by subjecting it to an initial positive or negative charge, which sends electrons around the loop either clockwise or counterclockwise. This binary option equates to the 0s and 1s needed for computing, and the cell’s memory is preserved with no additional energy applied—that is, the electrons keep moving as long as the wires stay superconductive.
Keeping nanowires at the cool conditions needed for superconducting—especially when they don’t generate any heat of their own—could prove far more affordable and far less space-intensive than current supercomputing solutions demand. And when things get smaller and cheaper, they open up whole new markets and usher in a wave of further innovations.
Putting a superconducting supercomputer onboard a self-driving car could become a reality, making them vastly more responsive, reliable and more affordable, both to build and to operate. Space-bound supercomputers could proliferate, with all kinds of academic and commercial applications. We’re not likely to see even a nano-sized cooling system in our smartphones anytime soon, but desktop supercomputers could be markedly reduced in upfront and long-term costs.
Quantum computing, which uses relatively low energy to change the quantum states of electronics to preserve memory, is undoubtedly going to hit the market first, and we’ll see some major leaps in computing power, size, and efficiency. Nanotech is keeping supercomputers on the map for a variety of applications, though, and we’re eager to see just how small things can get.
Daniel A. Guttenberg is an Atlanta-based writer who fell into the startup world by accident and has been gleefully treading water ever since. He will be survived by his beard and his legacy of procrastination.
Latest posts by Daniel A. Guttenberg (see all)
- The Startup Rushing to Usher in the Self-Driving Era Even Faster - July 7, 2017
- Who Are the AR Leaders…And Who’s Just Hype? - June 30, 2017
- As US Stock Worries Loom, Canada’s Startup Scene Booms - June 23, 2017


Quantum computers have the potential to fundamentally change digital technology and therefore the ways in which we solve problems, interact and do business. In her series for Roman Road Journal, science writer Gemma Milne looks into how quantum computers work and how they might change our lives. In Part 1, she explores the why, what and when behind quantum computing and some of the potential applications for what could be the most significant technological development of the 21st century.
In a world where we can send instant messages to anyone on the planet at the touch of a button; where there are warehouses full of robots which build millions of planes, trains and automobiles every year; and where ideas, discussions and revolutions spread digitally within seconds…you’d think we’d have reached the limit of our computational ability.
But what if I told you that, in fact, an entirely different kind of computer is on the horizon? Not simply a faster, better computer, but one which can solve some of the world’s most intricate and complicated problems like nothing we’ve seen before.
I’m talking about a quantum computer.
Let me give you an example of the limitations of our standard computers. We use 4% of all the world’s energy making fertiliser, using a process created in the early 1900s called the Haber process. Essentially, you take nitrogen from the air and hydrogen from natural gas, and turn them into ammonia. In order to break the bonds between the nitrogen atoms to create the new substance, we use an iron catalyst, heated up to about 450°C and kept at an extremely high pressure. The energy required to create the ammonia – the key ingredient needed for fertilisation of crops – is therefore huge. When you think about how much food is needed by the entire world, the growth of which is powered by fertiliser, it’s easy to see why so much energy is eaten up creating ammonia.
We know that there’s a better, more energy-efficient way, as microbes in soil manage to create ammonia from the nitrogen in the air with only tiny amounts of sunlight, in a process called Biological Nitrogen Fixation. It’s the exact same equation – nitrogen plus hydrogen to create ammonia – but instead of using a chamber with high pressure and temperature to break the bonds, bacteria living in soil and plant roots ‘fix’ the nitrogen naturally. We don’t yet understand how they do it – all we know is that this bacteria is capable of breaking those bonds and opening the nitrogen up to turn into ammonia.
We can’t simply grab all the soil microbes and task them with creating fertiliser for us – it wouldn’t create enough and they’ve got their own work to do – but if we could understand how they do it and copy their genius, our man-made process could be revolutionised. But to do this, to create a man-made scalable method that we could implement in fertiliser factories all over the world, it requires simulating the chemistry of life at such a small scale, with so many different variables, that our conventional computers simply cannot handle the numbers.
Discovering a new drug, and bringing it to the people who need it most, on average takes ten years and costs $2.6billion. Part of the reason it takes so long and costs so much, is linked to the arduous process of finding the right molecules to match together, to create an effective drug. It’s a case of trial and error, testing so many different combinations, that the problem is of a similar scale as with Biological Nitrogen Fixation.
At the moment, pharmaceutical companies will run billions of comparisons on their super-fast versions of conventional computers, but they are still severely limited to using only the small molecules which conventional computers can handle. Again, understanding the complex chemistry of life proves to be too difficult and full of way too many to-do lists to be sped up with conventional computers.
Quantum computers could solve these kinds of problems for breakfast.
Now, there are two concepts which are worth getting to grips with enough to understand quantum computing: superposition, and the uncertainty principle. Superposition basically means that all particles – everything that you, me and everything that surrounds us is made up of at the tiniest of levels – can be in more than one place at one time.
Sounds mental, yes, but the idea is that instead of our particles being tied to one physical space, there’s a variety of places they can be, each with a different probability. As opposed to saying ‘the particle could be here or could be there’, we describe them as being in all those multiple places at the same time. The uncertainty principle states that we can never know both the position and the momentum of a particle at any one point – if we decide to measure one, we lose the other. In other words, if we want to locate the particle, we cannot know what speed it is going at, and if we want to know the speed, then we cannot locate it – we can only know one at a time. Taking both of these concepts together, means that particles can be in many places at once, but only when we leave them to it, and don’t actually try to work out where exactly they are.
It’s worth mentioning that Niels Bohr, the Danish Nobel Prize winning physicist, famously said: “Anyone not shocked by quantum mechanics has not yet understood it.” Even the most accomplished scientists cannot get their head around quantum physics, but it’s exactly this kind of science which sits at the heart of what will become our solution to the world’s hardest problems.
So we know that in the quantum world, particles can be in more than one place at one time. In the same way that multiple versions of yourself would result in a much more productive person, multiple particles in multiple places inside a quantum computer results in multiple calculations being able to be done at the same time.
Conventional computers can do long arduous calculations, but they essentially have to work through a big long line of 0s and 1s (like a to-do list) to get to the answer. A quantum computer can do all the tasks on the long to-do list all at once, meaning they can work on very complicated tasks – like simulating the chemistry of life.
So how long until we can feed the world using less energy and get drugs to the people who need them most, faster? In short: we still have a way to go. We haven’t yet got to the point at which a quantum computer has been proven to be better than a conventional computer – known as ‘quantum supremacy’. Google, Microsoft, IBM and many universities and startups are working towards a working, useful, reliable quantum computer, but so far their efforts are still at an early stage.
That’s not a reason to lose hope, however. Progress has been accelerating over the last 10 years, particularly with the interest of corporates matching that of academia, and it’s been estimated that quantum supremacy could be reached as soon as this year.
As with all hyped-up tech trends though, it’s worth bearing in mind that real world applications are always far behind the first working model. A whole new way of writing algorithms for these computers has to be worked out, and translating real world problems which abide by the laws of classical physics into problems which make sense in the quantum world is a hefty task in itself.
Quantum computing is coming, and with it come exciting prospects for solving complicated issues lying at the heart of huge social issues around the world. It’s not all rosy though: with any new technology, changes can disrupt the status quo in negative ways too. For our world to work, we need problems which cannot be solved – this idea is at the root of encryption, for example. Hype around new technologies can also prompt poor investment decisions and early hustlers out to make a quick buck off a buzzword.
In the same way that particles can be more than one thing at one time, quantum computers can be thought of both as a blessing and a curse…
This article continues in Part 2 here


Researchers Discover New Way To Split And Sum Photons With Silicon
A team of researchers at The University of Texas at Austin and the University of California, Riverside have found a way to produce a long-hypothesized phenomenon—the transfer of energy between silicon and organic, carbon-based molecules—in a breakthrough that has implications for information storage in quantum computing, solar energy conversion and medical imaging. The research is described in a paper out today in the journal Nature Chemistry.
Silicon is one of the planet’s most abundant materials and a critical component in everything from the semiconductors that power our computers to the cells used in nearly all solar energy panels. For all of its abilities, however, silicon has some problems when it comes to converting light into electricity. Different colors of light are comprised of photons, particles that carry light’s energy. Silicon can efficiently convert red photons into electricity, but with blue photons, which carry twice the energy of red photons, silicon loses most of the energy as heat.
The new discovery provides scientists with a way to boost silicon’s efficiency by pairing it with a carbon-based material that converts blue photons into pairs of red photons that can be more efficiently used by silicon. This hybrid material can also be tweaked to operate in reverse, taking in red light and converting it into blue light, which has implications for medical treatments and quantum computing.
“The organic molecule we’ve paired silicon with is a type of carbon ash called anthracene. It’s basically soot,” said Sean Roberts, a UT Austin assistant professor of chemistry. The paper describes a method for chemically connecting silicon to anthracene, creating a molecular power line that allows energy to transfer between the silicon and ash-like substance. “We now can finely tune this material to react to different wavelengths of light. Imagine, for quantum computing, being able to tweak and optimize a material to turn one blue photon into two red photons or two red photons into one blue. It’s perfect for information storage.”
For four decades, scientists have hypothesized that pairing silicon with a type of organic material that better absorbs blue and green light efficiently could be the key to improving silicon’s ability to convert light into electricity. But simply layering the two materials never brought about the anticipated “spin–triplet exciton transfer,” a particular type of energy transfer from the carbon-based material to silicon, needed to realize this goal. Roberts and materials scientists at UC Riverside describe how they broke through the impasse with tiny chemical wires that connect silicon nanocrystals to anthracene, producing the predicted energy transfer between them for the first-time.
“The challenge has been getting pairs of excited electrons out of these organic materials and into silicon. It can’t be done just by depositing one on top of the other,” Roberts said. “It takes building a new type of chemical interface between the silicon and this material to allow them to electronically communicate.”
Roberts and his graduate student Emily Raulerson measured the effect in a specially designed molecule that attaches to a silicon nanocrystal, the innovation of collaborators Ming Lee Tang, Lorenzo Mangolini and Pan Xia of UC Riverside. Using an ultrafast laser, Roberts and Raulerson found that the new molecular wire between the two materials was not only fast, resilient and efficient, it could effectively transfer about 90% of the energy from the nanocrystal to the molecule.
“We can use this chemistry to create materials that absorb and emit any color of light,” said Raulerson, who says that, with further fine tuning, similar silicon nanocrystals tethered to a molecule could generate a variety of applications, from battery-less night-vision goggles to new miniature electronics.
Other highly efficient processes of this sort, called photon up-conversion, previously relied on toxic materials. As the new approach uses exclusively nontoxic materials, it opens the door for applications in human medicine, bioimaging and environmentally sustainable technologies, something that Roberts and fellow UT Austin chemist Michael Rose are working towards.
At UC Riverside, Tang’s lab pioneered how to attach the organic molecules to the silicon nanoparticles, and Mangolini’s group engineered the silicon nanocrystals.
“The novelty is really how to get the two parts of this structure—the organic molecules and the quantum confined silicon nanocrystals—to work together,” said Mangolini, an associate professor of mechanical engineering. “We are the first group to really put the two together.”
The paper’s other authors include Devin Coleman and Carter Gerke of UC Riverside.
Funding for the research was provided by the National Science Foundation, the Robert A. Welch Foundation, the Research Corporation for Science Advancement, the Air Force Office of Scientific Research and the Department of Energy. Additionally, Raulerson holds the Leon O. Morgan Graduate Fellowship at UT Austin.
Source: The University of Texas at Austin


Teleportation of electricity
This article summarizes some recent work from our group. For another perspective on the same research, see PhysRev Focus (6 February 2004).
Teleportation is the transfer of a quantum mechanical state between two particles that can only communicate by classical means. Because the transfer takes place without exchange of matter, it is reminiscent of the well known Beam me up! from the StarTrek television series. Teleportation of isolated particles was invented ten years ago  and demonstrated for photons in free space . We have found a way to teleport electrical charge in the solid state . This discovery could be used to transfer quantum mechanical bits (qubits) in a quantum computer.
Electron meets hole
There exist two types of charge carriers in the solid state, electrons and holes. Because they are oppositely charged, they can only exist simultaneously if they are separated from each other by an insulating barrier. If the barrier still passes a small current and an electron meets a hole, then both are annihilated. We have discovered that this need not be the end of the story. Under special circumstances the electron can continue its existence at a distant location by teleportation.
The meeting of an electron and a hole is illustrated is figure 1. Both particles live in the conduction band of a metal or semiconductor. At low temperature all energy levels are filled with electrons up to a maximal energy. This "sea" of electrons is called the Fermi sea and the maximal energy level is the Fermi level. The fully filled Fermi sea is in equilibrium and therefore carries no electrical current. To pass a current you need excitations. These are filled states (electrons) above the Fermi level or empty states (holes) below it. The meeting takes place at an insulating barrier, which plays the role of a sluice: The Fermi level is a little higher on one side of the barrier than on the other, so that the hole is elevated to the same energy as the electron.
Figure 1: An electron meets a hole.
Usually the electron and the hole are reflected by the barrier, but each time they meet there is a small probability that the electron will tunnel through the barrier and fall into the hole at the other side. Then both particles disappear without leaving a trace. End of story. Unless ... the hole had been entangled in the past with another electron, at some distant location in the material. I will refer to this second, distant electron as the "heavenly" electron and to the first electron as the "earthly" electron.
Entangled electron-hole pair
Entanglement is a quantum mechanical correlation between the spins. The spin of the hole is, on the one hand, oriented completely isotropically and, on the other hand, fully correlated with the spin of the heavenly electron. In classical mechanics this would be impossible, but not so in quantum mechanics. The wave function that describes such an entangled state of hole and electron is . It is a superposition of two states; In the first state electron and hole have both spin and in the second state they have both spin . Such an entangled state is created naturally when the heavenly electron tunnels through a barrier and leaves behind a hole with the same spin . See figure 2.
Figure 2: Creation of an entangled electron-hole pair. Both spins are isotropically distributed but perfectly correlated. The hole continues its path and will eventually be annihilated by the electron from figure 1. Because of the entanglement of the spins, the remaining electron takes on the state of the annihilated electron.
Teleportation by electron-hole annihilation
Back to the earthly electron. It falls in the hole and disappears in the Fermi sea. Its quantum mechanical state was unknown and seems lost. The entanglement, however, acts like a "soul" that transfers the state from the earthly electron to the heavenly electron. Here is how it works: In order to fill the hole, the spin of the earthly electron and that of the hole have to line up. The entanglement ensures that this spin correlation is inherited by the heavenly electron. The spin of the heavenly electron is therefore no longer distributed isotropically, but has acquired the state of the earthly electron. This instantaneous transfer of a quantum mechanical state between two distant particles is what Bennett et al. have called teleportation , with a nod to StarTrek.
Although teleportation is instantaneous, Einstein can rest assured: There is no instantaneous transfer of information. Since it is unpredictable when a tunneling attempt is successful, a message will need to be sent by regular (classical) means that the teleportation has happened. The distant electron can not measure whether its state is still isotropic or not, because any measurement will destroy the quantum mechanical state itself. The instantaneous transfer of the state from one to the other electron is necessary to satisfy the no-cloning theorem from quantum mechanics: At no instant does there exist more than a single copy of the state .
The state is the qubit in a quantum computer. Teleportation makes it possible in principle to transport that state from one part of the electrical circuit to the other, without having to disturb that state by a measurement. That is the long-term motivation of our research. On the short term, it would be a major breakthrough if the entanglement of the electron-hole pair could be measured. Teleportation over a distance of a few micrometers would then be the logical next step. A small step, perhaps, for Captain Kirk, but a giant step for science.
A Dutch version of this article appeared in: Nederlands Tijdschrift voor Natuurkunde 70, 112 (2004).
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