Scientists are using quantum computing to help them discover signs of life on other planets
Quantum computers are assisting researchers in scouting the universe in search of life outside of our planet – and although it’s far from certain they’ll find actual aliens, the outcomes of the experiment could be almost as exciting.
During the eight-week program, quantum resources will be combined with classical computing tools to resolve complex calculations with better accuracy, with the end goal of finding out whether quantum computing could provide a useful boost to the work of astrophysicists, despite the technology’s current limitations.
SEE: There are two types of quantum computing. Now one company says it wants to offer both
Detecting life in space is as tricky a task as it sounds. It all comes down to finding evidence of molecules that have the potential to create and sustain life – and because scientists don’t have the means to go out and observe the molecules for themselves, they have to rely on alternative methods.
Typically, astrophysicists pay attention to light, which can be analyzed through telescopes. This is because light – for example, infrared radiation generated by nearby stars – often interacts with molecules in outer space. And when it does, the particles vibrate, rotate, and absorb some of the light, leaving a specific signature on the spectral data that can be picked up by scientists back on Earth.
For researchers, therefore, all that is left to do is to detect those signatures and trace back to which molecules they correspond.
The problem? MIT researchers have previously established that over 14,000 molecules could indicate signs of life in exoplanets’ atmospheres. In other words, there is still a long way to go before astrophysicists have drawn a database of all the different ways that those molecules might interact with light – of all the signatures that they should be looking for when pointing their telescopes to other planets.
That’s the challenge that the University of Hull has set for itself: the institution’s Centre for Astrophysics is effectively hoping to generate a database of detectable biological signatures.
For over two decades, explains David Benoit, senior lecturer in molecular physics and astrochemistry at the University of Hull, researchers have been using classical means to try and predict those signatures; but the method is rapidly running out of steam.
The calculations carried out by the researchers at the center in Hull involve describing exactly how electrons interact with each other within a molecule of interest – think hydrogen, oxygen, nitrogen and so on. “On classical computers, we can describe the interactions, but the problem is this is a factorial algorithm, meaning that the more electrons you have, the faster your problem is going to grow,” Benoit tells ZDNet.
“We can do it with two hydrogen atoms for example, but by the time you have something much bigger, like CO2, you’re starting to lose your nerve a little bit because you’re using a supercomputer and even they don’t have enough memory or computing power to do that exactly.”
Simulating these interactions with classical means, therefore, ultimately comes at the cost of accuracy. But as Benoit says, you don’t want to be the one claiming to have detected life on an exo-planet when it was actually something else.
Unlike classical computers, however, quantum systems are built on the principles of quantum mechanics – those that govern the behavior of particles when they are taken at their smallest scale: the same principles as those that underlie the behavior of electrons and atoms in a molecule.
This prompted Benoit to approach Zapata with a “crazy idea”: to use quantum computers to solve the quantum problem of life in space.
“The system is quantum, so instead of taking a classical computer that has to simulate all of the quantum things, you can take a quantum thing and measure it instead to try and extract the quantum data we want,” explains Benoit.
Quantum computers, by nature, could therefore allow for accurate calculations of the patterns that define the behavior of complex quantum systems like molecules, without calling for the huge compute power that a classical simulation would require.
The data that is extracted from the quantum calculation about the behavior of electrons can then be combined with classical methods to simulate the signature of molecules of interest in space, when they come into contact with light.
It remains true that the quantum computers that are currently available to carry out this type of calculation are limited: most systems don’t break the 100-qubit count, which is not enough to model very complex molecules.
SEE: Preparing for the ‘golden age’ of artificial intelligence and machine learning
Benoit explains that this has not put off the center’s researchers. “We are going to take something small and extrapolate the quantum behavior from that small system to the real one,” says Benoit. “We can already use the data we get from a few qubits, because we know the data is exact. Then, we can extrapolate.”
That is not to say that the time has come to get rid of the center’s supercomputers, continues Benoit. The program is only starting, and over the course of the next eight weeks, the researchers will be finding out whether it is possible at all to extract those exact physics on a small scale, thanks to a quantum computer, in order to assist large-scale calculations.
“It’s trying to see how far we can push quantum computing,” says Benoit, “and see if it really works, if it’s really as good as we think it is.”
If the project succeeds, it could constitute an early use case for quantum computers – one that could demonstrate the usefulness of the technology despite its current technical limitations. That in itself is a pretty good achievement; the next milestone could be the discovery of our exo-planet neighbors.


Today’s computers use pulses of electricity and flipping magnets to manipulate and store data. But information can be processed in many other, weirder, ways .
1. Optical computing
There’s nothing weird about encoding data in light – global communications depend on optical fibre. But using light signals to actually process data and carry out computations is still not practical.
Optical computers are a worthwhile goal because using light could increase a computer’s speed and the quantity of data it can handle. But trapping, storing and manipulating light is difficult.
Research by people like Paul Braun, at the University of Illinois, Urbana Champaign, US, is bringing us closer to this goal. He has created 3D optical waveguides out of photonic crystals that should make possible to trap light, slow it down and bend it around sharp corners, without fear of it escaping.
Meanwhile Mikhail Lukin at Harvard University has developed what is essentially an optical version of the transistor that underlies all today’s computing power. Lukin and colleagues have created a way to make a single photon from one light signal switch another light signal on and off.
2. Quantum computing
If you want to tear up all the rules of classical computing, look no further than quantum computers. Instead of using electronic bits of information that exist in either 1 or 0 states, they use quantum mechanical effects to create qubits that can be in both states at once.
Calculations show that this ability allows many parallel computations to be carried out. As the number of qubits a quantum computer increases, the data it can process increases exponentially.
That would make possible things that are unfeasible with today’s computers – such as rapidly factoring extremely large numbers to crack cryptographic keys.
3. DNA computing
DNA may be the perfect material for carrying out computations. In a sense that is precisely what it evolved to do: DNA processes data and runs programs stored in sequences of genomic base pairs, as well as coordinating proteins that process information themselves to keep organisms alive.
The first person to co-opt these processes for computational problems was Leonard Adleman at the University of Southern California. In 1994, he used DNA to solve a well-known mathematical problem called the 7-point Hamiltonian Path problem.
The basic principle is to use sequences of DNA to recognise shorter “input” strands, and to produce different “output” sequences. The results can then be read, for example, through the activation of fluorescent proteins.
Recently DNA-computing enthusiasts have become interested in having their creations go to work inside biological systems like the human body. It makes sense, because that’s where they fit in best – and where conventional computers fit in least.
4. Reversible computing
Some people think we should be recycling our bits as well as our trash.
Hardware companies have long tried to reduce the power consumption of computers. One unusual way to do this is by engineering chips that are “reversible”.
Normally every computational operation that involves losing a bit of information also discards the energy used to represent it. Reversible computing aims to recover and reuse this energy.
One way to do this, which is being developed by Michael Frank at the University of Florida, US, involves making versions of logic gates than can run in reverse.
Every computing operation involves feeding inputs into logic gates, which produce output signals. Instead of discarding the energy of those signals, Frank’s gates run in reverse after every operation. That returns the energy of the output signal to the start of the circuit where it is used to carry a new input signal.
It may sound odd, but according to Frank, as computing power improves it won’t be long before chips’ wastefulness will be a major limit to their performance.
5. Billiard Ball computing
Computing today involves chain reactions of electrons passing from molecule to molecule inside a circuit. So it makes sense to try and harness other kinds of chain reaction for computing – even dominoes or marbles.
Logic gates have been made by carefully arranging dominoes or chutes for marbles to roll down (video).
Basic computing circuits like half-adders can also be made.
But making something as powerful as a microprocessor this way would require acres of space – unless your balls or dominoes are very small.
Researchers at IBM have experimented with logic circuits that use cascades of atoms bouncing off each other like billiard balls to pass information along their length.
Such gates can only be used once, but could be significantly smaller than even the tiniest existing transistors.
6. Neuronal computing
Why start from scratch when you can borrow already successful ideas? Some researchers hope to get ahead by copying nature’s very own computers.
Output from light sensors on the robot was passed to the neurons, and their responses used to control the robot’s movement. The brain cells normally used by the lamprey to orientate itself proved capable of making the robot follow a light source.
It’s not the first time a critter’s brain has been co-opted in this way.
Claire Rind, a neurobiologist at the University of Newcastle, UK, used recordings of the neuronal activity of locusts watching manoeuvring “TIE-fighter” spacecraft from the movie Star Wars to develop extremely accurate obstacle avoidance systems.
7. Magnetic (NMR) computing
Every glass of water contains a computer, if you just know how to operate it.
Susan Stepney and colleagues at the University of York, UK, use strong magnetic fields (nuclear magnetic resonance) to control and observe the way in which molecules interact. This method can represent information in 3D and can also exploit the natural dynamics of how molecules interact.
If successful it may prove possible to model something as complex as our atmosphere using just a thimble of water.
So far, however, the group have only carried out a proof of principle by, somewhat ironically, simulating the water-based computer on a classical computer.
8. Glooper Computer
One of the weirdest computers ever built forsakes traditional hardware in favour of “gloopware”. Andrew Adamatzky at the University of the West of England, UK, can make interfering waves of propagating ions in a chemical goo behave like logic gates, the building blocks of computers.
The waves are produced by a pulsing cyclic chemical reaction called the Belousov-Zhabotinsky reaction.
Adamatzky has shown that his chemical logic gates can be used to make a robotic hand stir the mixture in which they exist. As the robot’s fingers stimulate the chemicals further reactions are triggered that control the hand.
The result is a sort of robotic existential paradox – did the chemical brain make the robot’s hand move, or the hand tell the brain what to think? Eventually Adamatzky aims to couple these chemical computers to an electroactive gel-based “skin” to create a complete “blob-bot”.
9. Mouldy computers
Even a primitive organism like slime mould can be used to solve problems that are tricky for classical computers.
Toshiyuki Nakagaki at the Institute of Physical and Chemical Research in Nagoya, Japan, has shown that slime mould can work out the shortest route through a maze.
In his experiments, the masses of independent amoeba-like cells that act as a single organism would initially spread out to explore all the possible paths of a maze.
But when one train of cells found the shortest path to some food hidden at the maze’s exit the rest of the mass stopped exploring. The slime mould then withdrew from the dead end routes and followed the direct path to the food.
This is interesting for computer scientists because maze solving is similar to the travelling salesman problem, which asks for the shortest route between a number of points in space. The problem quickly scales in complexity as more points are added, making it a tough problem for classical computers.
10. Water wave computing
Perhaps the most unlikely place to see computing power is in the ripples in a tank of water.
Using a ripple tank and an overhead camera, Chrisantha Fernando and Sampsa Sojakka at the University of Sussex, used wave patterns to make a type of logic gate called an “exclusive OR gate”, or XOR gate.
Perceptrons, a type of artificial neural network, can mimic some types of logic gates, but not a XOR. Only encoding the behaviour of a XOR gate into ripples made it possible for the perceptron to learn how that gate works.


The researchers, from the University of Cambridge, were able to inject a ‘needle’ of highly fragile quantum information in a ‘haystack’ of 100,000 nuclei.
Researchers during a recent study have found a way to use light and a single electron to communicate with a cloud of quantum bits and sense their behaviour. This discovery will help in making it possible to detect a single quantum bit in a dense cloud.
The researchers, from the University of Cambridge, were able to inject a ‘needle’ of highly fragile quantum information in a ‘haystack’ of 100,000 nuclei. Using lasers to control an electron, the researchers could then use that electron to control the behaviour of the haystack, making it easier to find the needle. They were able to detect the ‘needle’ with a precision of 1.9 parts per million: high enough to detect a single quantum bit in this large ensemble.
The technique makes it possible to send highly fragile quantum information optically to a nuclear system for storage, and to verify its imprint with minimal disturbance, an important step in the development of a quantum internet based on quantum light sources. The results are reported in the journal Nature Physics.
The first quantum computers – which will harness the strange behaviour of subatomic particles to far outperform even the most powerful supercomputers – are on the horizon. However, leveraging their full potential will require a way to network them: a quantum internet. Channels of light that transmit quantum information are promising candidates for a quantum internet, and currently, there is no better quantum light source than the semiconductor quantum dot: tiny crystals that are essentially artificial atoms.
However, one thing stands in the way of quantum dots and a quantum internet: the ability to store quantum information temporarily at staging posts along with the network.
“The solution to this problem is to store the fragile quantum information by hiding it in the cloud of 100,000 atomic nuclei that each quantum dot contains, like a needle in a haystack,” said Professor Mete Atature from Cambridge’s Cavendish Laboratory, who led the research. “But if we try to communicate with these nuclei like we communicate with bits, they tend to ‘flip’ randomly, creating a noisy system.”
The cloud of quantum bits contained in a quantum dot don’t normally act in a collective state, making it a challenge to get information in or out of them. However, Atature and his colleagues showed in 2019 that when cooled to ultra-low temperatures also using light, these nuclei can be made to do ‘quantum dances’ in unison, significantly reducing the amount of noise in the system.
Now, they have shown another fundamental step towards storing and retrieving quantum information in the nuclei. By controlling the collective state of the 100,000 nuclei, they were able to detect the existence of the quantum information as a ‘flipped quantum bit’ at an ultra-high precision of 1.9 parts per million: enough to see a single bit flip in the cloud of nuclei.
“Technically this is extremely demanding,” said Atature, who is also a Fellow of St John’s College. “We don’t have a way of ‘talking’ to the cloud and the cloud doesn’t have a way of talking to us. But what we can talk to is an electron: we can communicate with it sort of like a dog that herds sheep.”
Using the light from a laser, the researchers are able to communicate with an electron, which then communicates with the spins, or inherent angular momentum, of the nuclei.
By talking to the electron, the chaotic ensemble of spins starts to cool down and rally around the shepherding electron; out of this more ordered state, the electron can create spin waves in the nuclei.
“If we imagine our cloud of spins as a herd of 100,000 sheep moving randomly, one sheep suddenly changing direction is hard to see,” said Atature. “But if the entire herd is moving as a well-defined wave, then a single sheep changing direction becomes highly noticeable.”
In other words, injecting a spin-wave made of a single nuclear spin-flip into the ensemble makes it easier to detect a single nuclear spin-flip among 100,000 nuclear spins.
Using this technique, the researchers are able to send information to the quantum bit and ‘listen in’ on what the spins are saying with minimal disturbance, down to the fundamental limit set by quantum mechanics.
“Having harnessed this control and sensing capability over this large ensemble of nuclei, our next step will be to demonstrate the storage and retrieval of an arbitrary quantum bit from the nuclear spin register,” said co-first author Daniel Jackson, a PhD student at the Cavendish Laboratory.
“This step will complete a quantum memory connected to light – a major building block on the road to realising the quantum internet,” said co-first author Dorian Gangloff, a Research Fellow at St John’s College.
Besides its potential usage for a future quantum internet, the technique could also be useful in the development of solid-state quantum computing.


Future of Information Systems
Today’s computers use bits as data units. A bit value can only be either 0 or 1, as we discussed in Chapter 2. Quantum computers use qubit, which can represent a combination of both 0 and 1 simultaneously, leveraging the principles of quantum physics. This is a game-changer for computing and will disrupt all aspects of information technology. The benefits include a significant speed increase in calculations that will enable solutions for unsolvable problems today. However, there are many technical problems to be solved yet since all the IS elements will need to be re-imagined. Google announced the first real proof of a working quantum computer in 2019 (Menard, et al., 2020). Menard et al. also indicated that the industries that would benefit from this new computer type would be industries with complex problems to solve, such as pharmaceutical, autonomous vehicles, cybersecurity, or intense mathematical modeling such as Finance, Energy. For a full report, please visit McKinsey.com.
A blockchain is a set of blocks or a list of records linked using cryptography to record a transaction and track assets in a network. Anything of value can be considered an asset and be tracked. Examples include a house, cash, patents, a brand. Once a transaction is recorded, it cannot be changed retroactively. Hence, it is considered highly secured.
Blockchain has many applications, but bitcoin is mostly associated with it because it was the first application using blockchain technology. Sometimes bitcoin and blockchain are mistakenly meant to be the same thing, but they are not.
Bitcoin is digital money or a cryptocurrency. It is an open-source application built using blockchain technology. It is meant to eliminate the need for a central bank since people can directly send bitcoins. Simply put, bitcoin keeps track of a list of who sends how many bitcoins to another person. One difference with today’s money is that a bitcoin's value fluctuates since it works like a stock. Anyone can buy different bitcoin cryptocurrencies or other cryptocurrencies on bitcoin exchanges such as Coinbase. Bitcoin and other cryptocurrencies are accepted by a few organizations such as Wikimedia, Microsoft, Wholefoods. However, bitcoin’s adoption is still uncertain. If the adoption by major companies is accelerated, then banking locally and globally will change significantly.
Some early businesses have begun to use blockchain as part of their operations. Kroger uses IBM blockchain to trace food from the farms to its shelves to respond to food recalls quickly (IBM.com.) Amazon Managed Blockchain is a fully managed service that makes it easy to create and manage scalable blockchain networks.
Artificial Intelligence (AI)
Artificial intelligence (AI) comprises many technologies to duplicate the functions of the human brain. It has been in research since the 1950s and has seen an ebb and flow of interest. To understand and duplicate a human brain, AI is a complex interdisciplinary effort that involves multiple fields such as computer science, linguistics, mathematics, neuroscience, biology, philosophy, and psychology. One approach is to organize the technologies as below, and commercial solutions have been introduced:
Expert systems: also known as decision support systems, knowledge management. These solutions have been widely deployed for decades, and we have discussed in earlier chapters such as knowledge management, decision support, customer relationship management system, financial modeling.
Robotics: this trend is more recent even though it has been in research for decades. Robots can come in different shapes, such as a familiar object, an animal, or a human. It can be tiny or as big as it can be designed:
A nanobot is a robot whose components are on the scale of about a nanometer.
A robot with artificial skins to look like a human is called a humanoid. They are being deployed in limited situations such as assistants to police, senior citizens who need help, etc. Two popular robots are Atlas from Boston Dynamic and humanoid Sophia from Hanson Robotics.
Consumer products such as the smart vacuum iRobot Roomba are now widely available. The adoption of certain types of robots has accelerated in some industries due to the pandemic: Spot, the dog-like robot from Boston dynamics, is used to patrol for social distancing.
Natural language: voice as a form of communication with our smart devices is now the norm—for example, Apple’s Siri, Amazon’s Alexa.
Vision: advanced progress has been made towards camera technologies and solutions to store and manipulate visual images. Examples include advanced security systems, drones, face recognition, smart glasses, etc.
Learning systems: Learning systems allow a computer (i.e., a robot) to react to situations based on the immediate feedback it receives or the collection of feedback stored in its system. Simple forms of these learning systems can be found today in customers' online-chat support, also known as ‘AI bot.’ One such example is IBM’s Watson Assistant.
Neural networks: This is a collection of hardware and software technologies. The hardware includes wearable devices that allow humans to control machines using thoughts such as Honda Motor’s Brain-Machine Interface. This is still in the research phase, but its results can impact many industries such as healthcare.
The goal of 100% duplicating a human brain has not been achieved yet since no AI systems have passed the Alan Turing test known as Turing Test to answer the question 'Can a machine think?" Alan is widely considered a founder of the AI field and devises a test to a machine's ability to show the equivalent intelligent behavior to that humans. The test does not look for correct answers but rather answers closely resemble those a human would give.
Even though AI has not been to duplicate a human brain yet, its advances have introduced many AI-based technologies such as AI bot, robotics in many industries. AI progress has contributed to producing many practical business information systems that we discussed throughout this book such as, voice recognition, cameras, robots, autonomous cars, etc. It has also raised concerns over how ethical is the development of some AI technologies as we discussed in previous chapters.
Advances in artificial intelligence depend on the continuous effort to collect vast amounts of data, information, and knowledge, advances in hardware, sophisticated methods to analyze both unconnected and connected large datasets to make inferences to create new knowledge, supported by secured, fast networks.
Boston Dynamics’ dog-like robot Spot is being used on coronavirus social distancing patrol (2020). Retrieved December 13, 2020, from https://www.cnbc.com/2020/05/15/boston-dynamics-dog-like-robot-spot-used-on-social-distancing-patrol.html.
Changing your idea of what robots can do. Retrieved December 13, 2020, from https://www.bostondynamics.com/.
Honda's Brain-Machine Interface: controlling robots by thoughts alone (2009). Retrieved December 11, 2020, from https://newatlas.com/honda-asimo-brain-machine-interface-mind-control/11379/#:~:text=Honda%20Research%20Institute%2C%20Japan%2C%20has,using%20nothing%20more%20than%20thought.&text=Then%2C%20the%20doors%20will%20be,and%20act%20directly%20upon%20them.
Kroger uses IBM Blockchain technology for farm to fork food traceability. Retrieved December 11, 2020, from https://mediacenter.ibm.com/media/Kroger+uses+IBM+Blockchain+technology+for+farm+to+fork+food+traceability/0_527q9xfy.
Menard A., Ostojic I., and Patel M. (2020, February 6). A game plan for quantum computing. Retrieved December 10, 2020, from https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/a-game-plan-for-quantum-computing.
The smarter AI assistant for business. Retrieved December 11, 2020, from https://www.ibm.com/cloud/watson-assistant-2/


How a Student Photographed a Single Atom With a Store-Bought Camera
Look closely and you’ll see it: a pale, purple pixel hanging in a black field between two cylindrical needles.What looks like a shimmering speck of dust is actually something much, much smaller: a single atom of strontium, isolated in an ion-trap machine at the University of Oxford.
That’s small. Really small. Each atom is roughly 0.25 nanometers (or billionths of a meter) across; billions of the atoms would fit comfortably inside a single red blood cell.
How do you capture a photo of something this seemingly infinitesimally small? One photographer, David Nadlinger, used a standard digital camera — but he had some help setting up the shot courtesy of Oxford’s Ion Trap Quantum Computing lab, where he is researching for his Ph.D. On Feb. 12, Nadlinger won first place in a national science photography competition organized by the Engineering and Physical Sciences Research Council for capturing this rare photo of a single illuminated atom.
“I think what makes this picture particularly interesting to people is that you can see the surrounding apparatus,” Nadlinger told Live Science. “And I think people are also surprised by how big the atom looks here. … I hope I’m not undoing 100 years of science education with this photo — atoms actually are unbelievably small!”
To be clear, Nadlinger said, the purple speck at the center of this photo is not the true size of the strontium atom itself; it’s the light from an array of surrounding lasers being re-emitted by the atom. When bathed in a specific wavelength of blue light, strontium creates a glow hundreds of times wider than the radius of the atom itself (which is about a quarter of a nanometer, or 2.5×10 to the -7 meters, Nadlinger said). This glow would be barely perceptible with the naked eye but becomes apparent with a little camera manipulation.
“The apparent size you see in the picture is what we’d call optical aberration,” Nadlinger said. “The lens we’re seeing it through is not perfect — also it’s slightly out of focus and slightly overexposed. You could compare it to looking at the stars in the night sky, which appear bright but are actually much, much smaller than the size they seem to be, just because our eyes (or the camera) don’t have enough resolution to process them.”
So, seeing a single atom with the naked eye is impossible. Trapping one in a lab, however, is a little more doable.
To catch an ion by the toe
To make a single atom camera-ready like this, researchers first need to turn it into an ion: an atom with an unequal number of protons and electrons, giving it a positive or negative net charge. “We can only ever trap charged particles,” Nadlinger said. “So, we take a stream of neutral strontium atoms, which come from an oven, and shine lasers at them to selectively photo-ionize them. This way, we can create single ions.”
When placed in an ion-trap apparatus, single atoms are held in place by four blade-shaped electrodes like those seen above and below the strontium speck in Nadlinger’s photo (two additional electrodes are out of view). These electrodes create a current that keeps the atom fixed on the vertical axis; the two needle-shaped cylinders on either side of the atom keep it trapped horizontally.
As the currents from these electrodes interact, they create what is called a rotating saddle potential. “You can see videos online where people literally take a saddle and rotate it and put a ball on it; because of the rotation, the ball actually stays in the center of the saddle. So that’s what these electrodes do to confine the ion,” Nadlinger said.
Once an atom is confined, an array of lasers hits the atom, which scatters light in all directions; in Nadlinger’s photo, you can see traces of the blue laser throughout the background. Using this system, researchers can potentially trap strings of hundreds of ions between the little electrodes, resulting in some stunning imagery.
“On our website, we have a picture of nine ions trapped in a string,” Nadlinger said. “In terms of the science, that’s actually more interesting than having a single bright pixel surrounded by the ion trap. But to illustrate the concept, this might be more appealing.”
Nadlinger does not believe he is the first researcher to take such a photo, but he may well be the most successful at capturing the public’s attention with one.
“A group led by Hans Dehmelt, a pioneer of ion trapping and a Nobel laureate [in 1989], once took a picture of a single barium atom in their lab,” Nadlinger said. “It was a single bright speck on a dark background, apart from some laser scatter. There’s this story that they submitted this image to some conference proceedings — and the image editor just stamped out the ion because he thought it was a speck of dust.”
Source: Live Science


If information cannot be destroyed, what happens when a black hole that has swallowed a mega belly full of information disappears?
A seemingly unsolvable black hole paradox first proposed by physicist Stephen Hawking may finally be resolved, by wormholes through space time.
The “black hole information paradox” refers to the fact that information cannot be destroyed in the universe, and yet when a black hole eventually evaporates, any information sucked up by this cosmic vacuum cleaner should be long gone. . The new study proposes that the paradox could be resolved by means of nature. latest cheat code: wormholes or passages in space-time.
“A wormhole connects the inside of the black hole and the outside radiation, like a bridge,” said Kanato Goto, a theoretical physicist at RIKEN’s Interdisciplinary Program for Theoretical and Mathematical Sciences in Japan. he said in a press release.
According to Goto’s theory, a second surface appears within a black hole’s event horizon, the boundary beyond which nothing can escape. Threads from a wormhole connect this surface to the outside world, entangling information between the interior of the black hole and the radiation that seeps through its edges.
Black hole information paradox
In the 1970s, Hawking discovered that black holes aren’t exactly black, but at first he didn’t realize what a big problem he had created. Before his discovery, physicists had assumed that black holes were extremely simple. Sure, all sorts of complicated stuff fell into it, but the black holes locked away all that information, never to be seen again.
But Hawking discovered that black holes do release radiation and it can eventually evaporate completely, in a process now known as Hawking radiation, but this radiation itself did not carry information. In fact, he couldn’t; by definition, the event horizon of a black hole prevents information from getting out. So when a black hole evaporates and eventually disappears from the universe, where did all its locked up information go?
This is the black hole information paradox. One possibility is that the information could be destroyed, which seems to violate everything we know about physics. (For example, if information can be lost, the past cannot be reconstructed from present events or future events cannot be predicted.) Instead, most physicists try to resolve the paradox by finding a way, any way, for the information inside the black hole to escape. through Hawking radiation. Thus, when the black hole disappears, the information is still present in the universe.
Either way, describing this process requires new physics.
“This suggests that general relativity and quantum mechanics as they currently stand are incompatible with each other,” said Goto. “We need to find a unified framework for quantum gravity technology.”
A tale of two entropies
In 1992, physicist Don Page, a former Hawking graduate student, saw the problem of the information paradox differently. He started by looking at quantum entanglement, that is, when distant particles have their fates linked. This entanglement acts as the quantum mechanical connection between the Hawking radiation and the black hole itself. Page measured the amount of entanglement by calculating “entanglement entropy,” which is a measure of the amount of information contained in the entangled Hawking radiation.
In Hawking’s original calculation, no information is leaked and the entanglement entropy always increases until the black hole finally disappears. But Page found that if black holes release information, the entropy of entanglement initially increases; then, halfway through the black hole’s lifetime, it decreases before finally reaching zero, when the black hole evaporates (i.e., all the information inside the black hole eventually escaped).
If Page’s calculations are correct, it suggests that if black holes allow information to leak out, then something special must be happening halfway through their lives. Although Page’s work hasn’t solved the information paradox, it has given physicists something juicy to work on. If they could give black holes a mid-life crisis, then this solution could resolve the paradox.
More recently, various teams of theorists have applied mathematical techniques borrowed from string theory – an approach to unifying Einstein’s relativity with quantum mechanics – to examine this problem. They were examining how spacetime near an event horizon might be more complex than scientists initially thought. How complex? As intricate as possible, allowing for all kinds of bends and curves on a microscopic scale.
His work led to two surprising features. One was the appearance of an “extreme quantum surface” just below the event horizon. This inner surface moderates the amount of information that comes out of the black hole. At first glance, it is not very useful. But when the black hole is in the middle of its life, it begins to dominate entanglement, reducing the amount of information released, so that the entropy of entanglement follows Page’s predictions.
Second, the calculations revealed the presence of wormholes, lots of them. These wormholes seemed to connect the extreme quantum surface to the outside of the black hole, allowing information to bypass the event horizon and be released as Hawking radiation.
But this earlier work only applied to highly simplified “toy” models (such as one-dimensional versions of black holes). With Goto’s work, this same result has now been applied to more realistic scenarios, a major advance that brings this work closer to explaining reality.
However, there are many questions. For one thing, it is not yet clear whether the wormholes that appear in the math are the same wormholes that we think of as shortcuts in time and space.
They are so deeply buried in mathematics that it is difficult to determine their physical meaning. On the one hand, it could literally mean wormholes going in and out of an evaporating black hole. Or it could simply be a sign that spacetime near a black hole is not local, which is a feature of entanglement: two entangled particles don’t need to be in causal contact to influence each other.
Originally published on Live Science.


Prior to the mid-18th century, it was tough to be a sailor. If your voyage required east-west travel, you couldn't set out to a specific destination and have any real hope of finding it efficiently.
At the time sailors had no reliable method for measuring longitude, the coordinates that measure a point's east-west position on the globe. To find longitude, you need to know the time in two places--the ship you're on, and the port you departed from. By calculating the difference between those times, sailors got a rough estimate of their position. The problem: The clocks back then just couldn't keep time that well. They lost their home port's time almost immediately after departing.
Today, time is just as important to navigation, only instead of calculating positioning with margins of errors measured in miles and leagues, we have GPS systems that are accurate within meters. And instead of springs and gears, our best timepieces rely on cesium atoms and lasers.
But given the history, it's fitting that scientists like Clayton Simien, a National Science Foundation (NSF)-funded physicist at the University of Alabama at Birmingham who works on atomic clocks, was inspired by the story of John Harrison, an English watchmaker who toiled in the 1700s to come up with the first compact marine chronometer. This device marked the beginning of the end for the "longitude problem" that had plagued sailors for centuries.
"If you want to measure distances well, you really need an accurate clock," Simien said.
Despite the massive leaps navigation technology has made since Harrison's time, scientists--many NSF-funded--are looking for new ways to make clocks more accurate, diminishing any variables that might distort precise timekeeping. Some, for example, are looking for ways to better synchronize atomic clocks on earth with GPS satellites in orbit, where atmospheric distortion can limit signal accuracy to degrees that seem minute, but are profound for the precise computer systems that govern modern navigation.
The National Institute of Standards and Technology, Department of Defense, join NSF in the search for even better atomic clocks. But today's research isn't just about building a more accurate timepiece. It's about foundational science that has other ramifications.
'One Mississippi,' or ~9 billion atom oscillations
Atomic clocks precisely measure the ticks of atoms, essentially tossing cesium atoms upward, much like a fountain. Laser-beam photons "cool down" the atoms to very low temperatures, so the atoms can transfer back and forth between a ground state and an excited state.
The trick to this process is finding just the right frequency to move directly between the two states and overcome Doppler shifts that distort rhythm. (Doppler shifts are increases or decreases in wave frequency as the waves move closer or further away -- much like the way a siren's sound changes depending on its distance.)
Laser improvements have helped scientists control atoms better and address the Doppler issue. In fact, lasers helped to facilitate something known as an optical lattice, which can layer atoms into "egg cartons" to immobilize them, helping to eliminate Doppler shifts altogether.
That shift between ground state and excited state (better known as the atomic transition frequency) yields something equivalent to the official definition of a second: 9,192,631,770 cycles of the radiation that gets a cesium atom to vibrate between those two energy states. Today's atomic clocks mostly still use cesium.
NSF-funded physicist Kurt Gibble, of Pennsylvania State University, has an international reputation for assessing accuracy and improving atomic clocks, including some of the most accurate ones in the world: the cesium clocks at the United Kingdom's National Physical Laboratory and the Observatory of Paris in France.
But accurate as those are, Gibble says the biggest advance in atomic clocks will be a move from current-generation microwave frequency clocks -- the only kind currently in operation -- to optical frequency clocks.
The difference between the two types of clocks lies in the frequencies they use to measure the signals their atoms' electrons emit when they change energy levels. The microwave technology keeps reliable time, but optical clocks offer significant improvements. According to Gibble, they're so accurate they would lose less than a second over the lifetime of the universe, or 13.8 billion years.
Despite that promise of more accurate performance, the optical frequency clocks don't currently keep time.
"So far, optical standards don't run for long enough to keep time," Gibble said. "But they will soon."
Optical frequency clocks operate on a significantly higher frequency than the microwave ones, which is why many researchers are exploring their potential with new alkaline rare earth elements, such as ytterbium, strontium and gadolinium.
"The higher frequency makes it a lot easier to be more accurate," Gibble said.
Gibble is starting work on another promising elemental candidate: cadmium. Simien, whose research employs gadolinium, has focused on minimizing--or eliminating if possible--key issues that limit accuracy.
"Nowadays, the biggest obstacle, in my opinion is the black body radiation shift," Simien said. "The black body radiation shift is a symptomatic effect. We live in a thermal environment, meaning its temperature fluctuates. Even back in the day, a mechanical clock had pieces that would heat up and expand or cool down and contract.
"A clock's accuracy varied with its environment. Today's system is no longer mechanical and has better technology, but it is still susceptible to a thermal environment's effects. Gadolinium is predicted to have a significantly reduced black body relationship compared to other elements implemented and being proposed as new frequency standards."
While Simien and Gibble agree that optical frequency research represents the next generation of atomic clocks, they recognize that most people don't really care if the Big Bang happened 13 billion years ago or 13 billion years ago plus one second.
"It's important to understand that one more digit of accuracy is not always just fine tuning something that is probably already good enough," said John Gillaspy, an NSF program director who reviews funding for atomic clock research for the agency's physics division. "Extremely high accuracy can sometimes mean a qualitative breakthrough which provides the first insight into an entirely new realm of understanding--a revolution in science."
Gillaspy cited the example of American physicist Willis Lamb, who in the middle of the last century measured a tiny frequency shift that led theorists to reformulate physics as we know it, and earned him a Nobel Prize. While research to improve atomic clocks is sometimes dismissed as trying to make ultra-precise clocks even more precise, the scientists working in the field know their work could potentially change the world in profound, unexpected ways.
"Who knows when the next breakthrough will come, and whether it will be in the first digit or the 10th?" Gillaspy continued. "Unfortunately, most people cannot appreciate why more accuracy matters."
From Wall Street to 'Interstellar'
Atomic clock researchers point to GPS as the most visible application of the basic science they study, but it's only one of this foundational work's potential benefits.
Many physicists expect it to provide insight that will illuminate our understanding of fundamental physics and general relativity. They say new discoveries will also advance quantum computing, sensor development and other sensitive instrumentation that requires clever design to resist natural forces like gravity, magnetic and electrical fields, temperature and motion.
The research also has implications beyond the scientific world. Financial analysts worry that worldwide markets could lose millions due to ill-synchronized clocks.
On June 30 th at 7:59:59 p.m. EDT, the world adds what is known as a "leap second" to keep solar time within 1 second of atomic time. History has shown, however, that this adjustment to clocks around the world is often done incorrectly. Many major financial markets are taking steps ranging from advising firms on how to deal with the adjustment to curtailing after-hours trading that would occur when the change takes place.
Gibble says the goal of moving to ever more accurate clocks isn't to more precisely measure time over a long period.
"It's the importance of being able to measure small time differences."
GPS technology, for example, looks at the difference of the propagation of light from multiple satellites. To provide location information, several GPS satellites send out signals at the speed of light--or one foot per nanosecond--saying where they are and what time they made their transmissions.
"Your GPS receiver gets the signals and looks at the time differences of the signals--when they arrive compared to when they said they left," Gibble said. "If you want to know where you are to a couple of feet, you need to have timing to a nanosecond--a billionth of a second."
In fact, he said, if you want that system to continue to accurately operate for a day, or for weeks, you need timing significantly better than that. Getting a GPS to guide us in deserts, tropical forests, oceans and other areas where roads aren't around to help as markers along the way--one needs clocks with nanosecond precision in GPS satellites to keep us from getting lost.
And if you're not traveling to those locales, then there's still the future to think about.
"Remember the movie, 'Interstellar,'" Simien said. "There is someone on a spaceship far away, and Matthew McConaughey is on a planet in a strong gravitational field. He experiences reality in terms of hours, but the other individual back on the space craft experiences years. That's general relativity. Atomic clocks can test this kind of fundamental theory and its various applications that make for fascinating science, and as you can see, they also expand our lives."


It was the eminent French philosopher and mathematician René Descartes who first suggested that the human mind may operate outside of the physical realm. He called it his mind-matter duality theory. The idea was that the human brain was above the physical world and could use its power to influence it. The “father of modern philosophy,” may have been more prescient than he’d ever realize.
Currently, a theoretical physicist is gearing up to test this theory in modern form. Lucien Hardy of the Perimeter Institute in Canada, will use an EEG machine, to see if the mind operates on the quantum level or outside of it. The results could have vast implications for our understanding of consciousness and free will.
The experiment centers on the concept of quantum entanglement. Here, particles influence each other, even when far apart. Photons are light particles. Say using a laser, you shoot them through a crystal. Two photons suddenly become entangled. Afterwards, they’re move quite a distance apart. If you interact with one photon it affects the other, instantaneously, no matter their distance from one another.
In the 1930’s, Einstein—puzzled by this, called it a “spooky action at a distance.” One problem is that acting upon one particle causes changes in the other faster than the speed of light, something relativity states is impossible.
Another weird effect, when we measure the spin of one entangled particle, the other always has the opposite spin, be it just around the corner from its partner or across the galaxy. This is as if measuring one influences the spin of the other at a rate faster than the speed of light. Is it true or is something else going on? This is one of the greatest mysteries of quantum physics.
In 1964, famed physicist John Bell developed an experiment to test the spin of entangled particles, to find out if they held some kind of hidden information, as Einstein thought, or if the particles actually communicated with each other at a rate faster than the speed of light. He developed the Bell test to evaluate the spin of entangled particles. Here, particles are separated. One goes to location A and the other to location B.
The spin of each is evaluated at each station. Since the angle of the measurement is taken at random, it isn’t possible to know the settings at any location beforehand. Each time particles are measured like this, when one registers a certain spin, say clockwise, the other always comes up its opposite.
According to Dr. Lucien, an experiment based off of the Bell test should be able to tell us if the human brain operates within quantum mechanics or outside of it. He’s recruiting 100 participants. Each will have their brain attached to an EEG machine through a skull cap covered with sensors. These record brainwaves.
Hardy wrote, “The radical possibility we wish to investigate is that, when humans are used to decide the settings (rather than various types of random number generators), we might then expect to see a violation of Quantum Theory in agreement with the relevant Bell inequality.” Participants will be 100 km. (approx. 62 mi.) apart. The signals from these caps will be used to change the settings on a measuring device.
If the measurements don’t match up as expected, it could challenge our current understanding of physics. “[If] you only saw a violation of quantum theory when you had systems that might be regarded as conscious, humans or other animals,” Hardy writes, it could mean that the consciousness is able to supersede natural law.
This would give a tremendous boost in the notion of free will, as a person’s will would literally defy the laws of physics. Yet, “It wouldn’t settle the question,” according to Hardy. Prevailing physics and neuroscience theories have favored predeterminism in recent decades. This experiment may also offer insight into human consciousness, where it stems from inside the brain, and even what it might be.
What are the implications if we find out the human mind operates outside of quantum physics?
The study fits into the fledgling field of quantum biology, which is shaking up our understanding of traditional biology in quite a number of ways. For instance, researchers at the University of California, Berkeley and at Washington University, in St. Louis, have found quantum effects operating within photosynthesis.
Biophysicist Luca Turin has a theory, based on quantum physics, to explain how our sense of smell works. Others in quantum biology theorize about how antioxidants and enzymes work, among other processes.
Splintering off of this is quantum neuroscience. Researchers here are looking at how quantum mechanics might explain the processes of the brain. Stuart Hameroff is a practicing anesthesiologist, and the director of the Center for Consciousness Studies, at the University of Arizona. He’s offered a theory using quantum mechanics to explain how anesthesia works.
According to Dr. Hameroff, consciousness may also be born on the quantum level. Physicist Matthew Fisher at the University of California, Santa Barbara, has proposed a way in which the brain might operate as a quantum computer. Hardy’s experiment could support Hameroff and even Fisher’s conclusions.
Others have doubted the claim. Since a quantum computer is very volatile system, any interference can cause decoherence, where the particles form a giant lump and no longer perform calculations. Critics argue that the human brain is awash in a host of different biochemicals and processes. So how could a quantum computer-like system operate there?


Present simple — passive voice
There are several reasons as to why we use the passive voice in English. In these notes, we are going to focus on the present simple in the passive voice. Generally, we use the passive voice when the focus is on the action and NOT on WHO or WHAT is performing the action.
Present Simple passive construction: am/is/are + past participle
Example verb: draw
|I am drawn||We are drawn|
|You are drawn||You (guys) are drawn|
|He/she/it is drawn||They are drawn|
The agent is unknown. We don’t know who is the agent
- The man who is believed to have stolen the goods must be brought to justice. (we don’t know who is the man)
We use the passive to emphasise the subject
- Paris and London are visited by many people each year. (The emphasis is on Paris and London).
We use the passive to talk about general truths
- Certain animals are known to attack humans.
We can use the passive if we want to be unclear or vague about the subject
- Mistakes are committed.
We use the passive when the subject is irrelevant
(We don’t care who or what has caused the action to be).
- English classes are taught here every day. (WHO teaches the classes is not important within the given situation).
We use the passive in a more formal atmosphere like a thesis or an important piece of writing, especially scientifically speaking
- The water is thus poured into the dish to form the desired product.
- The whole scientific process is done over three years.
Lesson #29: Present simple – passive voice
Construction: am/is/are + past participle (helped, known, found)
Example verb: make
|I am made||We are made|
|You are made||You (guys) are made|
|He/she/it is made||They are made|
- Which industries do you think will dominate the future, Sarah?
- Well, we’re living in a very technological era,1 and I think we’re set2 to see the birth of technologies such as blockchain, cloud computers, electric cars and quantum computing.
- It sounds incredible, doesn’t it?3
- It sure does. It is argued that cloud computing and quantum computers are the main innovations so far.4
- So, what is known about cloud computing thus far?5
- I only know from what I’ve read, but cloud computing is used by most of us already.6
- Oh really? How so?
- The cloud is used for such things like7 our email accounts, documents and photos with Google etc., things like that, I guess.
- Moreover, I’ve read that it’s expected8 we’ll see much more cloud computing in the future.
- I sure hope so!
- Well, we’re living in a very technological era. Here, the present continuous (we’re living) is used to talk about a present state. The state being ‘living in a very technological era’. The present simple could also be used here.
- I think we’re set. The passive voice in the present simple is used here (we are set). The past participle is ‘set’ (set – set – set), and it’s being used to emphasise the subject ‘we’.
- It sounds incredible, doesn’t it? ‘Doesn’t it’ is a question tag. The verb ‘do’ is used to form the question tag because ‘sounds’ is a normal verb. We always use ‘do’ as the default verb to make question tags with normal, non-auxiliary verbs.
- It is argued that cloud computing and quantum computers are the main innovations so far. ‘It is argued’ is a passive construction for the present simple tense. The construction being the verb to be in third person singular (is) and the past participle of ‘argue’, ‘argued’.
- What is known about cloud computing thus far? The present simple in the passive construction ‘is known’ is used because we don’t know anything about the subject.
- Cloud computing is used by most of us already. The present simple in the passive ‘is used’ details the passive voice in the present simple. Emphasis is put on ‘cloud computing’.
- The cloud is used for such things like… ‘is used’, is another use of the passive voice in present simple.
- I’ve read that it’s expected. ‘It’s expected’ is the passive voice in the present simple. The passive is used here to be unclear or vague about ‘what is expected’.
All passive forms:
- Articles (a/an, the, zero article)
- Pronouns: subject, object and possessive
- Question tags
- English conditionals
- Interrogatives in English
- Phrasal verbs
- Prefixes and suffixes
- Reported and direct speech
- Numbers: cardinal, ordinal, and Roman numbers
- The verb: “get”
- ‘Get’ vs. ‘go’ and ‘got’ vs. ‘gotten’
- Copular verbs
- Cleft sentences
- Subjunctive in English
- Vulgar and taboo in English
- Split infinitive
- Emphasis with inversion
- Gerunds in English
- To + infinitive
- Bare infinitive
- British and American spelling


RICHMOND, Va. (March 19, 2007) – Researchers have made an important advance in the emerging field of ‘spintronics’ that may one day usher in a new generation of smaller, smarter, faster computers, sensors and other devices, according to findings reported in today's issue of the journal Nature Nanotechnology.
The research field of ‘spintronics’ is concerned with using the ‘spin’ of an electron for storing, processing and communicating information.
The research team of electrical and computer engineers from the Virginia Commonwealth University’s School of Engineering and the University of Cincinnati examined the ‘spin’ of electrons in organic nanowires, which are ultra-small structures made from organic materials. These structures have a diameter of 50 nanometers, which is 2,000 times smaller than the width of a human hair. The spin of an electron is a property that makes the electron act like a tiny magnet. This property can be used to encode information in electronic circuits, computers, and virtually every other electronic gadget.
“In order to store and process information, the spin of an electron must be relatively robust. The most important property that determines the robustness of spin is the so-called ‘spin relaxation time,’ which is the time it takes for the spin to ‘relax.’ When spin relaxes, the information encoded in it is lost. Therefore, we want the spin relaxation time to be as long as possible,” said corresponding author Supriyo Bandyopadhyay, Ph.D., a professor in the Department of Electrical and Computer Engineering at the VCU School of Engineering.
“Typically, the spin relaxation time in most materials is a few nanoseconds to a few microseconds. We are the first to study spin relaxation time in organic nanostructures and found that it can be as long as a second. This is at least 1000 times longer than what has been reported in any other system,” Bandyopadhyay said.
The team fabricated their nanostructures from organic molecules that typically contain carbon and hydrogen atoms. In these materials, spin tends to remain relatively isolated from perturbations that cause it to relax. That makes the spin relaxation time very long.
The VCU-Cincinnati team was also able to pin down the primary spin relaxation mechanism in organic materials, which was not previously known. Specifically, they found that the principal spin relaxation mechanism is one where the spin relaxes when the electron collides with another electron, or any other obstacle it encounters when moving through the organic material. This knowledge can allow researchers to find means to make the spin relaxation time even longer.
“The organic spin valves we developed are based on self-assembled structures grown on flexible substrates which could have a tremendous impact on the rapidly developing field of plastic electronics, such as flexible panel displays,” said Marc Cahay, Ph.D., a professor in the Department of Electrical and Computer Engineering at the University of Cincinnati. “If the organic compounds can be replaced by biomaterials, this would also open news areas of research for biomedical and bioengineering applications, such as ultra-sensitive sensors for early detection of various diseases.”
“These are very exciting times to form interdisciplinary research teams and bring back the excitement about science and engineering in students at a very young age to raise them to become the future generations of nanopioneers,” Cahay said.
The fact that the spin relaxation time in organic materials is exceptionally long makes them the ideal host materials for spintronic devices. Organic materials are also inexpensive, and therefore very desirable for making electronic devices.
The VCU-Cincinnati research advances nanotechnology, which is a rapidly growing field where engineers are developing techniques to create technical tools small enough to work at the atomic level. Additionally, by using nanoscale components researchers have the ability to pack a large number of devices within a very small area. The devices themselves are just billionths of a meter; and trillions of them can be packed into an area the size of a postage stamp. Furthermore, they consume very little energy when they process data.
In 1994, Bandyopadhyay and colleagues were the first group to propose the use of spin in classical computing. Then two years later, they were among the first researchers to propose the use of spin in quantum computing. The recent work goes a long way toward implementing some of these ideas.
The work is supported by the U.S. Air Force Office of Scientific Research and the National Science Foundation.
Sandipan Pamanik, a graduate student in the VCU School of Engineering’s Department of Electrical and Computer Engineering, was first author of the study. The research team also included Carmen Stefanita, Ph.D., and graduate student, Sridhar Patibandla, both in the VCU Department of Electrical and Computer Engineering; and graduate students Kalyan Garre and Nick Harth from the University of Cincinnati’s Department of Electrical and Computer Engineering.


Flat solar panels still face big limitations when it comes to making the most of the available sunlight each day. A new spherical solar cell design aims to boost solar power harvesting potential from nearly every angle without requiring expensive moving parts to keep tracking the sun’s apparent movement across the sky.
The spherical solar cell prototype designed by Saudi researchers is a tiny blue sphere that a person can easily hold in one hand like a ping pong ball. Indoor experiments with a solar simulator lamp have already shown that it can achieve between 15 percent and 100 percent more power output compared with a flat solar cell with the same ground area, depending on the background materials reflecting sunlight into the spherical solar cell. The research group hopes its nature-inspired design can fare similarly well in future field tests in many different locations around the world.
“The placement and shape of the housefly’s eyes increase their angular field of view so they can see roughly 270 degrees around them in the horizontal field,” says Nazek El-Atab, a postdoctoral researcher in microsystems engineering at the King Abdullah University of Science and Technology (KAUST). “Similarly, the spherical architecture increases the ‘angular field of view’ of the solar cell, which means it can harvest sunlight from more directions.”
To create the spherical solar cell design, El-Atab and her colleagues built upon their previous work, which demonstrated how to create thinner and more flexible solar cell designs based on a corrugated groove technique. The new work is detailed in a paper that has been submitted for review to the journal MRS Communications.
Measurement setup of the spherical solar cell under a solar simulator in air and using a regular a white paper as the reflective background material.Photo: Nazek El-Atab/KAUST
Testing with the solar simulator lamp showed that the spherical solar cell provided 24 percent more power output over a traditional flat solar cell upon immediate exposure to sunlight. That power advantage jumped to 39 percent after both types of solar cells had begun to heat up and suffered some loss in power efficiency—an indication that the spherical shape may have some advantages in dissipating heat.
The spherical solar cell also delivered about 60 percent more power output than its flat counterpart when both could collect only scattered sunlight under a simulated roof rather than receiving direct sunlight. Additional experiments with different reflective backgrounds—including an aluminum cup, aluminum paper, white paper, and sand—showed that the hexagonal aluminum cup background helped the spherical solar cell outperform the flat solar cell by 100 percent in terms of power output.
The Saudi team created the spherical solar cell using the monocrystalline silicon solar cells that currently account for almost 90 percent of the world’s solar power production. That choice sprang from the goal of helping to maximize the light-harvesting potential of such solar cells, along with the aim of potentially making it easier to scale up production if the design proves cost efficient.
“What surprises me is the authors have demonstrated the ultra-flexibility that can be achieved with rigid silicon solar cells using the corrugation technique in a series of articles,” says Zhe Liu, a postdoctoral researcher in solar engineering at MIT, who was not involved in the study. “I’m more excited about the ability to make spherical cells, which means you can have industrial IBC-type (interdigitated back contact) silicon solar cells cover any shapes and ‘solarize’ everywhere.”
Previous solar cell designs have fabricated tiny microscale spherical cells—sometimes made with nanowires or quantum dot cells—on top of a flat surface to help better collect both direct and scattered sunlight, says Rabab Bahabry, an assistant professor of physics at the University of Jeddah in Saudi Arabia. But the larger spherical solar cell may offer improved efficiency and coverage compared with the microsphere arrays when it comes to collecting sunlight reflected from background surfaces.
Creating the large spherical solar cell required the researchers to etch alternating grooves in 15 percent of a flat solar cell to make a pattern resembling a band of elliptical shapes connected at the middle. A CO2 laser created the appropriate pattern in a polymeric hard mask covering the solar cell and allowed a deep reactive ion etching tool to create grooves in the exposed areas of the silicon solar cell. The flex and bend in those groove areas allowed the researchers to subsequently fold the solar cell into a spherical shape.
Dust accumulation on a spherical solar cell is limited to the silicon area with a small tilt angle.Image: Rabab Bahabry/University of Jeddah and KAUST
The loss of solar cell material in the areas that have been etched out reduces the overall potential solar power output. But the researchers see cost over time favoring spherical solar cells over flat solar cells in certain parts of the world because the spherical design is less prone to dust accumulation and may help dissipate heat that might otherwise reduce the solar cell’s efficiency. In addition, the spherical solar cells don’t require additional costly moving parts to continually track the sun.
Still, the spherical solar cells may not replace traditional solar cell technology at utility-scale solar power plants, says Liu at MIT. In his view, this particular spherical solar cell design could find use in more niche market applications. He noted that one of his colleagues is currently searching for a solar cell design to cover a golf ball so that it can power a tracker inside the ball. But Liu sees much promise in such ultra-flexible solar cell designs being installed in buildings, cars, or even mobile devices.
“The application of spherical design may seem very limited, but the ability to make commercial silicon solar cells into any shapes would enable broad adaption of photovoltaic in autonomous devices, such as IoT (Internet of Things) sensors, and autonomous vehicles,” Liu says. “If we can fully power these autonomous devices with shaped photovoltaic panels, this could be a game changer.”
For future testing, Liu says he would like to see how the spherical solar cell performs in a wide array of both outdoor and indoor lighting environments at different times of day. He also wants to see how well the spherical solar cells can be integrated into certain applications that they might power. And he is curious about seeing a “quantified cost” summary of all the processing steps required to make such spherical solar cells in order to better understand the technology’s commercialization potential.
The Saudi researchers had to manually fold and form their spherical solar cells in their latest demonstration, but they have already begun designing and developing ways to automate the process using “robotic hands” to mimic the manual folding, says Muhammad Mustafa Hussain, a professor of electrical and computer engineering at KAUST who was one of the study’s coauthors.
Eventually, Hussain and his colleagues envision building and testing large arrays of the spherical solar cells. And they’re already working on new shapes that resemble tents or umbrellas to see if those offer any advantages. They are also integrating solar cells with the surfaces of drones that have unusual shapes.
The COVID-19 pandemic that forced the closure of research labs has delayed the Saudi group’s initial plans for outdoor testing. But Hussain says the group still plans to move forward with field trials before the end of 2020. He expects help from the KAUST alumni network in eventually testing the spherical solar cells in California, along with countries such as Bangladesh, China, India, South Korea, Germany, Spain, Brazil, Colombia, Mexico, South Africa, Australia, and New Zealand.
“We will be creating arrays of spherical cells for 100-square-foot to 1,000-square-foot areas, and will compare functionality over cost benefit with that of traditional cells,” Hussain says. “Next, we will deploy it in different geographic locations throughout the year to understand its performance and reliability.”
Editor’s note: A correction to this article was made on 16 June 2020. The sentence on indoor experiments was revised to correct an inaccurate interpretation of the power output comparison between the spherical solar cell and flat solar cell in the submitted paper.
Jeremy Hsu has been working as a science and technology journalist in New York City since 2008. He has written on subjects as diverse as supercomputing and wearable electronics for IEEE Spectrum. When he’s not trying to wrap his head around the latest quantum computing news for Spectrum, he also contributes to a variety of publications such as Scientific American, Discover, Popular Science, and others. He is a graduate of New York University’s Science, Health & Environmental Reporting Program.


“SCIENTISTS FACTOR THE NUMBER 15.”
Hardly a headline to grab the popular imagination. But when it’s done by a quantum computer – and one that’s scalable – it’s time to take notice.
A paper published today in Science describes a five-atom quantum computer that can factor numbers – that is, start with a number and find numbers that, when multiplied, equal that first number. For instance, 15 factors into three times five.
It’s also a striking illustration of how quantum computers will smash today’s internet encryption – when they arrive, that is.
Computerised factoring is not new – quantum computers have factored numbers before (and those much bigger than 15). The key point here, though, is the new design can be upscaled to much more powerful versions simply by adding atoms.
Many of the world’s public key security systems, which encrypt online banking transactions and the like, operate on a simple principle: that it’s easy to multiply two large prime numbers to generate a gigantic number.
But given the gigantic number, it’s next to impossible to work out its factors, even using a computer.
In March 1991 the encryption company RSA set a challenge – they published a list of very large numbers and announced cash awards for whoever could factor them. The prizes went from $1,000 for factoring a 100-digit number, up to $200,000 for a 617-digit number.
A quarter of a century later, most of those numbers remain uncracked.
But with a large enough quantum computer, factoring huge numbers – even those 600 digits long – would be child’s play.
In classical computing, numbers are represented by either 0s or 1s called “bits”, which the computer manipulates in a series of linear, plodding logic operations trying every possible combination until it hits the right one.
Without any prior knowledge of the answers, the system returned the correct factors (15 = 5 x 3), with a confidence of more than 99%.
For example, to factor a 232-digit monster (the largest RSA number broken) took two years with hundreds of classical computers running in parallel – and ended up being solved too late to claim the $50,000 prize.
In contrast, quantum computing relies on atomic-scale units, or “qubits”, that can be 0, 1 or – weirdly – both, in a state known as a superposition. This allows quantum computers to weigh multiple solutions at once, making some computations, such as factoring, far more efficient than on a classical computer.
The problem has been building these qubits into a large-enough assembly to make meaningful calculations. The more atoms, the more they jostle together and the harder it is to control each one.
And as superposition is a very delicate state, a small bump will cause an atom to flip to 0 or 1 easily.
The new design, devised by physicists at the Massachusetts Institute of Technology and constructed at the University of Innsbruck in Austria, uses five calcium ions (atoms stripped of an electron) suspended in mid-air by electric and magnetic fields.
The ions are close enough to one another – about a hundredth the width of a human hair – to still interact. The researchers use laser pulses to flip them between 0, 1 and superposition to perform faster, more efficient logic operations.
Without any prior knowledge of the answers, the system returned the correct factors (15 = 5 x 3), with a confidence of more than 99%. Previous quantum computers achieved the same result with 12 ions.
And this system is “straightforwardly scalable”, according to Isaac Chuang, a physicist at MIT whose team designed the computer.
A truly practical quantum computer would likely require thousands of atoms manipulated by thousands of laser pulses. Meanwhile, other researchers are working on scalable computer systems using more conventional technology such as silicon.
“It might still cost an enormous amount of money to build – you won’t be building a quantum computer and putting it on your desktop anytime soon – but now it’s much more an engineering effort, and not a basic physics question,” says Chuang.
Whatever the cost, the abililty to crack internet security would make a large-scale quantum computer, literally, invaluable.
Read our handy primer on quantum mechanics – Quantum physics for the terminally confused
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We live in a time where the phrase “artificial intelligence” (called AI for short) is trendy and appears in the marketing descriptions of many products and services. But what is precisely AI?
Broadly speaking, AI originated as an idea to create artificial “thinking” along the lines of the human brain.
As of today, however, we can only make assumptions about how the human brain works, primarily based on medical research and observation. From a medical point of view, we know that the brain looks like a complex network of connections in which neurons are the main element and that our thoughts, memory, and creativity are a flow of electrical impulses. This knowledge has given hope to construct an analogous brain in an electronic version, either hardware or software, where neurons are replaced by electronics or software. However, since we are not 100% sure exactly how the brain works, all current models in AI are certain mathematical approximations and simplifications, serving only certain specific uses. Nevertheless, we know from observation that it is possible, for example, to create solutions that mimic the mind quite well – they can recognize the writing, images (objects), music, emotions, and even create art based on previously acquired experiences. However, the results of the latter are sometimes controversial.
What else does AI resemble the human brain in?
Well… it has to learn! AI solutions are based on one fundamental difference from classical algorithms: the initial product is a philosophical “tabula rasa”, or “pure mind”, which must first be taught.
In the case of complex living organisms, knowledge emerges with development: the ability to speak, to move independently, to name objects, and in the case of humans and some animal species, there are elements of learning organized in kindergartens, schools, universities, and during work and independent development. Analogously in most artificial intelligence solutions – the AI model must first receive specific knowledge, most often in the form of examples, to be able to later function effectively as an “adult” algorithm. Some of the solutions learn once, while others improve their knowledge while functioning (Online Learning, or Reinforced Learning). It vividly resembles the human community: some people finish their education and work for the rest of their lives in one company doing one task. Others have to train throughout their lives as their work environment changes dynamically.
Is AI already “smarter” than humans?
As an interesting aside, we can compare the “computing power” of the brain versus the computing power of computers. It, of course, will be a simplification because the nature of the two is quite different.
First, how many neurons does the average human brain have? It was initially estimated to be around 100 billion neurons. However, according to recent research (https://www.verywellmind.com/how-many-neurons-are-in-the-brain-2794889), the number of neurons in the “average” human brain is “slightly” less, by about 14 billion, or 86 billion neuronal cells. For comparison, the brain of a fruit fly is about 100 thousand neurons, a mouse 75 million neurons, a cat 250 million, a chimpanzee 7 billion. An interesting fact is an elephant’s brain (much larger than a human in terms of size), which has … 257 billion neurons, which is definitely more than the brain of a human.
From medical research, we know that for each neuron, there are about 1000 connections with neighboring neurons or so-called synapses, so in the case of humans, the total number of connections is around 86 trillion (86 billion neurons * 1000 connections). Therefore, in simplified terms, we can assume that each synapse performs one “operation”, analogous to one instruction in the processor.
At what speed does the brain work? In total … not much. We can determine it based on BCI type interfaces (Brain-Computer Interface), which not so long ago appeared as a result of the development of medical devices for electroencephalography (EEG), such as armbands produced by Emotiv, thanks to which we can control the computer using brain waves. Of course, they do not integrate directly with the cerebral cortex but measure activity by analyzing electrical signals. Based on this, we can say that the brain works at variable speed (analogous to the Turbo mode in the processor), and it is between 0.5Hz for the so-called delta state (complete rest) and about 100Hz for the gamma state (stress, full tension).
Thus, we can estimate the maximum computational power of the brain as 8.6 billion operations (8.6*10^15) or 8.6 Petaflops! Despite the relatively slow performance of the brain, this is a colossal number thanks to the parallelization of operations. From Wikipedia (https://en.wikipedia.org/wiki/Supercomputer), we learn that supercomputers did not break this limit until the first decade of the 21st century. The situation will change with the advent of quantum computers, which inherently work in parallel, just like the human brain. However, as of today, quantum computing technology for cyber threat hunting is still in its infancy.
In conclusion, at the moment, AI has not yet overtaken the human brain, but it probably will someday. However, we are only talking about learning speed here, leaving aside the whole issue of creativity, “coming up with” ideas, emotions, etc.
AI and mobile devices
Artificial intelligence applications require substantial computational power, especially at the so-called learning stage, and pose a significant challenge in integrating them with AR and VR solutions. Unfortunately, AR and VR devices mostly have very limited resources, as they are effectively ARM processor-based mobile platforms comparable in performance to smartphones. As a result, most artificial intelligence models are so computationally (mathematically) complex that they cannot be trained directly on mobile devices. OK – you can, but it will take an incredibly and unacceptably long time. So in most cases, to learn models, we use powerful PCs (clusters) and GPU gas pedals, mainly Nvidia CUDA. This knowledge is then “exported” into a simplified model “implanted” into AR and VR software or mobile hardware.
In our next blog post, you’ll learn how we integrated AI into VR and AR, how we dealt with the limited performance of mobile devices, and what we use AI for in AR and VR.


When it comes to programming languages, the first name that comes to mind typically is C. Dating back to the ‘70s when it was developed at AT&T Bell Laboratories by Dennis Ritchie and Ken Thompson, it was easy to learn by those who wanted to work on computer coding. Most existing computer programs were written in assembly language, communicating directly with hardware but being complex, long, and hard to debug. C offered ease and intuitiveness of use and brought in a totally new audience to computer programming.
Quantum computing represents the next stage in the development from classical computing. The latter encodes information as a series of 0s and 1s, while a qubit (from quantum computers) could be a 0, 1, or both at a time. Quantum computers use entangled quantum states with overlapping information bits to perform their calculations. This could make them much, much faster than classical computing at doing calculations and data-crunching, which is why they also looked at as the future of computing and as the source of programming languages for AI.
Working with the full potential of quantum computing requires two things:
- The most current technology
- A quantum programming language to describe quantum algorithms
Essentially, while the algorithm explains how to solve a problem, the programming language helps the computer to perform the necessary calculations by describing the algorithm.
Present approaches to quantum computation look to adapt and use existing tools and technologies, as this would allow them to be run on devices that will be available over the next few years. Current quantum languages are somewhat similar to assembly languages in their expressiveness, as the programmer must provide every operation the computer is to perform. The former is also at a lower level than the latter in some respects – chiefly, in describing operations on individual quantum bits, more like what low-level hardware description languages do. Another shortcoming is how closely they are tied to specific hardware, describing the behavior of underlying circuits precisely and thereby requiring highly-detailed individual programming instructions describing the required minutiae. Given the complexity of current programming languages for quantum computers, a new language is needed.
This is how Silq came about. It was created by researchers at ETH Zurich, Switzerland, and is claimed to be the first high-level quantum language in the world. Classical and quantum languages are currently quite far apart in their conceptual bases, and Silq looks to bridge that gap, offering an approach that is far more intuitive than imagined. And given how quantum computing could revolutionize AI, Silq could be a very useful programming language for AI.
Silq offers several advantages, some of which are detailed below:
- A level of abstraction close to that of C
- Better usage of the potential of quantum computers than existing languages
- The code used by Silq is more compact, faster, more intuitive, and easier to understand for programmers.
- Existing quantum languages make it difficult to directly support subexpressions such as (a+b) + c, which are directly supported by Silq.
- It facilitates the expression of the high-level intent of programmers through a descriptive view of quantum algorithms. A specialized compiler can take care of compiling these algorithms to low-level quantum circuits.
- Programs in Silq are less-focused on low-level details, which makes analyzing such programs easier than the programs written in existing quantum languages.
- Silq could facilitate the development of tools for analysis to support developers.
What keeps Silq ahead of other languages is its design. It is the first programming language for quantum computing whose design does not limit its focus to the construction and functionality of underlying hardware. The design instead pays due consideration to the mindset of a programmer when a problem is to be solved, and helps in finding a solution that does not need the understanding of each detail of the architecture and implementation of the computer.
Silq falls into the category of high-level programming languages, as it abstracts from technical details of a particular type of computer. It is the first such language for quantum computers, and is more expressive as it can use much lesser code to describe more complex algorithms and tasks. This is why programmers find it easier to comprehend and use, also because it works with different computer architectures.
Possibly the most important innovation of Silq is in dealing with a particular common source of errors. More than one intermediary step makes up the process of calculating a task by a computer, in which process intermediate results or temporary values are created. Classical computers automatically get rid of these values in what is known as a process of “garbage collection”, which however is dicey for quantum computers, as previously-calculated values can interfere with correct calculations due to interactions with current values (which is also called quantum entanglement). This requires an uncomputation technique that is more advanced, and Silq allows such identification and erasure automatically.
Silq is definitely a way ahead and is attracting more attention from computer scientists working on usable ideas. Given how it is easier to use, it could stimulate the development of further languages and algorithms for quantum computers.


Google researchers claim to have achieved a major milestone in computer science known as "quantum supremacy."
Google scientists explain their breakthrough in a research paper, a copy of which was obtained by Fortune, that was briefly posted to a NASA website earlier this week before subsequently being taken down.
NASA has been working with Google on one aspect of their quantum computing research. News of the paper's existence was first reported by The Financial Times on Friday.
Google has declined to comment on the report. If the technology company has indeed achieved the milestone, it is a significant step towards the day when quantum computers, which use the powerful properties of quantum physics to perform their calculations, will be able to solve a vast array complex problems that lie beyond the abilities of today's most advanced supercomputers.
Among the most anticipated uses of quantum computers is the ability to create new chemicals, like catalysts for producing nitrogen-based fertilizers or for use in cells in higher-powered batteries. Quantum computing could also be used to crack most commonly used forms of digital encryption. It may one day also be used to streamline logistics and delivery operations, as well as speeding up machine learning applications.
But "quantum supremacy" does not mean quantum computers have yet arrived in the sense that they will soon replace the conventional computers that power our lives.
What is quantum supremacy?
Quantum supremacy means only that researchers have been able to use a quantum computer to perform a single calculation that no conventional computer, even the biggest supercomputer, can perform in a reasonable amount of time.
In the case of Google, this calculation involved checking whether the output of an algorithm for generating random numbers was truly random.
The researchers were able to use a quantum computer to perform this complex mathematical calculation in three minutes and 20 seconds, according to the paper. They say it would have taken Summit 3—an IBM-built machine that is the world's most powerful commercially-available conventional computer—about 10,000 years to perform the same task.
How do quantum computers work?
Quantum computers work by harnessing the properties of quantum mechanics. Classical computers process information in a binary format, called bits, which can represent either a 0 or 1. Quantum computers, in contrast, use logical units called quantum bits, or qubits for short, that can be put into a quantum state where they can simultaneously represent both 0 and 1.
What's more, while the bits in a classical computer all operate independently from one another, in a quantum computer, the status of one qubit effects the status of all the other qubits in the system, so they can all work together to achieve a solution.
These two properties are what give quantum computers so much more potential power than conventional computers.
But while a conventional computer outputs the same answer to a problem every time you run a calculation, the outputs of a quantum computer are probabilistic. That means it does not always produce the same answer. So to use a quantum computer, you have to run a calculation through the system thousands or even millions of times, and the array of outputs converge around the answer that is most likely to be correct.
In the case of Google's research, the company used a new quantum processor, which it named Sycamore, that has 54 qubits (although one did not function properly, the researchers said, so only 53 were actually used in the experiment) which sampled the random number generating circuit it was testing some 1 million times.
What's so special about Sycamore?
Sycamore is not the world's largest quantum processor. Google itself had produced a 72 qubit system last year. And Rigetti, a California startup working on quantum computers, has said it plans to have a 128 qubit system ready soon. But Google's researchers said they made major advances in how long its qubits can remain in a quantum state and how each qubit interacts with the other qubits next to it.
That's important because when qubits fall out of a quantum state, they introduce errors into the calculations the quantum computer is performing. Those errors then have to be corrected by using additional qubits. These error rates are the reason that your laptop can beat today's quantum computers in getting a correct answer to most mathematical problems.
Does quantum supremacy make quantum computers better than conventional computers?
No. Google's achievement only means its quantum computer could outperform a classical supercomputer on this one complex calculation.
The Google researchers say in their paper that their quantum computer may also have uses in optimization problems, machine learning as well as materials science and chemistry.
But it is unclear how much of an advantage or increase in speed Google's new quantum computing hardware, which it used to achieve quantum supremacy, will have in these other applications.
And Google's machine is not yet powerful enough to tackle other difficult mathematical problems, such as breaking current encryption systems, a task which involves factoring very large prime numbers, according to the research paper.
For many business applications, in fact, today's quantum computers are no match for the power and accuracy of today's conventional laptops.
Could hackers armed with quantum computers steal my bitcoin?
For the moment, the public-private key encryption techniques on which bitcoin and other cryptocurrencies are based cannot be broken by a quantum computer. But Google's researchers, in their paper, predict that quantum computing power will continue to advance at a "double exponential rate," so those bitcoins may not be safe for all that much longer.
The fear of quantum computers being capable of breaking most common encryption techniques has lead the U.S. National Security Agency to call for the adoption of new techniques that use different kinds of math that are not susceptible to attack from a quantum computer. Although the U.S. has not yet settled on which class of new algorithms should be used, a number of startups are currently helping financial firms and governments prepare their systems to use such "post-quantum" encryption methods.
When can I have a quantum computer on my desk?
Not any time soon.
While almost any material that can be put into a quantum state can be used to form a qubit, the most advanced quantum systems today tend to use tiny bits of superconducting materials, often bonded together using fairly exotic materials. The qubits in Google's Sycamore processor used aluminum loops bonded with indium, an element that is about as rare as silver.
To put those materials into a quantum state, and to safeguard the qubits from interference from outside energy sources, the quantum processors have to be carefully suspended in large dilution freezers at temperatures colder than those found in deep space.
Ultimately, the companies racing to commercialize quantum computers— which besides Google and Rigetti, include IBM, Microsoft, Intel, D-Wave and a host of others—plan to offer customers the ability to run calculations on a quantum computer through the cloud. So it's more likely that one will never grace your desk, at all.