Superconductivity is a fascinating phenomenon in which, below a so-called critical temperature, a material loses all its resistance to electrical currents. In certain materials, at low temperatures, all electrons are entangled in a single, macroscopic quantum state, meaning that they no longer behave as individual particles but as a collective – resulting in superconductivity. The general theory for this collective electron behaviour has been known for a long time, but one family of materials, the cuprates, refuses to conform to the paradigm. It was long thought that for these materials the mechanism that ‘glues together’ the electrons must be special, but recently the attention has shifted and now physicists investigate the non-superconducting states of cuprates, hoping to find out their differences with normal superconductors.
Most superconductors, when heated to exceed their critical temperature, change into ‘ordinary’ metals. The quantum entanglement that causes the collective behaviour of the electrons fades away, and the electrons start to behave like an ordinary ‘gas’ of charged particles.
Cuprates are special, first of all because their critical temperature is considerably higher than that of other superconductors. On top of that, they have very special measurable properties even in their ‘metal phase’. In 2009, physicist Nigel Hussey observed experimentally that the electrons in these materials form a new type of structure, different from that in ordinary metals, and the term ‘strange metal’ was born.
At nearly the same time, originating in Stanford in the United States, physicists started applying the theoretical machinery of string theory – a theory for a very different phenomenon, the behavior of gravity at the quantum level – to the description of electrons in metals. Completely unexpectedly, this machinery turned out to be able to predict certain phenomena that experimentally were known to occur in cuprates and other strange metals. Theoretical physicists Jan Zaanen and Koenraad Schalm (Leiden University) were involved in the early stages of these developments and made important contributions. In 2017, the pioneering work was transformed into a national research programme funded by NWO: Strange Metals. The programme is a special collaboration that involves both experimental and theoretical groups.
Special behaviour at low temperatures
The higher the temperature of a material, the more ‘noise’ measurements will show. To make the special properties of the strange metal state clearly visible, one would like to study the material at a temperature that is as low as possible, at most 1 degree above the absolute temperature minimum of -273°C. The obstacle for this is superconductivity itself: most strange metals already turn into superconductors when cooled to temperatures around -200°C. For this reason, in the Strange Metals programme, the choice was made to focus exclusively on a material with the chemical name Bi2Sr2CuO6, also known as ‘Bi2201’. This material becomes superconducting at about 35 degrees above the absolute minimum temperature. That is still too ‘hot’ for good measurements, but now the researchers can use a trick: superconductivity can be suppressed by a magnetic field.
The general rule of thumb is: the larger the critical temperature of a material, the stronger the magnetic field required to suppress superconductivity. Since for Bi2201 the critical temperature is already quite low, the required magnetic field comes just within reach of the biggest magnets available in the Netherlands. This allowed PhD students Jake Ayres and Maarten Berben working within the groups of Hussey (HFML-FELIX, Bristol) and Van Heumen to eventually study the strange metal state of Bi2201 at various low temperatures and various magnetic field strengths.
In this domain, the differences between strange metals and ordinary metals become strikingly visible. For ordinary metals, for example, one expects the electrical resistance to increase quadratically with temperature: increase the temperature by a factor of two, and the resistance will grow by a factor of four. The same holds if it is not the temperature but the magnetic field that is increased. The Dutch/UK team has now shown that these golden rules do not hold for cuprates. In these materials a new phase exists where the resistance depends linearly on the temperature and field strength: if one of these increases by a factor of two, so does the resistance. Contrary to what was observed before, the group discovered that this behaviour persists for a large range of the parameters.
At the moment, there are two widely accepted theories that could explain the linear behaviour of the resistance. The first theory assumes that the linear behaviour only occurs near very specific values of the temperature and magnetic field strength. With the new measurements, this theory has now come under considerable pressure. The second theory is the theory of extreme quantum entanglement that comes from the string theoretic approach. Within this theory it is possible to observe the linear behavior for a large range of parameters. Surprisingly, therefore, it seems that to describe strange metals, one truly needs a theory that can also be used to describe quantum gravity!
Quantum gravity in the lab
The link between strange metals and quantum gravity has special observable effects. In an extensive analysis, the team shows that within the conventional models of electrical transport, it is absolutely impossible to properly explain the data. Their analysis shows that there exists a previously unobserved mechanism that makes the electrons lose energy. This loss occurs at extremely short time scales related to a fundamental constant of nature in quantum mechanics: Planck’s constant. According to general theory, this is the shortest time scale at which a quantum system can lose energy – something which moreover is only possible when the system is maximally entangled. This fingerprint of quantum gravity behaviour in the data excites many supporters of the link with string theory: it would be a first clue of physics far beyond the usual model of metals.
To shed further light on the tension between ‘normal’ and ‘strange’ behaviour of metals, further experiments are needed. In that respect, promising developments still lie ahead within the Strange Metals program. Using a technique called ‘optical spectroscopy’, Van Heumen expects to be able to provide new details soon, and the groups of Mark Golden (Amsterdam) and Milan Allan (Leiden) are also working on results that could cause new surprises when it comes to the mysterious relation between quantum gravity and strange metals.
Incoherent transport across the strange metal regime of overdoped cuprates, J. Ayres, M. Berben, M. Čulo, Y.-T. Hsu, E. van Heumen, Y. Huang, J. Zaanen, T. Kondo, T. Takeuchi, J. R. Cooper, C. Putzke, S. Friedemann, A. Carrington and N. E. Hussey. Nature 595 (2021) 661-666.


Feb 27, 2015
The values of two inherent properties of one photon – its spin and its orbital angular momentum – have been transferred via quantum teleportation onto another photon for the first time by physicists in China. Previous experiments have managed to teleport a single property, but scaling that up to two properties proved to be a difficult task, which has only now been achieved. The team's work is a crucial step forward in improving our understanding of the fundamentals of quantum mechanics and the result could also play an important role in the development of quantum communications and quantum computers.
Alice and Bob
Quantum teleportation first appeared in the early 1990s after four researchers, including Charles Bennett of IBM in New York, developed a basic quantum teleportation protocol. To successfully teleport a quantum state, you must make a precise initial measurement of a system, transmit the measurement information to a receiving destination and then reconstruct a perfect copy of the original state. The "no-cloning" theorem of quantum mechanics dictates that it is impossible to make a perfect copy of a quantum particle. But researchers found a way around this via teleportation, which allows a flawless copy of a property of a particle to be made. This occurs thanks to what is ultimately a complete transfer (rather than an actual copy) of the property onto another particle such that the first particle loses all of the properties that are teleported.
The protocol has an observer, Alice, send information about an unknown quantum state (or property) to another observer, Bob, via the exchange of classical information. Both Alice and Bob are first given one half of an additional pair of entangled particles that act as the "quantum channel" via which the teleportation will ultimately take place. Alice would then interact the unknown quantum state with her half of the entangled particle, measure the combined quantum state and send the result through a classical channel to Bob. The act of the measurement itself alters the state of Bob's half of the entangled pair and this, combined with the result of Alice's measurement, allows Bob to reconstruct the unknown quantum state. The first experimentation teleportation of the spin (or polarization) of a photon took place in 1997. Since then, the states of atomic spins, coherent light fields, nuclear spins and trapped ions have all been teleported.
But any quantum particle has more than one given state or property – they possess various "degrees of freedom", many of which are related. Even the simple photon has various properties such as frequency, momentum, spin and orbital angular momentum (OAM), which are inherently linked.
More than one
Teleporting more than one state simultaneously is essential to fully describe a quantum particle and achieving this would be a tentative step towards teleporting something larger than a quantum particle, which could be very useful in the exchange of quantum information. Now, Chaoyang Lu and Jian-Wei Pan, along with colleagues at the University of Science and Technology of China in Hefei, have taken the first step in simultaneously teleporting multiple properties of a single photon.
In the experiment, the team teleports the composite quantum states of a single photon encoded in both its spin and OAM. To transfer the two properties requires not only an extra entangled set of particles (the quantum channel), but a "hyper-entangled" set – where the two particles are simultaneously entangled in both their spin and their OAM. The researchers shine a strong ultraviolet pulsed laser on three nonlinear crystals to generate three entangled pairs of photons – one pair is hyper-entangled and is used as the "quantum channel", a second entangled pair is used to carry out an intermediate "non-destructive" measurement, while the third pair is used to prepare the two-property state of a single photon that will eventually be teleported.
The image above represents Pan's double-teleportation protocol – A is the single photon whose spin and OAM will eventually be teleported to C (one half of the hyper-entangled quantum channel). This occurs via the other particle in the channel – B. As B and C are hyper-entangled, we know that their spin and OAM are strongly correlated, but we do not actually know what their values are – i.e. whether they are horizontally, vertically or orthogonally polarized. So to actually transfer A's polarization and OAM onto C, the researchers make a "comparative measurements" (referred to as CM-P and CM-OAM in the image) with B. In other words, instead of revealing B's properties, they detect how A's polarization and OAM differ from B. If the difference is zero, we can tell that A and B have the same polarization or OAM, and since B and C are correlated, that C now has the same properties that A had before the comparison measurement.
On the other hand, if the comparative measurement showed that A's polarization as compared with B differed by 90° (i.e. A and B are orthogonally polarized), then we would rotate C's field by 90° with respect to that of A to make a perfect transfer once more. Simply put, making two comparative measurements, followed by a well-defined rotation of the still-unknown polarization or OAM, would allow us to teleport A's properties to C.
One of the most challenging steps for the researchers was to link together the two comparative measurements. Referring to the "joint measurements" box in the image above, we begin with the comparative measurement of A and B's polarization (CM-P). From here, either one of three scenarios can take place – one photon travels along path 1 to the middle box (labelled "non-destructive photon-number measurement"); no photons enter the middle box along path 1; or two single photons enter the middle box along path 1.
The middle box itself contains the second set of entangled photons mentioned previously (not shown in figure) and one of these two entangled photons is jointly measured with the incoming photons from path 1. But the researcher's condition is that if either no photons or two photons enter the middle box via path 1, then the measurement would fail. Indeed, what the middle box ultimately shows is that exactly one photon existed in path 1, and so exactly one photon existed in path 2, given that two photons (A and B) entered CM-P. To show that indeed one photon existed in path two required the third and final set of entangled photons in the CP-OAM box (not shown), where the OAM's of A and B undergo a comparative measurement.
The measurements ultimately result in the transfer or teleportation of A's properties onto C – although it may require rotating C's (as yet unknown) polarization and OAM depending on the outcomes of the comparative measurements, but the researchers did not actually implement the rotations in their current experiment. The team's work has been published in the journal Nature this week. Pan tells physicsworld.com that the team verified that "the teleportation works for both spin-orbit product state and hybrid entangled state, achieving an overall fidelity that well exceeds the classical limit". He says that these "methods can, in principle, be generalized to more [properties], for instance, involving the photon's momentum, time and frequency".
Physicist Wolfgang Tittel from the University of Calgary, who was not involved in the current work (but wrote an accompanying "News and Views" article in Nature) explains that the team verified that the teleportation had indeed occurred by measuring the properties of C after the teleportation. "Of course, the no-cloning theorem does not allow them to do this perfectly. But it is possible to repeat the teleportation of the properties of photon A, prepared every time in the same way, many times. Making measurements on photon C (one per repetition) allows reconstructing its properties." He points out that although the rotations were not ultimately implemented by the researchers, they found that "the properties of C differed from those of A almost exactly by the amount predicted by the outcomes of the comparative measurements. They repeated this large number of measurements for different preparations of A, always finding the properties of C close to those expected. This suffices to claim quantum teleportation".
While it is technically possible to extend Pan's method to teleport more than two properties simultaneously, this is increasingly difficult because the probability of a successful comparative measurement decreases with each added property. "I think with the scheme demonstrated by [the researchers], the limit is three properties. But this does not mean that other approaches, either other schemes based on photons, or approaches using other particles (e.g. trapped ions), can't do better," says Tittel.
Pan says that to teleport three properties, their scheme "needs the experimental ability to control 10 photons. So far, our record is eight photon entanglement. We are currently working on two parallel lines to get more photon entanglement." Indeed, he says that the team's next goal is to experimentally create "the largest hyper-entangled state so far: a six-photon 18-qubit Schrödinger cat state, entangled in three degrees-of-freedom, polarization, orbital angular momentum, and spatial mode. To do this would provide us with an advanced platform for quantum communication and computation protocols".
The work is published in Nature.


Scientists pinpoint the singularity for quantum computers
Researchers from the University of Bristol have discovered that super-powerful quantum computers, which scientists and engineers across the world are racing to build, need to be even more powerful than previously thought before they can beat today's ordinary PCs.
Quantum computers are a new type of machine that operate on quantum mechanical hardware and are predicted to give enormous speed advantages in solving certain problems.
Research groups at leading universities and companies, including Google, Microsoft and IBM, are part of a worldwide race to realise the first quantum computer that crosses into the 'quantum computational singularity'.
This represents a problem so complex that today's top supercomputer would take centuries to find a solution, while a quantum computer could crack it in minutes.
Now a team of scientists from Bristol have discovered that the boundary to this singularity is further away than previously thought.
The research is reported this week in Nature Physics.
The results apply to a highly influential quantum algorithm known as 'boson sampling', which was devised as a very direct route to demonstrate quantum computing's supremacy over classical machines.
The boson sampling problem is designed to be solved by photons (particles of light) controlled in optical chips – technology pioneered by Bristol's Quantum Engineering and Technology Labs (QETLabs).
Predicting the pattern of many photons emerging from a large optical chip is related to an extremely hard random matrix calculation.
With the rapid progress in quantum technologies, it appeared as though a boson sampling experiment that crossed into the quantum computational singularity was within reach. However, the Bristol team were able to redesign an old classical algorithm to simulate boson sampling, with dramatic consequences.
Dr Anthony Laing, who heads a group in QETLabs and led this research, said: "It's like tuning up an old propeller aeroplane to go faster than an early jet aircraft.
"We're at a moment in history where it is still possible for classical algorithms to outperform the quantum algorithms that we expect to ultimately be supersonic.
"But demonstrating such a feat meant assembling a crack team of scientists, mathematicians, and programmers."
Classical algorithms expert Dr Raphaël Clifford, from Bristol's Department of Computer Science, redesigned several classical algorithms to attack the boson sampling problem, with the 1950's Metropolised Independence Sampling algorithm giving the best performance.
The simulation code was optimised by QETLabs researcher 'EJ', a former LucasArts programmer. Expertise on computational complexity came from Dr Ashley Montanaro, of Bristol's School of Mathematics, while QETLabs students Chris Sparrow and Patrick Birchall worked out the projected performance of the competing quantum photonics technology.
At the heart of the project and bringing all these strands together was QETLabs PhD student and first author on the paper, Alex Neville, who tested, implemented, compared, and analysed, all of the algorithms.
He said: "The largest boson sampling experiment reported so far is for five photons.
"It was believed that 30 or even 20 photons would be enough to demonstrate quantum computational supremacy."
Yet he was able to simulate boson sampling for 20 photons on his own laptop, and increased the simulation size to 30 photons by using departmental servers.
Alex added: "With access to today's most powerful supercomputer, we could simulate boson sampling with 50 photons."
The research builds on Bristol's reputation as a centre of activity for quantum science and the development of quantum technologies.
Through QETLabs, the university has embarked on an ambitious programme to bring quantum technologies out of the laboratory and engineer them in to useful devices that have real-world applications for tackling some of society's toughest problems.
In addition to collaborations with tech companies such as Microsoft, Google, and Nokia, start-ups and new business activities focused on quantum technologies have emerged in Bristol.
An important theme across the overall quantum research activity is developing our understanding of exactly how quantum technologies can provably outperform conventional computers.
Recently Dr Montanaro, together with Professor Noah Linden of the School of Mathematics, organised a Heilbronn Focused Research Group on the topic of quantum computational supremacy.
This meeting brought some of the world leaders in the field, from both industry and academia, to Bristol for a week of intense discussions and collaboration. Among the attendees was one of the theorists who devised boson sampling, Professor Scott Aaronson, from UT Austin.
Although outperforming classical computers might take a little longer than originally hoped, Dr Laing is still optimistic about the prospects for building a device to do just that.
He said: "We now have a solid idea of the technological challenge we must meet to demonstrate that quantum machines can out-compute their classical counterparts. For boson sampling, the singularity lies just beyond 50 photons. It's a tougher nut to crack than we first thought, but we still fancy our chances."
With Dr Laing's group focused on practical applications of quantum technologies, the current work puts bounds on the size and sophistication of photonic devices that will be required to tackle industrially relevant problems that are beyond the capabilities of today's classical algorithms.


In a world where technological advances are constantly on the rise, the word ‘impossible’ does not exist. From virtual reality, cryptocurrencies and quantum computing to flying cars of Dubai Police, things are only starting to get more advanced and innovative. This, in turn, has led to massive progress in countries all over the world, as well as open opportunities for people working in tech.
One of the greatest innovations of the century includes artificial intelligence, commonly referred to as AI. When people hear this term, they usually think of robots wreaking havoc upon humans on earth. They are usually portrayed as evil forces that aim to overthrow the human race, making them a debatable topic in scientific and academic circles. But are any of those portrayals real? Continue reading below for more information.
What is artificial intelligence (AI)?
Back when Alan Turing broke the Nazi cypher device Enigma that helped the Allied Forces win the war, he turned the tides and changed history once again. He wanted to ask and answer the question: Can machines have the human capacity to think?
During the 1950s, he published a seminal paper called Computing Machinery and Intelligence. It is considered to be the first scientific paper that established the foundation, goals and vision of artificial intelligence, opening new doors for further research in the field.
Artificial intelligence (AI) defines the simulation of human intelligence in machines that are automated to mimic the actions and thoughts of human beings. It often revolves around human characteristics that include the capacity to reason, find meaning and learn from previous experiences, among other things.
How does it work?
Even though the term AI has been popular for many years now, a huge chunk of the population that is not tech-savvy still doesn’t know how it works. How did scientists, robotics engineers and software developers manage to make computers act like humans? How is that possible?
Oftentimes, when people discuss the mechanisms of AI, they focus on one component which is machine learning. Before a machine can learn certain algorithms and patterns, AI should have a steady foundation of specialised hardware and software design. Some developers would use programming languages such as Java, R and Python, to name a few.
AI systems work through a combination of sets of data that focuses on analysing patterns and correlations. Through these data patterns, an AI machine can predict future events and execute human-like actions. For instance, when making a chatbot, developers will incorporate examples of text chats. This will make it easier for the system to create meaningful exchanges with people, making them feel like they’re not just talking to a machine.
Pros and cons of artificial intelligence (AI)
There’s no doubt that the invention of AI has contributed lots of progress toward machine learning, revolutionizing various fields like healthcare, autonomous flying and shopping. However, just like any other technological advance, it comes with its advantages and disadvantages. When does AI become harmful and become beneficial to different sectors of society?
If you’re an adult that has a full-time job, working for eight hours straight can put so much strain on your well-being. This is why you have to take breaks that would help you rest your mind and body so you can perform better at work. You also have to take your paid time off, rest on the weekends and prioritize other things outside of work.
This means that human workers won’t be available to provide services 24/7. Humans are not designed that way. However, with AI machines, companies can make them work nonstop without the fear of putting someone’s health at risk.
When companies focus on AI, they would increase their productivity rate, generate larger revenues and lessen costs in hiring new employees.
Takes risks that humans are not capable of
There are still undiscovered parts of the world that humans haven’t reached yet, especially the deepest parts of oceans. When basic ethics are applied, you can’t just send someone on an exploration quest and put them in grave danger. This also means no matter how trained an individual is, forcing them to defuse bombs during disasters or mine for coal and oil is still considered a risky business.
Thanks to artificial intelligence, all of these things are possible. Let’s take a look at the 1986 Chernobyl nuclear power plant explosion that happened in Ukraine. During that point in history, there were no AI-powered robots that can be used to lessen the effects of radiation and put the fire under control. Consequently, the humans who took the risk to get close and address the situation at hand died within minutes.
What does this mean? If AI was used during this deadly, hazardous situation, hundreds of lives could have been saved.
Handles repetitive jobs well
There are a lot of repetitive jobs in the market that can cause increased burnout, reduced creativity, less employee engagement and costly labour costs, among other things. This includes bookkeeping, telemarketing, proofreading and research analytics. These jobs, according to experts, will be replaced by automation and computerization, helping companies better analyse customer behaviour and data.
For instance, when it comes to market research, marketers and advertisers would do a great job in creating meaningful content, products and messaging. However, through AI and automated surveys, marketing companies can compile huge sets of data in one go. An example would be GrowthBot, conducting market research with just one click of a Slack command.
It can also lessen lengthy bank processes. When you visit banks, you have to undergo several document verifications when applying for a loan. But through AI Cognitive Automation, banks can speed up transactions in under a minute, increasing customer satisfaction and overall productivity.
Makes the right decisions
Since there is a complete absence of emotions in AI-powered systems, they can make the right decisions in a short period. It only works with its programmed data and previous automated tasks, helping it settle on a decision that’s not bound by emotions or practicality. This makes it the ideal choice for many industries, especially healthcare.
In Cambridge, Massachusetts, PathAI is starting to develop a machine learning system that would help pathologists diagnose illnesses more accurately. To further expand its goals, the company has collaborated with drug developers and organizations such as the Bill & Melinda Gates Foundation and Bristol-Myers Squibb.
AI is also used in diagnosing deadly blood diseases in Boston, Massachusetts. Beth Israel Deaconess Medical Center, a teaching hospital of Harvard Medical School, is using AI-enhanced microscopes to look for harmful bacteria such as staphylococcus and E. coli. Doctors are now studying blood samples faster than they did when they had to depend on manual scanning. The machines have 95% accuracy after scientists fed them 25,000 photos of blood samples.
Lack of creativity
Yes, you can teach machines how to think, talk like humans and study with big sets of data within seconds, but you can never teach them creativity. Keep in mind that they can only execute the commands and data integrated into them, so when it comes to being creative, they can’t compete with the human brain.
Humans are known for their intellect and emotions. They know how to push the limit, think out of the box and make things that machines cannot do. Their thoughts completely depend on their feelings and comprehension that AI-powered systems can never replicate.
Perhaps the most dangerous effect of AI development is increased unemployment. Since more companies are aiming to generate sales and reduced costs, they are prompted to replace human workers with AI and automation. They argue that AI robots can perform similar tasks with better efficiency and accuracy, so people looking for jobs that may be repetitive might have little to no chance of getting hired.
While this can be a sign of huge progress for many companies, it can also mean that workers won’t have many job opportunities to seek.
Unable to incorporate ethics
One of the reasons why AI is still debatable despite its great potential is because of its inability to incorporate ethics in certain situations. Remember that AI only has algorithms and data that it can use to make decisions and follow patterns, so it only focuses on logical results.
This might lead to discriminatory conclusions and inserted bias. The complete reliance on AI-powered systems may lead to inaccuracies that can put someone’s life on the line. For instance, there is a software program that shows bias when identifying future criminals. It showed bias against black people, incarcerating innocent individuals just because of the colour of their skin.
If you want to know more about AI technology and other related topics about it, visit Vula Telematix and find articles that will help you understand how they work.


Chenoa van den Boogaard, Physics & Astronomy editor
Teleportation has finally become a reality. But before you get too excited, the type of teleportation scientists are experimenting with is not the same as what you’ve seen on Star Trek. Scientists are not trying to teleport people or objects from one place to another. Instead, they are teleporting information in the quantum world, where things simply don’t behave the same way as they do in the world we can see.
Quantum teleportation has the potential to revolutionize current technology, especially in the areas of communication and computing. “Teleportation will likely be a key element of the quantum internet, when it comes to fruition,” explains John Nichol, an assistant professor of physics at the University of Rochester. Imagine a world where information could be sent and received instantaneously through a quantum internet and computers could store and calculate information at a substantially higher speed than is currently possible. But before we explore what quantum teleportation can do, let’s take a look at how it actually works.
To understand quantum teleportation, we must first look at quantum entanglement, a process so strange that Albert Einstein famously described it as “spooky action at a distance.” Quantum entanglement occurs when a pair or group of particles are generated in such a way that their behaviour can no longer be described as independent of each other, even when the particles are separated by great distances. A particle’s behaviour is described by its quantum state, which defines all the possible outcomes of a measurement on that particle. When particles exist in quantum entanglement, their quantum states are always correlated, meaning a measurement on one particle will allow us to know something about the state of the other. For example, imagine two entangled particles as a pair of gloves. If you were to mail each glove in separate boxes to two different locations, by opening only one box, you could determine whether the left or right glove was in the other box.
How particles are connected and communicate within a system of quantum entanglement is still a mystery, but scientists have succeeded in using the phenomenon to their advantage. In 1993, a group of physicists from Canada, the United States, France, and Israel collaborated to transfer, or teleport, the quantum state of an independent photon across two entangled photons. The process can be explained by considering a scenario in which there are two observers, Alice and Bob, who each possess one of a pair of entangled photons. The entangled photons have quantum states of either up or down. Because they are correlated, when measured, one photon will be in an up state and the other in a down state because entangled particles can never be in the same state (in this case, both up or down).
Alice then introduces a third independent photon to the system (the yellow photon in the figure below), which also has a quantum state of either up or down and forces it to interact with her entangled photon. If the state of the yellow photon is up, it will change the state of Alice’s entangled photon to down, which results in Bob’s photon’s state becoming up due to the correlation with its entangled partner. This process effectively teleports the state of the yellow photon to Bob’s photon, which also results in the annihilation of Alice’s entangled photon and the yellow photon.
Importantly, information telling Bob that his entangled photon is now an identical copy of the yellow photon must reach him from Alice. If he is able to determine instantaneously that his photon has changed, this means that the information has travelled to him faster than the speed of light, which is impossible within our current understanding of physics.
The yellow photon has not really been teleported in the sense that it has physically moved. Instead, Bob’s photon has taken on the quantum state of the yellow photon, forming an exact copy. The yellow photon is annihilated because an original particle and its copy cannot exist simultaneously, as outlined by the no-cloning theorem in physics. This means that if Star Trek were using this type of teleportation, Captain Kirk would be annihilated and an identical copy of him would be formed at his destination every time Scotty beamed him up.
Since 1993, several experiments have successfully demonstrated teleportation in various other materials including atoms, ions, and superconducting circuits. In 2017, a group of Chinese physicists achieved space-based teleportation by teleporting information from Earth to the Micius satellite, a record distance of 1,400 kilometers.
Scientists are continuing to push quantum teleportation into space, with plans to integrate teleportation into the design of space-based telescopes and satellites. “There are already efforts underway in different countries to create quantum networks in space,” explains Nichol. “Teleportation is often a key element of benchmarking these satellite-based systems [and] can also be used to create remotely entangled pairs of particles for secure communication protocols.”
In June of this year, scientists from the University of Rochester and Purdue University confirmed that quantum teleportation is possible between electrons, a discovery that has major implications for the world of quantum computing. The researchers, including John Nichol, explained their findings in an article published in Nature Communications.
In current computing methods, billions of transistors called bits, transfer information through a single binary value 0 or 1. By comparison, quantum bits (or qubits) have the ability to exist as both 0 and 1 simultaneously. “Electrons are desirable qubits because they can be manipulated quickly and their coherence times (the length of time over which they can retain quantum information) can be extremely long,” says Nichol. “Compared with photons, electrons also interact easily with each other, which is a key requirement for quantum computing.”
Quantum teleportation has the potential to revolutionize the way we obtain and pass on information, whether it is in areas of communication, computing, healthcare, economics, or other industries. One day, we may even succeed in teleporting matter. But for now, that kind of spooky action is still in the distant future.
Banner image by Matthias Weinberger, CC BY-NC-ND 2.0


16 September 2011—The long-promised arrival of practical quantum computers—machines that exploit the laws of quantum mechanics to solve complex problems much faster than conventional computers do—seems a step closer, thanks to two recent advances by physicists.
In the first development, reported in the 2 September issue ofNature by a group led by Serge Haroche of the École Normale Supérieure and the Collège de France in Paris, the researchers created a real-time feedback mechanism for a quantum computer. Control mechanisms, such as feedback loops, are central to the operation of large conventional computers.
In the second advance, reported the same week inScience by a group led by Matteo Mariantoni and John Martinis of the University of California, Santa Barbara, scientists created a quantum central processing unit (CPU) with memory. The rudimentary device is the first quantum computer based on the common von Neumann processor-memory architecture that conventional computers use.
Dick Slusher, director of the Quantum Institute at the Georgia Institute of Technology, in Atlanta, and other experts unanimously praised the work of both groups. However, Slusher says that ”for quantum computing to be fault tolerant—a condition required to scale up to true applications like factoring useful coding keys—the error levels must be much lower than achieved so far.”
Quantum computing is an emerging field that has witnessed considerable advances in recent years, including progress toward silicon devices. However, it has proved difficult to create a practical quantum computer that would rival the processing abilities of a conventional machine. Part of the difficulty lies in the fragility of quantum states, which break down (or ”decohere,” in the parlance of quantum mechanics) rather quickly. So far, only rudimentary quantum computers with a handful of ”qubits” (quantum bits) have been built. (In May, D-Wave Systems sold Lockheed Martin a special type of computer that relies on a ”quantum annealing” processor, but many quantum computing experts remain skeptical that it is a true quantum computer.)
As they seek to create larger quantum systems, scientists have tried to incorporate some of the same systems-engineering concepts that are used in conventional computers, but the equivalent quantum systems have proved elusive—until now. ”These machines are very fragile,” says Haroche. ”The coupling to their environment causes decoherence, which destroys the quantum features required to achieve their tasks. Correcting the effects of decoherence is thus a very important aspect of quantum information. One possibility is to control the quantum machine by quantum feedback.”
Yet therein lies a challenge: In the quantum world, the mere act of observing photons or atoms perturbs their motion and changes their positions and velocities—and therefore the value the qubit holds. So for quantum feedback to work, one must be able to observe the system by performing ”weak measurements,” perturbing it only minimally, and the computer must take the perturbation into account before applying the correction.
Haroche and his colleagues use a small collection of atoms as a kind of quantum sensor to overcome this challenge. They pass atoms through a microwave cavity that contains the qubits as photons. The atoms obtain a detectable signal—a shift in their phase. This technique provides information about the state of the photons, but it does so by performing only a weak measurement and does not lead to a total collapse of the light’s quantum nature. Measuring changes in the final state of atoms that sequentially pass through the light field provides a signal that can be used to control the light.
”The work is a very impressive demonstration experiment showing that the many techniques developed in the systems engineering community can be translated to the quantum regime—if one is clever enough,” says Michael Biercuk, a quantum physicist at the University of Sydney, in Australia.
The challenge of translating a classical system, in this case the common von Neumann processor-memory architecture, into a quantum system also motivated the second team of researchers. To build a quantum CPU and RAM, the UC Santa Barbara group used two superconducting Josephson junctions—two pieces of superconducting metal separated by a thin insulating layer—as qubits. They connected the qubits using a bus made of a superconducting microwave resonator. Each qubit also had a separate resonator that acted as RAM. With the help of microwave pulses, the qubits could influence one another’s state in a way that performed calculations, and the results could be stored in the quantum RAM. They tested their CPU by allowing it to solve a few quantum algorithms, including the equivalent of the Fourier transform. The demonstration could quickly lead to a larger-scale quantum processor based on superconducting circuits, according to the UC Santa Barbara team.
The most complex algorithms performed so far have used a quantum computing system based on trapped ions, but Biercuk says the superconducting system is quickly catching up, and that’s ”extremely exciting.”
While no one expects a quantum computer to rival a conventional computer in the very near future, experts were pleased with these recent developments.
Raymond Laflamme, executive director of the Institute for Quantum Computing at the University of Waterloo, in Canada, said both experiments had ”very strong results,” and that they ”demonstrate an increasing amount of control of quantum processors.”
About the Author
Saswato R. Das, a New York City–based writer, contributes frequently to IEEE Spectrum. For one assignment, Das got the last interview with famed science fiction writer Arthur C. Clarke before he died in 2008.


A new phase of matter has been discovered in a quantum computer after physicists beamed light at its qubits in a pattern inspired by the Fibonacci sequence.
If you think it’s mind-boggling, this strange quirk of quantum mechanics behaves as if it has two time dimensions instead of one; this trait, the scientists say, makes the qubits more robust, able to remain stable throughout an experiment.
This stability is called quantum coherence, and it’s one of the main goals of an infallible quantum computer—and one of the hardest to achieve.
The work is “a completely different way of thinking about the phases of matter,” according to computational physicist Philippe Dumitrescu of the Flatiron Institute, lead author of a new paper describing the phenomenon.
“I have been working on these theoretical ideas for more than five years, and it is very interesting to see how they are actually implemented in experiments.”
Quantum computing is based on qubits, the quantum equivalent of computing bits. However, when bits process information in one of two states, 1 or 0, qubits can be in both at the same time, a state known as quantum superposition.
The mathematical nature of this superposition can be incredibly powerful from a computational standpoint, being able to solve problems quickly under the right circumstances.
But the fuzzy, undefined nature of a series of qubits also depends on how their undefined states relate to each other, a relationship called entanglement.
Unfortunately, qubits can get entangled with just about anything in their environment, introducing errors. The more delicate the blurred state of a qubit (or the more chaos in its environment), the higher the risk of it losing this coherence.
Improving consistency to the point of viability is likely a multitactic approach to removing a major hurdle standing in the way of a functional quantum computer—every little thing counts.
“Even if you keep all the atoms under tight control, they can lose their quantumness by talking to the environment, heating up or interacting with things differently than you planned,” Dumitrescu explained.
“In practice, experimental devices have many sources of errors that can degrade coherence after just a few laser pulses.”
Ensuring symmetry can be one way to protect qubits from decoherence. Rotate the good old square ninety degrees and it will still have the same shape. This symmetry protects it from some rotational effects.
Exposing the qubits to uniformly distributed laser pulses guarantees a symmetry based not on space, but on time. Dumitrescu and his colleagues wanted to see if they could enhance this effect by adding not symmetric periodicity, but asymmetric quasi-periodicity.
They suggested that this would add not one temporal symmetry, but two; one is effectively hidden inside the other.
The idea was based on earlier work by the group, which proposed creating something called a quasi-crystal in time rather than space. If a crystal consists of a symmetrical lattice of atoms that repeats in space, like a square grid in a gym or a honeycomb, then the pattern of atoms on a quasicrystal is non-repeating, like a Penrose tiling, but still ordered.
The team ran their experiment on an advanced commercial quantum computer developed by Quantinuum, a quantum computing company. This beast uses 10 ytterbium atoms (one of the preferred elements for atomic clocks) for its qubits. These atoms are held in an electrical ion trap from which laser pulses can be used to control or measure them.
Dumitrescu and his colleagues created a sequence of laser pulses based on Fibonacci numbers, where each segment is the sum of the previous two segments. The result is a sequence that is ordered but not repeated, as in a quasicrystal.
Quasicrystals can be mathematically described as low-dimensional segments of multidimensional lattices. A Penrose tiling can be described as a two-dimensional slice of a five-dimensional hypercube.
Similarly, the team’s laser pulses can be described as a one-dimensional representation of a two-dimensional pattern. Theoretically, this meant that he could potentially impose two temporal symmetries on qubits.
The team tested their work by flashing lasers on an array of ytterbium qubits, first in a symmetrical sequence and then quasi-periodically. They then measured the coherence of the two qubits at both ends of the trap.
For a periodic sequence, the qubits were stable for 1.5 seconds. For a quasi-periodic sequence, they remained stable for 5.5 seconds, the duration of the experiment.
The extra temporal symmetry added another layer of protection against quantum decoherence, the researchers say.
“With this quasi-periodic sequence, a complex evolution occurs that eliminates all the errors living on the edge,” said Dumitrescu.
“Because of this, the edge remains quantum mechanically coherent for much, much longer than you would expect.”
The work is not yet ready to be integrated into functional quantum computers, but it represents an important step towards that goal, the researchers said.
The study was published in Nature.
#strange #phase #matter #span #time #dimensions


Quantum computers may one day rapidly find solutions to problems no regular computer might ever hope to solve, but there are vanishingly few quantum programmers when compared with the number of conventional programmers in the world. Now a new beginner’s guide aims to walk would-be quantum programmers through the implementation of quantum algorithms over the cloud on IBM’s publicly available quantum computers.
Whereas classical computers switch transistors either on or off to symbolize data as ones or zeroes, quantum computers use quantum bits, or “qubits,” which because of the peculiar nature of quantum physics can exist in a state called superposition where they are both 1 and 0 at the same time. This essentially lets each qubit perform two calculations at once. The more qubits are quantum-mechanically linked, or entangled (see our explainer), within a quantum computer, the greater its computational power can grow, in an exponential fashion.
Currently quantum computers are noisy intermediate-scale quantum (NISQ) platforms, meaning their qubits number up to a few hundred at most and are error-ridden as well. Still, quantum processors are widely expected to grow in terms of qubit count and quality, with the aim of achieving a quantum advantage that enables them to find the answers to problems no classical computers could ever solve.
Although the field of quantum programming started in the 1990s, it has to date drawn only a small community. “Programming quantum computers may seem like a great challenge, requiring years of training in quantum mechanics and related disciplines,” says the guide’s senior author, Andrey Lokhov, a theoretical physicist at Los Alamos National Laboratory, in New Mexico. “Additionally, the field is dominated by physics and algebraic notations that at times present unnecessary entry barriers for mainstream computer and mathematically trained scientists.”
Now, with their new guide, Lokhov and his colleagues hope to help pave the way “for the upcoming quantum-computing revolution,” he says. “We believe that our guide fills a missing space in the field of quantum computation, introducing nonexpert computer scientists, physicists, and engineers to quantum algorithms and their implementations on real-world quantum computers.”
The new guide explains the basics of quantum computing and quantum programming, including quantum algorithms.
“Very much like how classical algorithms describe a sequence of instructions that need to be executed on a classical computer, a quantum algorithm represents a step-by-step procedure, where each of the steps needs to be performed on a quantum computer,” Lokhov says. “However, the term ‘quantum algorithm’ is usually reserved for algorithms that contain inherently quantum operations, such as quantum superposition or quantum entanglement, which turn out to be computationally powerful.”
“We believe that our guide fills a missing space in the field of quantum computation, introducing nonexpert computer scientists, physicists, and engineers to quantum algorithms and their implementations on real-world quantum computers.” —Andrey Lokhov
To implement such quantum operations on quantum computers, quantum programs are represented as circuits describing a sequence of elementary operations, called gates, that are applied on a set of qubits. One major difference between quantum and classical programming lies in a central principle of quantum mechanics—when it comes to measuring a quantum program’s results, the process is inherently probabilistic, or subject to random variation.
“Our guide aims to explain the basic principles of quantum programming, which are quite different from classical programming, with straightforward algebra that makes understanding the underlying fascinating quantum-mechanical principles optional,” Lokhov says. “We have received positive feedback from many scientists—beginners in the field—who were able to quickly familiarize themselves with the basics of quantum programming using our guide.”
The new guide provides the minimal knowledge needed to start implementing and running quantum algorithms right away. These include 20 standard quantum algorithms, including Shor’s algorithm for factoring integers and Grover’s algorithm for database searching.
“In addition, our review covers the most successful hybrid quantum-classical algorithms, such as the quantum approximate optimization algorithm, as well as classical tools that are useful for certifying the performance of quantum algorithms, such as quantum tomography,” Lokhov says. “Hence, the guide surveys a combination of quantum, classical, and hybrid algorithms that are foundational for the field of quantum computing.”
The guide then walks quantum programmers through implementing these algorithms over the cloud on IBM’s publicly available quantum computers, such as its 5-qubit IBMQX4. The guide discusses the results of the implementation and explains differences between the simulator and the actual hardware runs.
Lokhov notes that currently, in order to show that a new quantum algorithm works efficiently, one needs to give a mathematical proof. In contrast, in classical computing, many efficient algorithms were discovered heuristically—that is, by trial and error, or by loosely defined rules—with theoretical guarantees coming much later. The hope is that new quantum algorithms may get discovered in a similar fashion the more quantum programmers there are.
“We believe that our guide could be useful for introducing more scientists to quantum computing and for inviting them to experiment with the forthcoming quantum computers with larger numbers of qubits,” Lokhov says.
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Scientists have now developed a universal quantum gate, which could become the key component in a quantum computer.
Light particles completely ignore each other. In order that these particles can nevertheless switch each other when processing quantum information, researchers at the Max Planck Institute of Quantum Optics in Garching have now developed a universal quantum gate. Quantum gates are essential elements of a quantum computer. Switching them with photons, i.e. light particles, would have practical advantages over operating them with other carriers of quantum information.
The light-saber fights of the Jedi and Sith in the Star Wars saga may well suggest something different, but light beams do not notice each other. No matter how high their intensity, they cut through each other without hindrance. When individual light particles meet, as is necessary for some applications of quantum information technology, nothing at all happens. Photons can therefore not switch each other just like that, as would have to be the case if one wanted to use them to operate a quantum gate, the elementary computing unit of a quantum computer.
A quantum computer can master some tasks, such as searching through databases, much faster than conventional computers. Physicists have already developed quantum gates for the super-computers of the future, for example by using nitrogen atoms contained in diamonds as impurities as the smallest computing unit. But “to have a quantum computer compute with photons would have practical advantages,” says Stephan Ritter, who leads a Research Group in Gerhard Rempe’s Division at the Max Planck Institute of Quantum Optics. “This is because quantum information has to be in the form of photons in order to be transmitted over large distances. If we can use photons to process it as well, we do not have to transfer it to other carriers, such as atoms, in order to compute with it.”
An atom in a resonator mediates between light particles
In order for photons to sense each other’s presence in the first place, let alone switch each other, they need mediators. In the experiments being conducted by Stephan Ritter’s team of physicists, this mediating role is taken on by a single atom in a resonator. The resonator consists of two mirrors 0.5 mm apart. The Garching-based researchers use a laser beam to trap the atom in the resonator.
For their experiments, the scientists now need two photons each carrying one qubit. A qubit is the quantum mechanical equivalent of the bit of a conventional computer. It can, however, not only encode the zero and the one, but assume all possible states in between as well. The researchers write the states of the two qubits into the polarization of the two light particles, i.e. into the direction of oscillation of the electromagnetic waves.
The Max Planck physicists send the two photons, one shortly after the other, onto the system of atom and resonator. The first photon thereby transfers information to the atom by changing its state – but only if the photon has the right polarization. This change then has an effect on the polarization of the second photon when it impinges onto the system of atom and resonator a short time later.
The quantum gate operates in a deterministic way
“Our system only becomes a universal quantum gate because the second photon can also transfer information onto the first photon, however,” says Bastian Hacker, who conducted the experiments as part of his doctoral thesis. To this end, the scientists initially store the two photons in an optical fiber more than one kilometer in length after the light particles have been reflected at the resonator. At the same time, they conduct a measurement on the atom, which can also affect the polarization state of the two photons due to the surprising properties of quantum mechanics. As is the case with a conventional bit, there are only two possible measurement results. They provide the researchers with reliable information about which rotation of the polarization of the first photon they can use to complete the gate operation.
“Our quantum gate operates in a deterministic way,” says Stephan Ritter. This means that the scientists can reliably predict which changes the light particles should experience in the quantum gate depending on the original polarization of the photons fed in. In addition, the gate carries out these operations on all photons which impinge on the resonator with the trapped atom – at least in principle. In reality, unavoidable technical shortcomings decrease the efficiency of the quantum gate as well as the precision of its operations. However, the researchers already have some ideas about how they can improve the two characteristics of the quantum gate: by using mirrors with lower losses, for example, or a storage device for the photons which is more efficient than an optical fibre. In other implementations of quantum gates between photons with which physicists have already experimented, the errors are inherent, however, because chance always plays a role here.
Two experiments demonstrate how reliable the quantum gate is
The Garching-based researchers have conducted two experiments to demonstrate how reliably their quantum gate already operates. Which operations the quantum gate executes here depends only on how the two input photons are polarized.
In one experiment, the researchers circularly polarize the first photon so that its direction of oscillation rotates either clockwise or counter-clockwise. The second photon is linearly polarized, i.e. so that it oscillates in a horizontal or vertical plane. On a photon pair with these input states, the quantum gate acts like a CNOT operation, where the first qubit controls the second one. This is because, depending on the direction in which the first photon rotates, the quantum gate flips the polarization of the second photon – from the vertical to the horizontal plane, for example – or not. CNOT gates are essential for a quantum computer, because they can be used to execute all logic operations.
For the second experiment, the researchers in Garching polarize both photons linearly. Fed with such input states, the quantum gate entangles the two photons. Entangled photons can no longer be described independently of each other, but only with a common state – no matter how great the distance between the two light particles. As much as entanglement puts our imagination to the test, for the quantum computer it is an indispensable ingredient like the CNOT gate. “Only the entanglement of qubits allows the strength of the quantum computer to be unfolded,” says Stephan Welte, who also contributed crucial work to the experiments as part of his doctoral thesis.
The atom in the resonator as the key element of a quantum computer
“With the quantum gate, we now have a key element for an optical quantum computer,” says Gerhard Rempe, Director at the Max Planck Institute in Garching. It will be a while before such a quantum computer completes some computing tasks at a speed which will outclass any conventional computer, however; not least because this requires the quantum gate to compute more reliably. Nevertheless, Gerhard Rempe already has definite ideas about how such a super-computer could be operated with an atom in the resonator. This would not require many of these systems, each of which can quite easily fill a laboratory. “The logic operations can be carried out one after the other with a single atom in a resonator,” says Gerhard Rempe.
The European Commission obviously also believes that these quantum technology concepts have a future. It plans to invest one billion euros into their development over a period of approx. ten years. This funding could also speed up the process of realizing the superfast quantum computer – which is also what Stephan Ritter and his colleagues in Garching are hoping.
Publication: Bastian Hacker, et al., “A photon-photon quantum gate based on a single atom in an optical resonator,” Nature (2016) doi:10.1038/nature18592
PDF copy of the Study: A photon-photon quantum gate based on a single atom in an optical resonator


Achieving the immense promise of quantum computing requires new developments at every level, including the computing hardware itself. A Lawrence Berkeley National Laboratory (Berkeley Lab)-led international team of researchers has discovered a way to use ion beams to create long strings of “color center” qubits in diamond. Their work is detailed in the journal Applied Physics Letters.
Creating large numbers of high-quality quantum bits (qubits), in close enough proximity for coupling to each other, is one of the great challenges of quantum computing. Collaborating with colleagues worldwide, the team has been exploring the use of ion beams to create artificial color centers in diamond for use as qubits.
Color centers are microscopic defects – departures from the rigorous lattice structure of a crystal, such as diamond. The type of defect that is of specific interest for qubits is a nitrogen atom next to a vacancy, or empty space, in a diamond lattice. (Nitrogen is commonly found in the crystal lattice of diamond, which is primarily a crystalline form of carbon, and can contribute to the color of the stone.)
When excited by the rapid energy deposition of a passing ion, nitrogen-vacancy centers can form in the diamond lattice. The electron and nuclear spins of nitrogen-vacancy centers and the adjacent carbon atoms can all function as solid-state qubits, and the crystal lattice can help protect their coherence and mutual entanglement.
The result is a physically durable system that does not have to be used in a cryogenic environment, which are attractive attributes for quantum sensors and also for qubits in this type of solid-state quantum computer. However, making enough qubits, and making them close enough to each other, has been a challenge.
When swift (high-energy) heavy ions such as the beams this team used – gold ions with a kinetic energy of about one billion electron volts – pass through a material, such as nitrogen-doped diamond, they leave a trail of nitrogen-vacancy centers along their tracks. Color centers were found to form directly, without need for further annealing (heat treatment). What’s more, they formed all along the ion tracks, rather than only at the end of the ion range as had been expected from earlier studies with lower-energy ions. In these straight “percolation chains,” color-center qubits are aligned over distances of tens of microns, and are just a few nanometers from their nearest neighbors. A technique developed by Berkeley Lab’s Molecular Foundry measured color centers with depth resolution.
The work on qubit synthesis far from equilibrium was supported by the Department of Energy’s Office of Science. The next step in the research will be to physically cut out a group of these color centers – which are like a series of beads on a string – and show that they are indeed so closely coupled that they can be used as quantum registers.
Results published in the current article show that it will be possible to form quantum registers with up to about 10,000 coupled qubits – two orders of magnitude greater than achieved thus far with the complementary technology of ion-trap qubits – over a distance of about 50 microns (about the width of a human hair).
“Interactions of swift heavy ions with materials have been studied for decades for a variety of purposes, including the behavior of nuclear materials and the effects of cosmic rays on electronics,” said Schenkel.
He added that researchers worldwide have sought to make quantum materials by artificially inducing color centers in diamond. “The solid-state approaches to quantum computing hardware scale beautifully, but integration has been a challenge. This is the first time that direct formation of color-center qubits along strings has been observed.”
The stars, like diamonds
On a miniscule and ephemeral scale (nanometers and picoseconds) the deposition of energy by the ion beams produces a state of high temperature, which Schenkel likens to the surface of the sun, in the 5000 K range, and pressure. Besides knocking carbon atoms out of the crystal lattice of diamond, this effect could enable fundamental studies of exotic states of transient warm dense matter, a state of matter that is present in many stars and large planets and which is difficult to study directly on Earth.
It might also enable formation of novel qubits with tailored properties that cannot be formed with conventional methods. “This opens a new direction for expanding our ability to form quantum registers,” said Schenkel.
Currently, color-center strings are formed with beams from large particle accelerators, such as the one at the German laboratory GSI that was used in this research. In the future, they might be made using compact laser-plasma accelerators like the ones being developed at the Berkeley Lab Laser Accelerator (BELLA) Center.
The BELLA Center is actively developing its ion-acceleration capabilities with funding by the DOE Office of Science. These capabilities will be used as part of LaserNetUS. Ion pulses from laser-plasma acceleration are very intense and greatly expand our ability to form transient states of highly excited and hot materials for qubit synthesis under novel conditions.
More facets in materials science far from equilibrium
The process of creating these color centers is interesting in its own right and has to be better understood as part of further progress in these applications. The details of how an intense ion beam deposits energy as it traverses the diamond samples, and the exact mechanism by which this leads to color-center formation, hold exciting prospects for further research.
“This work demonstrates both the discovery science opportunities and the potential for societally transformative innovations enabled by the beams from accelerators,” says ATAP Division Director Cameron Geddes. “With accelerators, we create unique states of matter and new capabilities that are not possible by other means.”
The authors includes several scientists from Berkeley Lab: Arun Persaud, who led the study, and Thomas Schenkel, head of the Accelerator Technology and Applied Physics (ATAP) Division’s Fusion Science & Ion Beam Technology Program, as well as Casey Christian (now with Berkeley Lab’s Physics Division), Edward Barnard of Berkeley Lab’s Molecular Foundry, and ATAP affiliate Russell E. Lake.
For information about licensing or collaboration, contact Berkeley Lab’s Intellectual Property Office at firstname.lastname@example.org.


May 9, 2020
IST Austria scientists demonstrate quantum radar prototype
New detection technique based on quantum technology developed at IST Austria – Study published in Science Advances
Physicists at the Institute of Science and Technology Austria (IST Austria) have invented a new radar prototype that utilizes quantum entanglement as a method of object detection. This successful integration of quantum mechanics into our everyday devices could significantly impact the biomedical and security industries. The research is published in the journal Science Advances.
Quantum entanglement is a physical phenomenon where two particles remain inter-connected, sharing physical traits regardless of how far apart they are from one another. Now, scientists from the research group of Professor Johannes Fink at the Institute of Science and Technology Austria (IST Austria) along with collaborators Stefano Pirandola from the Massachusetts Institute of Technology (MIT) and the University of York, UK, and David Vitali from the University of Camerino, Italy — have demonstrated a new type of detection technology called ‘microwave quantum illumination’ that utilizes entangled microwave photons as a method of detection. The prototype, which is also known as a ‘quantum radar’, is able to detect objects in noisy thermal environments where classical radar systems often fail. The technology has potential applications for ultra-low power biomedical imaging and security scanners.
Using quantum entanglement as a new form of detection
The working principles behind the device are simple: Instead of using conventional microwaves, the researchers entangle two groups of photons, which are called the ‘signal’ and ‘idler’ photons. The ‘signal’ photons are sent out towards the object of interest, whilst the ‘idler’ photons are measured in relative isolation, free from interference and noise. When the signal photons are reflected back, true entanglement between the signal and idler photons is lost, but a small amount of correlation survives, creating a signature or pattern that describes the existence or the absence of the target object—irrespective of the noise within the environment.
“What we have demonstrated is a proof of concept for Microwave Quantum Radar,” says lead author and at the time of the research project postdoc in the Fink group Shabir Barzanjeh, whose previous research helped advance the theoretical notion behind quantum enhanced radar technology. “Using entanglement generated at a few thousandths of a degree above absolute zero (-273.14 °C), we have been able to detect low reflectivity objects at room-temperature.”
Quantum technology can outperform classical low-power radar
While quantum entanglement in itself is fragile in nature, the device has a few advantages over conventional classical radars. For instance, at low power levels, conventional radar systems typically suffer from poor sensitivity as they have trouble distinguishing the radiation reflected by the object from naturally occurring background radiation noise. Quantum illumination offers a solution to this problem as the similarities between the ‘signal’ and ‘idler’ photons — generated by quantum entanglement — makes it more effective to distinguish the signal photons (received from the object of interest) from the noise generated within the environment. Barzanjeh who is now an Assistant Professor at the University of Calgary on the prototype’s performance: “The main message behind our research is that ‘quantum radar’ or ‘quantum microwave illumination’ is not only possible in theory but also in practice. When benchmarked against classical low-power detectors in the same conditions we already see, at very low-signal photon numbers, that quantum-enhanced detection can be superior.”
Prominent milestone towards advancing 80 year-old radar technology
Throughout history, basic science has been one of the key drivers of innovation, paradigm shift and technological breakthrough. Whilst still a proof of concept, the group’s research has effectively demonstrated a new method of detection that, in some cases, may already be superior to classical radar.
“Throughout history, proof of concepts such as the one we have demonstrated here have often served as prominent milestones towards future technological advancements. It will be interesting to see the future implications of this research, particularly for short-range microwave sensors.” says Barzanjeh.
Last author and group leader Professor Johannes Fink adds “This scientific result was only possible by bringing together theoretical and experimental physicists that are driven by the curiosity of how quantum mechanics can help to push the fundamental limits of sensing. But to show an advantage in practical situations we will also need the help of experienced electrical engineers and there still remains a lot of work to be done in order to make our result applicable to real-world detection tasks.”
S. Barzanjeh, S. Pirandola, D. Vitali & J. M. Fink. 2019. Science Advances. DOI: 10.1126/sciadv.abb0451
This IST Austria part of the project was supported by funding from the European Union (ERC Starting Grant QUNNECT, no. 758053), the EU’s Horizon 2020 research and innovation programme under grant agreement number 862644 (FET Open QUARTET), and IST Austria.


What is Quantum Computing?
Quantum Computing concentrates on the development of computer technology following the quantum theory principle.
The classical computers that we use in the present times use a bit to process information. Bit uses either 1 or 0 to decode a piece of information. However, in quantum computing, the computer uses quantum bits or qubits to process data. It uses the unique ability of subatomic particles which enable them to exist in more than one state. Quantum and classical both try to solve problems, but the approach followed by each is different.
Some of the major players engaged in quantum computing include Accenture, Alibaba Group, Amazon Bracket, AT&T, Atos Quantum, Baidu, Google Quantum AI Lab, IBM, Intel, and Microsoft.
What makes Quantum Computers unique?
Quantum Computing can be explained via two vital quantum physics features, ‘Superposition’ and ‘Entanglement’, that form the basis of supercomputers. These two features enable these supercomputers to operate at an exponentially higher speed as compared to conventional computers with less energy consumption.
Superposition refers to the counterintuitive ability of quantum objects, like an electron that can exist in multiple states at the same time.
Entanglement refers to the phenomenon quantum entities are created or manipulated in a way that none of them can be described without referencing others.
Interesting Facts About Quantum Computing:
- Quantum Computing is considered more efficient than modern computing because it uses quantum tunnelling, which supports in reducing the power up to a thousand times.
- IBM’s Deep Blue computer was successful in defeating chess champion Garry Kasparov as this computer was capable of calculating 200 million potential moves every second. Quantum computing is even more efficient than IBM’s Deep Blue computer as it can calculate one trillion per second.
- Quantum computers require cold temperature for accurate functioning.
- The increased speed of quantum computing would speed up the learning speed of Artificial Intelligence.
Why is Quantum Computing becoming so important? Why do we need it?
In 2017 IP Expo, Professor Brian Cox said that quantum computers have a huge capacity to find answers related to life, the universe, and encryption. He stated that quantum computing could be considered as a massive stack of possibilities and sets of data.
The objective to develop quantum computing was to execute Shor’s algorithm of large numbers. This led to the prime driver towards the field of quantum computing.
Shor’s algorithm is a well-known algorithm for factoring prime numbers on a classical (non-quantum) computer that needs an amount of time that is basically exponential with the size of N. Here N is any number, let say 25. If we enter N=25, then the quantum computer returns the factors of 25.
To develop a broader view of quantum computers, one must understand that quantum computing delivers incredible speedups for specific problems. On that front, the researchers are working understand the type of problem suitable for quantum speed-ups and accordingly develop the algorithms to resolve them.
In simple terms, quantum computing is believed to solve problems related to optimisation, which plays a crucial role in every field from defence to financial trading.
What are the different types of Quantum Computing?
There are three types of quantum computing (as depicted in the image below).
Quantum Annealing is used for solving optimisation problems. On this front, as highlighted above, researchers are looking for possible best possible configuration.
An example of this is the quantum experiment which was conducted by Volkswagen in association with Google and D-Wave Systems, which aimed to reduce heavy travel in the city Beijing.
The experiment was a success as it was able to reduce the traffic by selecting an ideal path for each vehicle.
While classical computers can take many years to compute the optimisation solution, quantum computing can make this happen within a few hours or even less.
Quantum Annealing can be provided beneficial for various industrial problems like air traffic control.
Quantum Simulations cater to specific problems in quantum physics and are beyond the capacity of classical computing.
An area where this type of quantum computing is more suitable includes modelling the effect of a chemical stimulation on massive subatomic particles. It is capable of simulating protein folding. Misfold protein can result in diseases such as Alzheimer’s and Parkinson’s.
In this area, quantum computing can help in computing the massive protein folding sequence to prepare an effective medication. In the upcoming period, there are possibilities that we see that quantum simulations would be used for rapid drug designer testing.
Universal Quantum Computing:
Universal Quantum Computing is most challenging to build; however, these computers are highly powerful and are most generally applicable. These computers would make use of 100,000 qubits. In present times, we can access not more than 128 qubits.
The idea behind developing the universal quantum computing is to direct the machine at any complex computation and get a quick solution. This includes solving the other two quantum computing types discussed above and even beyond that.
In the long run, experts believe that universal quantum computers could be beneficial in the fields of Artificial Intelligence.
INTERESTING READ: Top Technology Predictions for the Current Year & Beyond


Quantum computing has become a buzzword in the IT industry. Some people think it'll change how we do computing forever and give us more processing power than we ever imagined. Some fear this new technology might break all current encryption and security. Others are creating sci-fi shows based on quantum computing, like Devs, which appears in this list of our community's favorite TV shows.
But most people, even many developers, aren't quite sure what quantum computing is. Let's clear up some of the confusion.
Quantum computing terms you need to know
Before we get into how quantum computing works, let's look at some key terms that you'll need to know to understand the concept.
The quantum in quantum computing refers to quantum mechanics. A quantum in physics is the minimum amount of any physical property that can exist.
For instance, a photon is a single quantum of light. Quantization of energy and how it affects the interactions between matter and energy is part of the fundamental framework for describing the physical world.
Qubit is short for quantum bit — the quantum version of the bit we use in classical computing. Standard bits can only be one of two values: 1 or 0. Qubits, on the other hand, hold a superposition of all possible states.
Every quantum state can be represented as a sum of two or more other distinct states, and quantum particles combine all possible states. They remain in all of these states at once until they're actually observed and measured.
Think of a coin flip. Once the coin lands on the ground, it'll be heads or tails, but while it's in the air, it still has a chance of being either one. Quantum computers use the concept of superposition to manipulate qubits and affect their probabilities before making a final measurement to get the answer.
Entanglement is a process by which quantum particles can link up so that their states stay linked no matter how far apart they are in space. They share a unified quantum state and can exert an influence on each other.
By entangling qubits in a quantum computer, more information can be represented simultaneously, giving the quantum computer more computing power and the ability to solve more complicated problems.
In a quantum computer, entanglement is a good thing, but interference is bad. Quantum interference is part of a qubit’s natural behavior that can influence the probability of the final measurement of its superposition. Quantum computers try to reduce interference as much as possible to ensure more accurate results.
How does quantum computing work?
A quantum computer has three main parts.
The first part is the structure that holds the qubits used for computation. These qubits must be stored in a way that minimizes quantum interference. In some quantum computers, superfluids chill the qubit housing to a hundredth of a degree Celsius above absolute zero to keep the qubits stable. Other quantum computers use a vacuum to help with qubit cohesion and minimize interference between them.
The second part is a mechanism for transferring information to the qubits. To use them for computations, their behavior must be controlled so they can hold, change, and read information. There are a few ways to do this. Lasers, microwaves, and voltage are the most common.
The third and final major part of a quantum computer is a standard computer where the code written for the quantum computer is run. It interfaces with the control mechanism, which sends instructions to the qubits.
Where can quantum computing be used?
Quantum computing is still in its early stages, and it's not quite ready to be used in everyday businesses. Still, some companies are starting to find new uses for the technology.
Most of the work in quantum computing is currently being done by scientists and quantum computing experts who create proof-of-concept applications and test them on a small scale to help identify future uses for the technology. That way, they'll be ready when quantum hardware develops to the point that it's practical for more uses.
Also, while a quantum computer can do certain things many magnitudes faster than a classical computer, they don't do everything quicker and aren't practical for some computational problems. Here are some of the many industries where quantum computing will have the biggest impact.
The power of quantum computers threatens to make current cryptography techniques obsolete, such as RSA encryption, which is used to secure much of the sensitive data in the digital world. The good news is that there are already companies working on new cryptography techniques that even quantum computers can't crack.
Machine learning is changing many things about our world, but running machine learning algorithms on traditional computers takes a lot of time and resources. Scientists and Quantum Computing Researchers are looking into new ways to make machine learning faster and more efficient using quantum computers.
Quantum computers have many uses in the healthcare industry. They simulate chemical reactions much faster than standard computers, and they're also used for protein folding, where they help speed up the creation of new drugs.
Quantum computing is also used in fintech, where its power makes parsing massive amounts of financial data quicker and model creation more accurate. It can also be used in fraud detection and portfolio risk optimization.
Quantum computers are good at optimization. There are many challenges involved in supply chains and international shipping routes that can take a standard computer literally years to solve, but a quantum computer can solve in only minutes.
Programming languages and SDKs used in quantum computing
The programming languages used in quantum computing may have a similar syntax to those used in standard programming, but they were created specifically to handle the quantum computing environment.
But that doesn't mean you can't still use standard programming languages. There are high-level SDKs (Software Development Kits) written in languages like Python that allow you to branch into quantum computing without needing to learn a new language.
Here are some of the many programming languages and SDKs used in quantum computing:
- QCL: QCL (Quantum Computing Language) is one of the first programming languages used for quantum computing. Its syntax resembles the C programming language, and its data types are similar to the primitive data types in C.
- Q: Q was the second programming language implemented in quantum computers. It was designed as an extension of C++, so C++ developers can start working with it quickly.
- OpenQASM: OpenQASM (Open Quantum Assembly Language) is a low-level language released by IBM for use with quantum computers.
- Q#: Q# is an open-source quantum programming language offered by Microsoft. It has some features that developers who know the Python, C#, and F# programming languages will recognize.
- Silq: Silq is an open-source high-level programming language written in the D programming language. It's available on Github and is relatively new. The first version was published in 2020.
- Cirq: Cirq is a Python library created by Google for writing, manipulating, and optimizing quantum circuits. Cirq abstracts away many of the low-level details of quantum hardware in a language familiar to many developers.
- Qiskit SDK: Qiskit is a software development kit created specifically for working with the OpenQASM programming language and IBM Q quantum processors. It's written in Python, so developers don't have to have high-level knowledge of quantum hardware to use it.
- Braket SDK: The Braket SDK is yet another quantum computing SDK written in Python that works with Amazon's proprietary Braket quantum computing platform.
How to get started in quantum computing
As we said, quantum computing isn't yet practical enough to be used in the average business. So you can't get a job writing code for quantum computers yet, unless the job is with a business currently experimenting with the technology or building their own quantum computers.
Still, you can experiment with quantum computer coding right now. Here are four places you can do that:
- Amazon Braket: Amazon will give you one free hour per month to experiment with their quantum computing platform, and it provides an SDK written in Python to interact with the Braket platform so you can write quantum code in a familiar programming language.
- IBM Quantum: You can also sign up for an account with IBM to run experiments on their quantum computing platform. You can write your code in Python here using the Qiskit SDK.
- Azure Quantum: You can experiment with the quantum computers that Microsoft has access to, and when you sign up, you can get a free $200 credit.
- DWave Leap: DWave also provides developers with limited free access to their quantum computing platform.
Python is a good choice if you're ready to jump into quantum computing today since Circ, the Qiskit SDK, and the SDK for Amazon's Braket are based on the language. Check out our Learn Python 3 course to learn what you need to know to get started. Or, if you'd rather work with some of the low-level languages used for quantum computing, try Learn C++.


AI machine learning presents a roadmap to define new materials for any need, with implications in green energy and waste reduction.
Scientists and institutions dedicate more resources each year to the discovery of novel materials to fuel the world. As natural resources diminish and the demand for higher value and advanced performance products grows, researchers have increasingly looked to nanomaterials.
Nanoparticles have already found their way into applications ranging from energy storage and conversion to quantum computing and therapeutics. But given the vast compositional and structural tunability nanochemistry enables, serial experimental approaches to identify new materials impose insurmountable limits on discovery.
Now, researchers at Northwestern University and the Toyota Research Institute (TRI) have successfully applied machine learning to guide the synthesis of new nanomaterials, eliminating barriers associated with materials discovery. The highly trained algorithm combed through a defined dataset to accurately predict new structures that could fuel processes in clean energy, chemical, and automotive industries.
“We asked the model to tell us what mixtures of up to seven elements would make something that hasn’t been made before,” said Chad Mirkin, a Northwestern nanotechnology expert, and the paper’s corresponding author. “The machine predicted 19 possibilities, and, after testing each experimentally, we found 18 of the predictions were correct.”
The study, “Machine learning-accelerated design and synthesis of polyelemental heterostructures,” will be published December 22 in the journal Science Advances.
Mirkin is the George B. Rathmann Professor of Chemistry in the Weinberg College of Arts and Sciences; a professor of chemical and biological engineering, biomedical engineering, and materials science and engineering at the McCormick School of Engineering; and a professor of medicine at the Feinberg School of Medicine. He also is the founding director of the International Institute for Nanotechnology.
Mapping the materials genome
According to Mirkin, what makes this so important is the access to unprecedentedly large, quality datasets because machine learning models and AI algorithms can only be as good as the data used to train them.
The data-generation tool, called a “Megalibrary,” was invented by Mirkin and dramatically expands a researcher’s field of vision. Each Megalibrary houses millions or even billions of nanostructures, each with a slightly distinct shape, structure and composition, all positionally encoded on a two-by-two square centimeter chip. To date, each chip contains more new inorganic materials than have ever been collected and categorized by scientists.
Mirkin’s team developed the Megalibraries by using a technique (also invented by Mirkin) called polymer pen lithography, a massively parallel nanolithography tool that enables the site-specific deposition of hundreds of thousands of features each second.
When mapping the human genome, scientists were tasked with identifying combinations of four bases. But the loosely synonymous “materials genome” includes nanoparticle combinations of any of the usable 118 elements in the periodic table, as well as parameters of shape, size, phase morphology, crystal structure and more. Building smaller subsets of nanoparticles in the form of Megalibraries will bring researchers closer to completing a full map of a materials genome.
Mirkin said that even with something similar to a “genome” of materials, identifying how to use or label them requires different tools.
“Even if we can make materials faster than anybody on earth, that’s still a droplet of water in the ocean of possibility,” Mirkin said. “We want to define and mine the materials genome, and the way we’re doing that is through artificial intelligence.”
Machine learning applications are ideally suited to tackle the complexity of defining and mining the materials genome, but are gated by the ability to create datasets to train algorithms in the space. Mirkin said the combination of Megalibraries with machine learning may finally eradicate that problem, leading to an understanding of what parameters drive certain materials properties.
‘Materials no chemist could predict’
If Megalibraries provide a map, machine learning provides the legend.
Using Megalibraries as a source of high-quality and large-scale materials data for training artificial intelligence (AI) algorithms, enables researchers to move away from the “keen chemical intuition” and serial experimentation typically accompanying the materials discovery process, according to Mirkin.
“Northwestern had the synthesis capabilities and the state-of-the-art characterization capabilities to determine the structures of the materials we generate,” Mirkin said. “We worked with TRI’s AI team to create data inputs for the AI algorithms that ultimately made these predictions about materials no chemist could predict.”
In the study, the team compiled previously generated Megalibrary structural data consisting of nanoparticles with complex compositions, structures, sizes and morphologies. They used this data to train the model and asked it to predict compositions of four, five and six elements that would result in a certain structural feature. In 19 predictions, the machine learning model predicted new materials correctly 18 times — an approximately 95% accuracy rate.
With little knowledge of chemistry or physics, using only the training data, the model was able to accurately predict complicated structures that have never existed on earth.
“As these data suggest, the application of machine learning, combined with Megalibrary technology, may be the path to finally defining the materials genome,” said Joseph Montoya, senior research scientist at TRI.
Metal nanoparticles show promise for catalyzing industrially critical reactions such as hydrogen evolution, carbon dioxide (CO2) reduction and oxygen reduction and evolution. The model was trained on a large Northwestern-built dataset to look for multi-metallic nanoparticles with set parameters around phase, size, dimension and other structural features that change the properties and function of nanoparticles.
The Megalibrary technology may also drive discoveries across many areas critical to the future, including plastic upcycling, solar cells, superconductors and qubits.
A tool that works better over time
Before the advent of megalibraries, machine learning tools were trained on incomplete datasets collected by different people at different times, limiting their predicting power and generalizability. Megalibraries allow machine learning tools to do what they do best — learn and get smarter over time. Mirkin said their model will only get better at predicting correct materials as it is fed more high-quality data collected under controlled conditions.
“Creating this AI capability is about being able to predict the materials required for any application,” Montoya said. “The more data we have, the greater predictive capability we have. When you begin to train AI, you start by localizing it on one dataset, and, as it learns, you keep adding more and more data — it’s like taking a kid and going from kindergarten to their Ph.D. The combined experience and knowledge ultimately dictates how far they can go.”
The team is now using the approach to find catalysts critical to fueling processes in clean energy, automotive and chemical industries. Identifying new green catalysts will enable the conversion of waste products and plentiful feedstocks to useful matter, hydrogen generation, carbon dioxide utilization and the development of fuel cells. Producing catalysts also could be used to replace expensive and rare materials like iridium, the metal used to generate green hydrogen and CO2 reduction products.
Reference: “Machine learning-accelerated design and synthesis of polyelemental heterostructures” 22 December 2021, Science Advances.
The research was supported by TRI. Additional support came from the Sherman Fairchild Foundation, Inc., and the Air Force Office of Scientific Research (award numbers FA9550-16-1-0150 and FA9550-18-1-0493). Northwestern co-authors are materials science and engineering doctoral student Carolin B. Wahl and chemistry doctoral student Jordan H. Swisher, both members of the Mirkin lab. Authors from TRI include Muratahan Aykol and Montoya.
This work made use of the EPIC facility of Northwestern University’s NUANCE Center, which has received support from the Soft and Hybrid Nanotechnology Experimental (SHyNE) Resource (NSF ECCS-1542205); the MRSEC program (NSF DMR-1720139) at the Materials Research Center; the International Institute for Nanotechnology (IIN); the Keck Foundation; and the State of Illinois, through the IIN.