If you understand how these systems operate, then you understand why they could change everything.
If someone asked you to picture a quantum computer, what would you see in your mind?
Maybe you see a normal computer-- just bigger, with some mysterious physics magic going on inside? Forget laptops or desktops. Forget computer server farms. A quantum computer is fundamentally different in both the way it looks, and ,more importantly, in the way it processes information.
There are currently several ways to build a quantum computer. But let’s start by describing one of the leading designs to help explain how it works.
Imagine a lightbulb filament, hanging upside down, but it’s the most complicated light you’ve ever seen. Instead of one slender twist of wire, it has organized silvery swarms of them, neatly braided around a core. They are arranged in layers that narrow as you move down. Golden plates separate the structure into sections.
The outer part of this vessel is called the chandelier. It’s a supercharged refrigerator that uses a special liquified helium mix to cool the computer’s quantum chip down to near absolute zero. That’s the coldest temperature theoretically possible.
At such low temperatures, the tiny superconducting circuits in the chip take on their quantum properties. And it’s those properties, as we’ll soon see, that could be harnessed to perform computational tasks that would be practically impossible on a classical computer.
Traditional computer processors work in binary—the billions of transistors that handle information on your laptop or smartphone are either on (1) or they’re off (0). Using a series of circuits, called “gates,” computers perform logical operations based on the state of those switches.
Classical computers are designed to follow specific inflexible rules. This makes them extremely reliable, but it also makes them ill-suited for solving certain kinds of problems—in particular, problems where you’re trying to find a needle in a haystack.
This is where quantum computers shine.
If you think of a computer solving a problem as a mouse running through a maze, a classical computer finds its way through by trying every path until it reaches the end.
What if, instead of solving the maze through trial and error, you could consider all possible routes simultaneously?
Quantum computers do this by substituting the binary “bits” of classical computing with something called “qubits.” Qubits operate according to the mysterious laws of quantum mechanics: the theory that physics works differently at the atomic and subatomic scale.
The classic way to demonstrate quantum mechanics is by shining a light through a barrier with two slits. Some light goes through the top slit, some the bottom, and the light waves knock into each other to create an interference pattern.
But now dim the light until you’re firing individual photons one by one—elementary particles that comprise light. Logically, each photon has to travel through a single slit, and they’ve got nothing to interfere with. But somehow, you still end up with an interference pattern.
Here’s what happens according to quantum mechanics: Until you detect them on the screen, each photon exists in a state called “superposition.” It’s as though it’s traveling all possible paths at once. That is, until the superposition state “collapses” under observation to reveal a single point on the screen.
Qubits use this ability to do very efficient calculations.
For the maze example, the superposition state would contain all the possible routes. And then you’d have to collapse the state of superposition to reveal the likeliest path to the cheese.
Just like you add more transistors to extend the capabilities of your classical computer, you add more qubits to create a more powerful quantum computer.
Thanks to a quantum mechanical property called “entanglement,” scientists can push multiple qubits into the same state, even if the qubits aren’t in contact with each other. And while individual qubits exist in a superposition of two states, this increases exponentially as you entangle more qubits with each other. So a two-qubit system stores 4 possible values, a 20-qubit system more than a million.
So what does that mean for computing power? It helps to think about applying quantum computing to a real world problem: the one of prime numbers.
A prime number is a natural number greater than 1 that can only be divided evenly by itself or 1.
While it’s easy to multiply small numbers into giant ones, it’s much harder to go the reverse direction; you can’t just look at a number and tell its factors. This is the basis for one of the most popular forms of data encryption, called RSA.
You can only decrypt RSA security by factoring the product of two prime numbers. Each prime factor is typically hundreds of digits long, and they serve as unique keys to a problem that’s effectively unsolvable without knowing the answers in advance.
In 1995, M.I.T. mathematician Peter Shor, then at AT&T Bell Laboratories, devised a novel algorithm for factoring prime numbers whatever the size. One day, a quantum computer could use its computational power, and Shor’s algorithm, to hack everything from your bank records to your personal files.
In 2001, IBM made a quantum computer with seven qubits to demonstrate Shor’s algorithm. For qubits, they used atomic nuclei, which have two different spin states that can be controlled through radio frequency pulses.
This wasn’t a great way to make a quantum computer, because it’s very hard to scale up. But it did manage to run Shor’s algorithm and factor 15 into 3 and 5. Hardly an impressive calculation, but still a major achievement in simply proving the algorithm works in practice.
Even now, experts are still trying to get quantum computers to work well enough to best classical supercomputers.
That remains extremely challenging, mostly because quantum states are fragile. It’s hard to completely stop qubits from interacting with their outside environment, even with precise lasers in supercooled or vacuum chambers.
Any noise in the system leads to a state called “decoherence,” where superposition breaks down and the computer loses information.
A small amount of error is natural in quantum computing, because we’re dealing in probabilities rather than the strict rules of binary. But decoherence often introduces so much noise that it obscures the result.
When one qubit goes into a state of decoherence, the entanglement that enables the entire system breaks down.
So how do you fix this? The answer is called error correction--and it can happen in a few ways.
Error Correction #1: A fully error-corrected quantum computer could handle common errors like “bit flips,” where a qubit suddenly changes to the wrong state.
To do this you would need to build a quantum computer with a few so-called “logical” qubits that actually do the math, and a bunch of standard qubits that correct for errors.
It would take a lot of error-correcting qubits—maybe 100 or so per logical qubit--to make the system work. But the end result would be an extremely reliable and generally useful quantum computer.
Error Correction #2: Other experts are trying to find clever ways to see through the noise generated by different errors. They are trying to build what they call “Noisy intermediate-scale quantum computers” using another set of algorithms.
That may work in some cases, but probably not across the board.
Error Correction #3: Another tactic is to find a new qubit source that isn’t as susceptible to noise, such as “topological particles” that are better at retaining information. But some of these exotic particles (or quasi-particles) are purely hypothetical, so this technology could be years or decades off.
Because of these difficulties, quantum computing has advanced slowly, though there have been some significant achievements.
In 2019, Google used a 54-qubit quantum computer named “Sycamore” to do an incredibly complex (if useless) simulation in under 4 minutes—running a quantum random number generator a million times to sample the likelihood of different results.
Sycamore works very differently from the quantum computer that IBM built to demonstrate Shor’s algorithm. Sycamore takes superconducting circuits and cools them to such low temperatures that the electrical current starts to behave like a quantum mechanical system. At present, this is one of the leading methods for building a quantum computer, alongside trapping ions in electric fields, where different energy levels similarly represent different qubit states.
Sycamore was a major breakthrough, though many engineers disagree exactly how major. Google said it was the first demonstration of so-called quantum advantage: achieving a task that would have been impossible for a classical computer.
It said the world’s best supercomputer would have needed 10,000 years to do the same task. IBM has disputed that claim.
At least for now, serious quantum computers are a ways off. But with billions of dollars of investment from governments and the world’s biggest companies, the race for quantum computing capabilities is well underway. The real question is: how will quantum computing change what a “computer” actually means to us. How will it change how our electronically connected world works? And when?


From brain to heart to stomach, the bodies of humans and animals generate weak magnetic fields that a supersensitive detector could use to pinpoint illnesses, trace drugs – and maybe even read minds. Sensors no bigger than a thumbnail could map gas deposits underground, analyze chemicals, and pinpoint explosives that hide from other probes.
Now scientists at the U.S. Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) and the University of California at Berkeley, working with colleagues from Harvard University, have improved the performance of one of the most potent possible sensors of magnetic fields on the nanoscale – a diamond defect no bigger than a pair of atoms, called a nitrogen vacancy (NV) center.
The research team’s discoveries may eventually enable clocks smaller than computer chips yet accurate to within a few quadrillionths of a second, or rotational sensors quicker and more tolerant of extreme temperatures than the gyroscopes in smart phones. Before long, an inexpensive chip of diamond may be able to house a quantum computer.
A sensor made of diamond
Nitrogen vacancy centers are some of the most common defects in diamonds. When a nitrogen atom substitutes for a carbon atom in the diamond crystal and pairs with an adjacent vacancy (where a carbon atom is missing altogether), a number of electrons not bonded to the missing carbon atoms are left in the center.
The electron spin states are well defined and very sensitive to magnetic fields, electric fields, and light, so they can easily be set, adjusted, and read out by lasers.
“The spin states of NV centers are stable across a wide range of temperatures from very hot to very cold,” says Dmitry Budker of Berkeley Lab’s Nuclear Science Division, who is also a physics professor at UC Berkeley. Even tiny flecks of diamond costing pennies per gram could be used as sensors because, says Budker, “we can control the number of NV centers in the diamond just by irradiating and baking it,” that is, annealing it.
The challenge is to keep the information inherent in the spin states of NV centers, once it has been encoded there, from leaking away before measurements can be performed; in NV centers, this requires extending what’s called the “coherence” time of the electron spins, the time the spins remain synchronized with each other.
Recently Budker worked with Ronald Walsworth of Harvard in a team that included Harvard’s Nir Bar-Gill and UC Berkeley postdoc Andrey Jarmola. They extended the coherence time of an ensemble of NV electron spins by more than two orders of magnitude over previous measurements.
“To me, the most exciting aspect of this result is the possibility of studying changes in the way NV centers interact with one another,” says Bar-Gill, the first author of the paper, who will move to Hebrew University in Jerusalem this fall. “This is possible because the coherence times are much longer than the time needed for interactions between NV centers.”
Bar-Gill adds, “We can now imagine engineering diamond samples to realize quantum computing architectures.” The interacting NV centers take the role of bits in quantum computers, called qubits. Whereas a binary digit is either a 1 or a 0, a qubit represents a 1 and a 0 superposed, a state of Schrödinger’s-cat-like simultaneity that persists as long as the states are coherent, until a measurement is made that collapses all the entangled qubits at once.
“We used a couple of tricks to get rid of sources of decoherence,” says Budker. “One was to use diamond samples specially prepared to be pure carbon-12.” Natural diamond includes a small amount of the isotope carbon-13, whose nuclear spin hurries the decoherence of the NV center electron spins. Carbon-12 nuclei are spin zero.
“The other trick was to lower the temperature to the temperature of liquid nitrogen,” Budker says. Decoherence was reduced by cooling the samples to 77 degrees Kelvin, below room temperature but still readily accessible.
Working together in Budker’s lab, members of the team mounted the diamond samples inside a cryostat. A laser beam passing through the diamond, plus a magnetic field, tuned the electron spins of the NV centers and caused them to fluoresce. Their fluorescent brightness was a measure of spin-state coherence.
“Controlling the spin is essential,” Budker says, “so we borrowed an idea from nuclear magnetic resonance” – the basis for such familiar procedures as magnetic resonance imaging (MRI) in hospitals.
While different from nuclear spin, electron spin coherence can be extended with similar techniques. Thus, as the spin states of the NV centers in the diamond sample were about to decohere, the experimenters jolted the diamond with a series of up to 10,000 short microwave pulses. The pulses flipped the electron spins as they began to fall out of synchronization with one another, producing “echoes” in which the reversed spins caught up with themselves. Coherence was reestablished.
Eventually the researchers achieved spin coherence times lasting over half a second. “Our results really shine for magnetic field sensing and for quantum information,” says Bar-Gill.
Long spin-coherence times add to the advantages diamond already possesses, putting diamond NVs at the forefront of potential candidates for practical quantum computers – a favorite pursuit of the Harvard researchers. What Budker’s group finds an even hotter prospect is the potential for long coherence times in sensing oscillating magnetic fields, with applications ranging from biophysics to defense.
ABSTRACT – Solid-state spin systems such as nitrogen-vacancy colour centres in diamond are promising for applications of quantum information, sensing and metrology. However, a key challenge for such solid-state systems is to realize a spin coherence time that is much longer than the time for quantum spin manipulation protocols. Here we demonstrate an improvement of more than two orders of magnitude in the spin coherence time (T2) of nitrogen-vacancy centres compared with previous measurements: T2≈0.6 s at 77 K. We employed dynamical decoupling pulse sequences to suppress nitrogen-vacancy spin decoherence, and found that T2 is limited to approximately half of the longitudinal spin relaxation time over a wide range of temperatures, which we attribute to phonon-induced decoherence. Our results apply to ensembles of nitrogen-vacancy spins, and thus could advance quantum sensing, enable squeezing and many-body entanglement, and open a path to simulating driven, interaction-dominated quantum many-body Hamiltonians.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.


Image credit: Umberto Unsplash
Imagine being able to disappear from one place and then reappear in the exact same condition at another location. You could visit your favorite bakery in Paris for breakfast, spend the afternoon on a beach in Thailand, and — why relegate yourself to Earth? — beam yourself up to the moon before going home to your dinner. The idea of teleportation is fascinating and prevalent in science fiction, with Star Trek’s “Beam me up, Scotty” catchphrase immediately coming to mind.
While seemingly unattainable, it is not actually impossible according to the laws of physics … it just depends on scale.
The bizarre properties of subatomic particles
In 1993, a group of six international scientists discussed the idea that teleportation is possible on the subatomic level, and demonstrated the transportation of systems such as single photons, coherent light fields, nuclear spins, and trapped ions. While perhaps disappointing for the avid traveler, quantum teleportation cannot be applied to matter, but could be revolutionary in transporting information and in the creation of quantum computers; perhaps — according to some experts — even leading to a quantum internet in which the limitations of current networks are overcome with improved privacy, security, and computational capabilities.
Prior to this, scientists believed that perfect teleportation was not possible even on the subatomic scale as it violated the uncertainty principle in quantum mechanics. In simple terms, this principle states that the more accurately an object is measured or observed, the more it is disturbed by the process of measuring it. This means that it would be impossible to make a perfect replica because we cannot accurately measure the original without changing its quantum state.
But in 1993, the team of scientists found a way around this in the form of a paradoxical feature of quantum mechanics called the Einstein-Podolsky-Rosen (EPR) effect. First put forth in a paper in 1935 by Einstein and his post-doctoral researchers, the thought experiment behind EPR describes a phenomenon known as “quantum entanglement”, in which the quantum states of two or more entangled objects, such as a pair of photons, have the same state even when separated by great distances. Changing the state of one of these objects simultaneously changes the state of the other even when their is no physical connection. This phenomenon was famously dubbed “spooky action at a distance” by Einstein.
The role of quantum entanglement
Scientists are only beginning to understand the mysteries of entanglement and how it makes quantum teleportation possible. In brief, one could “scan” an object to be teleported and supplement the missing information about that object (that arises as a result of the uncertainty principle) using information from a pair of entangled partners. The process would involve three particles in which one particle “teleports” its state to two distant entangled particles. Scientists call this teleportation in the sense that a particle with a particular set of properties disappears at one location and one with the exact same properties appears somewhere else.
Entanglement provides the means of sending qubits of information — the quantum version of a binary bit; a basic unit of information — without any physical contact. Individual atoms or particles could replace transistors, vastly expanding our computing powers and capabilities. As opposed to binary computers, which operate in basic units of information represented by 1s and 0s, qubits can exist as a “1” or “0” simultaneously through the principle of superposition. This simple attribute allows a quantum computer to store greater amounts of data and quickly reason through complex problems or computing tasks by simultaneously exploring multiple pathways and choosing the most efficient one.
This, coupled with entanglement and a principle called the no-cloning theorem, which forbids the identical copying of unknown quantum states, will forever and completely change the way in which we store, transfer, and encrypt data. Though we are still quite a way away from realizing a true quantum age, researchers are making some impressive strides and getting us ever closer, one experiment at a time.
Entanglement in the “real” world
Perhaps the most memorable occurred in 2017 when a team of Chinese researchers teleported information to the orbiting Micuis satelite and back, demonstrating “the first quantum teleportation of independent single-photon qubits from a ground observatory to a low Earth orbit satellite … with a distance up to 1400 km”.
Prior to this, teleportation experiments had only been demonstrated between locations that were limited to a distance on the order of 100 kilometers. However, to realize a global-scale quantum internet, the team proposed exploiting “space-based links” to connect two remote points on the Earth, which would reduce what they called “channel loss” (essentially signal loss) because most of the photons’ propagation path is in empty space. The team also demonstrated advancements in their data transmission abilities at greater distances and with better encryption.
More recently, researchers extended entanglement to electrons by making qubits from individual electrons. This in particular has been challenging compared to using photons — which naturally propagate over large distances — because electrons are confined in space. These types of studies pave the way to explore quantum teleportation in all spin states of matter.
All of this is part of what researchers call “the second quantum revolution”, which follows the initial discovery of the quantum world and its seemingly bizarre principles in the 20th century by key players such as Heisenberg, Schrödinger, and Einstein.
It’s exciting to see this field developing from infancy. Similar to the way in which we scoff at the first room-sized computers unveiled in the 1950s, we may one day look at our current binary computers in the same way. Although we are still far off from this new world built on the mind-bending principles of quantum mechanics, we are on the verge of unlocking incredible new capabilities that will provide endless possibilities.


Rice University physicists have created the world’s first laser-cooled neutral plasma, completing a 20-year quest that sets the stage for simulators that re-create exotic states of matter found inside Jupiter and white dwarf stars.
The findings are detailed this week in the journal Science and involve new techniques for laser cooling clouds of rapidly expanding plasma to temperatures about 50 times colder than deep space.
“We don’t know the practical payoff yet, but every time physicists have laser cooled a new kind of thing, it has opened a whole world of possibilities,” said lead scientist Tom Killian, professor of physics and astronomy at Rice. “Nobody predicted that laser cooling atoms and ions would lead to the world’s most accurate clocks or breakthroughs in quantum computing. We do this because it’s a frontier.”
Killian and graduate students Tom Langin and Grant Gorman used 10 lasers of varying wavelengths to create and cool the neutral plasma. They started by vaporizing strontium metal and using one set of intersecting laser beams to trap and cool a puff of strontium atoms about the size of a child’s fingertip. Next, they ionized the ultracold gas with a 10-nanosecond blast from a pulsed laser. By stripping one electron from each atom, the pulse converted the gas to a plasma of ions and electrons.
Energy from the ionizing blast causes the newly formed plasma to expand rapidly and dissipate in less than one thousandth of a second. This week’s key finding is that the expanding ions can be cooled with another set of lasers after the plasma is created. Killian, Langin and Gorman describe their techniques in the new paper, clearing the way for their lab and others to make even colder plasmas that behave in strange, unexplained ways.
Plasma is an electrically conductive mix of electrons and ions. It is one of four fundamental states of matter; but unlike solids, liquids and gases, which are familiar in daily life, plasmas tend to occur in very hot places like the surface of the sun or a lightning bolt. By studying ultracold plasmas, Killian’s team hopes to answer fundamental questions about how matter behaves under extreme conditions of high density and low temperature.
To make its plasmas, the group starts with laser cooling, a method for trapping and slowing particles with intersecting laser beams. The less energy an atom or ion has, the colder it is, and the slower it moves about randomly. Laser cooling was developed in the 1990s to slow atoms until they are almost motionless, or just a few millionths of a degree above absolute zero.
“If an atom or ion is moving, and I have a laser beam opposing its motion, as it scatters photons from the beam it gets momentum kicks that slow it,” Killian said. “The trick is to make sure that light is always scattered from a laser that opposes the particle’s motion. If you do that, the particle slows and slows and slows.”
During a postdoctoral fellowship at the National Institute of Standards and Technology in Bethesda, Md., in 1999, Killian pioneered the ionization method for creating neutral plasma from a laser-cooled gas. When he joined Rice’s faculty the following year, he started a quest for a way to make the plasmas even colder. One motivation was to achieve “strong coupling,” a phenomenon that happens naturally in plasmas only in exotic places like white dwarf stars and the center of Jupiter.
“We can’t study strongly coupled plasmas in places where they naturally occur,” Killian said. “Laser cooling neutral plasmas allows us to make strongly coupled plasmas in a lab, so that we can study their properties”
“In strongly coupled plasmas, there is more energy in the electrical interactions between particles than in the kinetic energy of their random motion,” Killian said. “We mostly focus on the ions, which feel each other, and rearrange themselves in response to their neighbors’ positions. That’s what strong coupling means.”
Because the ions have positive electric charges, they repel one another through the same force that makes your hair stand up straight if it gets charged with static electricity.
“Strongly coupled ions can’t be near one another, so they try to find equilibrium, an arrangement where the repulsion from all of their neighbors is balanced,” he said. “This can lead to strange phenomena like liquid or even solid plasmas, which are far outside our normal experience.”
In normal, weakly coupled plasmas, these repulsive forces only have a small influence on ion motion because they’re far outweighed by the effects of kinetic energy, or heat.
“Repulsive forces are normally like a whisper at a rock concert,” Killian said. “They’re drowned out by all the kinetic noise in the system.”
In the center of Jupiter or a white dwarf star, however, intense gravity squeezes ions together so closely that repulsive forces, which grow much stronger at shorter distances, win out. Even though the temperature is quite high, ions become strongly coupled.
Killian’s team creates plasmas that are orders of magnitude lower in density than those inside planets or dead stars, but by lowering the temperature they raise the ratio of electric-to-kinetic energies. At temperatures as low as one-tenth of a Kelvin above absolute zero, Killian’s team has seen repulsive forces take over.
“Laser cooling is well developed in gases of neutral atoms, for example, but the challenges are very different in plasmas,” he said.
“We are just at the beginning of exploring the implications of strong coupling in ultracold plasmas,” Killian said. “For example, it changes the way that heat and ions diffuse through the plasma. We can study those processes now. I hope this will improve our models of exotic, strongly coupled astrophysical plasmas, but I am sure we will also make discoveries that we haven’t dreamt of yet. This is the way science works.”
The research was supported by the Air Force Office of Scientific Research and the Department of Energy’s Office of Science.


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”.


Every high school science course focuses on the fundamental states of matter in the form of gases, liquids, and solids—states that are straightforward to study and manipulate. But there is a fourth state of matter that most people are much less familiar with because it does not exist freely on Earth.
This is plasma—a gas in which electrons have been stripped from atoms. The sun is such a mixture of ions and electrons, and much of interstellar space is filled with plasma. But on Earth, plasmas tend to occur fleetingly—in lightning, for example.
However, in the past 100 years, scientists and engineers have begun exploiting this form of matter to create light (neon lights are plasmas) and to interact with materials in a way that modifies the properties of their surfaces.
Because plasmas are generally hard to make and control, they are often confined to industrial machinery or specialized labs. But an easier way to make and control plasmas could change all that.
Enter Kausik Das of the University of Maryland Eastern Shore, and several colleagues who have found a way to create plasmas in an ordinary kitchen microwave. Their technique opens the way for a new generation to experiment with this exotic form of matter and perhaps to develop new applications.
First, some background. One way to make plasmas is to break apart molecules using powerful electric fields. This creates ions that the electric fields then accelerate, causing them to smash into other molecules. These collisions knock electrons off the atoms, creating more ions.
In the right circumstances, this process triggers a cascade that causes the entire gas to become ionized.
Das and his colleagues have worked out how to do this in a standard kitchen microwave oven (they don’t identify the brand). They also use a cheap glass flask capable of holding a vacuum as well as a seal.
Kitchen microwaves produce electromagnetic radiation with a wavelength of around 12 centimeters. These waves particularly influence polar molecules that have a positive charge at one end and a negative charge at the other.
Water is a good example of a polar molecule. As the alternating field changes, water molecules attempt to align themselves with the field. This rotation causes them to bump into other molecules, thereby raising their temperature.
But if the density of molecules is low, they do not bump into other molecules and so cannot dissipate this extra energy. In that case, the alternating field causes the water molecules to rotate ever faster and eventually rip apart.
That’s the process that triggers the formation of a plasma. Das and company exploit it by sucking air out of their flask to create a low pressure. The low-pressure gas consists mostly of nitrogen and oxygen, but a few water molecules are also inevitably present.
Das's team then places the flask in the microwave and switches it on. The microwaves rip apart the water molecules inside the flask and accelerate them. If the pressure is low enough, they gain enough kinetic energy to knock electrons off nitrogen molecules, and the cascade begins. This creates a plasma that glows with a soft blue light.
But only for a few seconds. Soon the process begins to tear apart oxygen atoms, which creates a purple light. So the plasma changes color.
Das and company observe exactly this color evolution in their experiments, although they had to experiment carefully with the pressure in the flask. Too much gas prevents the water molecules from gaining enough kinetic energy to trigger the cascade. Too little gas means that collisions are less likely, so a plasma is more difficult to form. Das and his colleagues say their goal is to operate at the sweet spot between these regimes.
To get a better idea of what is going on, the team has analyzed the spectrum of light produced by the plasma to reveal the telltale signature of oxygen and nitrogen. And voilà—they have a plasma generated in a kitchen microwave.
That turns out to be useful for a variety of things that are otherwise impossible outside specialized labs. For example, Das and company show how to use the plasma to change the properties of polydimethylsiloxane, or PDMS, a common silicon-based polymer.
This is usually hydrophyllic—it attracts water. But bathing the material in the plasma for just a few seconds makes it hydrophobic. This property can be quantified by measuring the contact angle that a drop of water makes with the surface. Before treatment, PDMS has a contact angle of 64 degrees. After treatment, the angle increases to 134 degrees.
This is probably because the various ions in the plasma become embedded in the surface of the material during exposure. Those ions repel water.
The team goes on to show how to modify surfaces so they can become more adhesive and even change their electronic properties.
That’s interesting work that can be done not just in any lab but in any kitchen. It will certainly be a useful teaching method, but it may also allow home-based makers to experiment with plasma cleaning and etching.
As Das and his colleagues conclude: “These simple techniques of plasma generation and subsequent surface treatment and modification may lead to new opportunities to conduct research not only in advanced labs, but also in undergraduate and even high school research labs.”
Ref: arxiv.org/abs/1807.06784 : Plasma Generation by Household Microwave Oven for Surface Modification and Other Emerging Applications
Quantum computing has a hype problem
Quantum computing startups are all the rage, but it’s unclear if they’ll be able to produce anything of use in the near future.
These hackers showed just how easy it is to target critical infrastructure
Two Dutch researchers have won a major hacking championship by hitting the software that runs the world’s power grids, gas pipelines, and more. It was their easiest challenge yet.
Russia hacked an American satellite company one hour before the Ukraine invasion
The attack on Viasat showcases cyber’s emerging role in modern warfare.
Russia is risking the creation of a “splinternet”—and it could be irreversible
If Russia disconnects from—or is booted from— the internet’s governing bodies, the internet may never be the same again for any of us.
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What is quantum computing and why do we need it
Quantum computing is a type of computing that relies on the quantum mechanics phenomenon called quantum superposition. Quantum computing harnesses the power of atoms and molecules to allow us to solve problems that we can’t yet solve with our traditional computers. We’ve managed to use this type of computer for factoring large numbers, developing new material, identifying complex networks, and simulating physical phenomena. However, quantum computing is limited to extremely low temperatures and only represents a tiny fraction of today’s computers. Therefore, it is difficult to imagine how this technology will impact the world in the near future. By learning more about quantum computing and how it works, we can make practical use of this technology in our lives, making this amazing discovery useful for everyone.
The first thing to know is that computers, or other forms of computation, can be broken into two parts: hardware and software. Hardware consists of individual components while software includes all of the rules required to use those components. More specifically, hardware is divided into processors (which handle operations) and memory (which holds data). Software can be broken down even further into applications. This article about quantum computing will only cover the hardware side of the equation as this is more useful in day-to-day life.
Each computer chip today contains billions of transistors that can perform logical operations. However, the speed of these transistors is restricted by the laws of thermodynamics. These limitations make it difficult to perform calculations quickly enough to be useful for complex tasks like encryption or factoring large numbers. Each logical operation takes about 10 microseconds, which is about 1,000 times slower than what a human can achieve in this time frame when memorizing numbers.
How quantum computers work
All matter, including atoms and molecules, can be in a superposition state. According to the rules of quantum mechanics, this means that an atom or molecule can exist in any state of a particular observable property. This can allow an atom or molecule to exist as multiple states at the same time. Since classical computers represent information as a binary system (either 1 or 0), the superposition state of atoms and molecules can be used to create new types of computers. This is the idea behind quantum computers: they are based on quantum mechanics and can solve certain problems much faster than classical computers.
In a classical computer, for example, each bit of information is represented by an on or off state (1 or 0). Electrons orbiting an atom’s nucleus represent one set of information in a quantum computer, while the nucleus’ position represents another set of information. Each piece of information can exist as multiple states at once. This superposition allows the quantum computer to make computations that surpass the speed of any classical computer.
How Quantum Computers Work
The benefits of quantum computing
a. More Powerful: The quantum superposition state can store more information than the classical state, making quantum computers much more powerful. In fact, it’s possible to use a single atom to perform computations that are comparable in speed to a supercomputer.
b. More Efficient: Because quantum computers can process large amounts of data, they can solve problems much more quickly than classical computers, making them much more efficient.
c. More Secure: Because quantum computers are less susceptible to hackers, they are a lot more difficult to break into. Additionally, because the information is stored in quantum superpositions, it’s impossible to tamper with the information without doing so on a system-wide scale.
d. More Transparent: Because the superposition state is more robust, it’s possible to use quantum computers to hide information (in other words, to “quantum-encrypt” information). In fact, this ability could be used to build large-scale encryption and key management systems with which it would be impossible for anyone to hack into.
e. More Robust: Because the quantum state is much less susceptible to noise and interference, it’s possible to build quantum computers in which errors don’t occur as often.
f. More Secure: Because the quantum state is much more robust, it’s possible to build quantum computers that are resistant to hacking.
g. More Robust: Because it’s impossible to hack the information in a quantum state without destroying the superposition, a quantum computer is much more difficult to hack or crack than one that uses classical information.
Challenges in developing quantum computers
a. Unstable: Quantum computers must be kept at very low temperatures in order to function properly, which limits their practical use. In addition, quantum computers can not be easily integrated into current computer systems because they operate on completely different principles. b. Expensive: Large amounts of resources are required to build and maintain quantum computers. For example, some materials used in the development of quantum computers can cost thousands of dollars per gram. This restricts their use to only wealthy countries. c. Difficult to interface with classical computers: Once quantum computing is developed, scientists must come up with a way to connect existing computers with these new devices in order for them to work optimally together. d. Limited to solving only specific problems: Quantum computers are very specialized, and are currently limited to solving specific types of problems. For example, quantum computing is not ideal for tasks that require processing large amounts of data quickly.
a. Coulomb-blockade technology b. Superconducting circuits c. Ion traps d. Chemical reactions e. Spin glass f. Biological quantum computing
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Back in 1958, in the earliest days of the computing revolution, the US Office of Naval Research organized a press conference to unveil a device invented by a psychologist named Frank Rosenblatt at the Cornell Aeronautical Laboratory. Rosenblatt called his device a perceptron, and the New York Times reported that it was “the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself, and be conscious of its existence.”
Those claims turned out to be somewhat overblown. But the device kick-started a field of research that still has huge potential today.
A perceptron is a single-layer neural network. The deep-learning networks that have generated so much interest in recent years are direct descendants. Although Rosenblatt’s device never achieved its overhyped potential, there is great hope that one of its descendants might.
Today, there is another information processing revolution in its infancy: quantum computing. And that raises an interesting question: is it possible to implement a perceptron on a quantum computer, and if so, how powerful can it be?
Today we get an answer of sorts thanks to the work of Francesco Tacchino and colleagues at the University of Pavia in Italy. These guys have built the world’s first perceptron implemented on a quantum computer and then put it through its paces on some simple image processing tasks.
In its simplest form, a perceptron takes a vector input—a set of numbers—and multiplies it by a weighting vector to produce a single-number output. If this number is above a certain threshold the output is 1, and if it is below the threshold the output is 0.
That has some useful applications. Imagine a pixel array that produces a set of light intensity levels—one for each pixel—when imaging a particular pattern. When this set of numbers is fed into a perceptron, it produces a 1 or 0 output. The goal is to adjust the weighting vector and threshold so that the output is 1 when it sees, say a cat, and 0 in all other cases.
Tacchino and co have repeated Rosenblatt’s early work on a quantum computer. The technology that makes this possible is IBM’s Q-5 “Tenerife” superconducting quantum processor. This is a quantum computer capable of processing five qubits and programmable over the web by anyone who can write a quantum algorithm.
Tacchino and co have created an algorithm that takes a classical vector (like an image) as an input, combines it with a quantum weighting vector, and then produces a 0 or 1 output.
The big advantage of quantum computing is that it allows an exponential increase in the number of dimensions it can process. While a classical perceptron can process an input of N dimensions, a quantum perceptron can process 2N dimensions.
Tacchino and co demonstrate this on IBM’s Q-5 processor. Because of the small number of qubits, the processor can handle N = 2. This is equivalent to a 2x2 black-and-white image. The researchers then ask: does this image contain horizontal or vertical lines, or a checkerboard pattern?
It turns out that the quantum perceptron can easily classify the patterns in these simple images. “We show that this quantum model of a perceptron can be used as an elementary nonlinear classifier of simple patterns,” say Tacchino and co.
They go on to show how it could be used in more complex patterns, albeit in a way that is limited by the number of qubits the quantum processor can handle.
That’s interesting work with significant potential. Rosenblatt and others soon discovered that a single perceptron can only classify very simple images, like straight lines. However, other scientists found that combining perceptrons into layers has much more potential. Various other advances and tweaks have led to machines that can recognize objects and faces as accurately as humans can, and even thrash the best human players of chess and Go.
Tacchino and co’s quantum perceptron is at a similarly early stage of evolution. Future goals will be to encode the equivalent of gray-scale images and to combine quantum perceptrons into many-layered networks.
This group’s work has that potential. “Our procedure is fully general and could be implemented and run on any platform capable of performing universal quantum computation,” they say.
Of course, the limiting factor is the availability of more powerful quantum processors capable of handling larger numbers of qubits. But most quantum researchers agree that this kind of capability is close.
Indeed, since Tacchino and co did their work, IBM has already made a 16-qubit quantum processor available via the web. It’s only a matter of time before quantum perceptrons become much more powerful.
Ref: arxiv.org/abs/1811.02266 : An Artificial Neuron Implemented on an Actual Quantum Processor
Russia is risking the creation of a “splinternet”—and it could be irreversible
If Russia disconnects from—or is booted from— the internet’s governing bodies, the internet may never be the same again for any of us.
Quantum computing has a hype problem
Quantum computing startups are all the rage, but it’s unclear if they’ll be able to produce anything of use in the near future.
These hackers showed just how easy it is to target critical infrastructure
Two Dutch researchers have won a major hacking championship by hitting the software that runs the world’s power grids, gas pipelines, and more. It was their easiest challenge yet.
Inside the plan to fix America’s never-ending cybersecurity failures
The specter of Russian hackers and an overreliance on voluntary cooperation from the private sector means officials are finally prepared to get tough.
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“I think I can safely say that nobody understands quantum mechanics.”
Richard P. Feynman
Quantum mechanics is the foundation of physics, which underlies chemistry, which is the foundation of biology –> nature. Scientists who wants to simulate nature, biology, chemistry, they need a better way of making calculations that can handle uncertainty. Quantum computing will impact our ability to solve problems that are hard to address by traditional supercomputers. Instead of bits, quantum computers consist of qubits. Quantum mechanics allow qubits to code more information than bits. And without quantum mechanics matter would not exist.
Quantum Computers are particularly good at calculating properties of systems based on quantum mechanical. It includes molecules. Caffeine is a small molecule. It contains protons, neutrons, and electrons. Number of bits required to the molecule and bonds that hold it all together is approximately 10^48 (in case you do not know how big such number is here it is: 10000000000000000000000000000000000000000000000000). Just one molecule!
☕ Cup of coffee contains approximately 95 mg of caffeine – means 2.95 × 10^20 molecules (295000000000000000000 molecules).
Smell your coffee before drinking and reflect that nature handles the single caffeine molecule effectively almost without visible effort. A quantum computer with 160 qubits could make such calculation.
“With quantum computing, we really don’t know what we’re going to be able to solve. The answer is going to surprise us.”
You might be wondering how quantum mechanics is even relevant to businesses today? Quantum computing may provide a new path to solve some of the hardest or most memory intensive problems in business and science. There are 4 categories of problems, quantum computers can solve much better than classical computers:
Encryption and Cybersecurity – Our digital lives rely on cryptography. Current encryption algorithms, like RSA, can be broken, if one can figure out the two prime factors of a number with hundreds of digits. Classical computers would need enormous amount of time to solve it. Algorithm on a quantum computer could quickly calculate the prime numbers used in current encryption schemes Currently, quantum computers are too small and error prone to accomplish this. But it is only matter of time.
Chemistry&Biology Research – Quantum computers could replicate chemical systems to give us new insights into molecules and reactions by simulating how the electrons in the atoms that make up molecules interact with each other. Designing new fertilizers is key in food production. Scientists hope quantum computers will give them a better understanding of this process in the near future and find more energy-efficient ways to make fertilizer.
Optimization Problems (eg. logistics) rather than billions of trillions of individual operations, quantum computing can reduce the most difficult optimization problems down to a number of operations where even a classical computer could find the optimal answer quickly.
Data Analysis – finding patterns is harder as the datasets get larger – and they are getting huge in many scientific fields. Quantum computers offer a fundamentally different and faster way to explore these large datasets and could help solve this important type of problem
Progress of quantum computing is happening fast. There is great progress in developing algorithms that quantum computers may use. But the devices themselves still need a lot more work.
In October 2019, Google’s Californian research lab became the first to achieve “quantum supremacy”, performing a calculation that would be practically impossible for even the most powerful classical supercomputer. The University of Science and Technology of China achieved quantum supremacy only 14 months later, claiming its quantum computer to be 10 billion times faster than Google’s. IBM hopes to have a 1,000-qubit machine by 2023.
The history of quantum computers started in 1935 with EPR Paradox … but everyone can start learning about quantum computers & quantum physics (and what #qubits are) from comics: “The Talk” by Scott Aaronson & Zach Weinersmith. Learning about complex topics in engaging way is essential (especially during pandemic).
In case comics are not for you, there is one book which explained quantum computing without unnecessary difficult terms and advanced math:
“Q is for Quantum” by Terry Rudolph
Part of the trouble with quantum computing is that it involves new weird terms and unknown concepts. Author of that book found a way to explain basic concepts of quantum mechanics in a way it could be understandable for everyone. He presumed readers only to know basic arithmetic. If you would like to try if that book is for you – free first chapter could be downloaded from: https://www.qisforquantum.org
After some theory, there is time to start practicing. It looks like good moment for developers and other IT specialists to start exploring quantum computing. Let’s start with 3 programming languages where you can design and execute quantum circuits:
- Microsoft Q# & Quantum Development Kit – https://docs.microsoft.com/en-us/azure/quantum/overview-what-is-qsharp-and-qdk
- IBM Qiskit – open-source quantum development kit https://qiskit.org
- Google Cirq – Python library for programing quantum computers https://quantumai.google/cirq
All those three are built with user-friendly development environments with sample documentation to help developers start their quantum journey.
Digital transformation would not slow down, new emerging technologies would be adopted across industries. If you want to be ready for next wave of digital transformation, it is a good time to learn some basics about quantum computing.


Researchers at PSI have compared the electron distribution below the oxide layer of two semiconductors. The investigation is part of an effort to develop particularly stable quantum bits –and thus, in turn, particularly efficient quantum computers. They have now published their latest research, which is supported in part by Microsoft, in the scientific journal Advanced Quantum Technologies.
By now, the future of computing is inconceivable without quantum computers. For the most part, these are still in the research phase. They hold the promise of speeding up certain calculations and simulations by orders of magnitude compared to classical computers.
Quantum bits, or qubits for short, form the basis of quantum computers. So-called topological quantum bits are a novel type that might prove to be superior. To find out how these could be created, an international team of researchers has carried out measurements at the Swiss Light Source SLS at PSI.
More stable quantum bits
“Computer bits that follow the laws of quantum mechanics can be achieved in different ways,” explains Niels Schröter, one of the study’s authors. He was a researcher at PSI until April 2021, when he moved to the Max Planck Institute of Microstructure Physics in Halle, Germany. “Most types of qubits unfortunately lose their information quickly; you could say they are forgetful qubits.” There is a technical solution to this: Each qubit is backed up with a system of additional qubits that correct any errors that occur. But this means that the total number of qubits needed for an operational quantum computer quickly rises into the millions.
“Microsoft’s approach, which we are now collaborating on, is quite different,” Schröter continues. “We want to help create a new kind of qubit that is immune to leakage of information. This would allow us to use just a few qubits to achieve a slim, functioning quantum computer.”
The researchers hope to obtain such immunity with so-called topological quantum bits. These would be something completely new that no research group has yet been able to create.
Topological materials became more widely known through the Nobel Prize in Physics in 2016. Topology is originally a field of mathematics that explores, among other things, how geometric objects behave when they are deformed. However, the mathematical language developed for this can also be applied to other physical properties of materials. Quantum bits in topological materials would then be topological qubits.
Quasiparticles in semiconductor nanowires
It is known that thin-film systems of certain semiconductors and superconductors could lead to exotic electron states that would act as such topological qubits. Specifically, ultra-thin, short wires made of a semiconductor material could be considered for this purpose. These have a diameter of only 100 nanometres and are 1,000 nanometres (i.e., 0.0001 centimetres) long. On their outer surface, in the longitudinal direction, the top half of the wires is coated with a thin layer of a superconductor. The rest of the wire is not coated so that a natural oxide layer forms there. Computer simulations for optimising these components predict that the crucial, quantum mechanical electron states are only located at the interface between the semiconductor and the superconductor and not between the semiconductor and its oxide layer.
“The collective, asymmetric distribution of electrons generated in these nanowires can be physically described as so-called quasiparticles,” says Gabriel Aeppli, head of the Photon Science Division at PSI, who was also involved in the current study. “Now, if suitable semiconductor and superconductor materials are chosen, these electrons should give rise to special quasiparticles called Majorana fermions at the ends of the nanowires.”
Majorana fermions are topological states. They could therefore act as information carriers, ergo as quantum bits in a quantum computer. “Over the course of the last decade, recipes to create Majorana fermions have already been studied and refined by research groups around the world,” Aeppli continues. “But to continue with this analogy: we still didn’t know which cooking pot would give us the best results for this recipe.”
Indium antimonide has the advantage
A central concern of the current research project was therefore the comparison of two “cooking pots”. The researchers investigated two different semiconductors and their natural oxide layer: on the one hand indium arsenide and on the other indium antimonide.
At SLS, the PSI researchers used an investigation method called soft X-ray angle-resolved photoelectron spectroscopy – SX-ARPES for short. A novel computer model developed by Noa Marom’s group at Carnegie Mellon University, USA, together with Vladimir Strocov from PSI, was used to interpret the complex experimental data. “The computer models used up to now led to an unmanageably large number of spurious results. With our new method, we can now look at all the results, automatically filter out the physically relevant ones, and properly interpret the experimental outcome,” explains Strocov.
Through their combination of SX-ARPES experiments and computer models, the researchers have now been able to show that indium antimonide has a particularly low electron density below its oxide layer. This would be advantageous for the formation of topological Majorana fermions in the planned nanowires.
“From the point of view of electron distribution under the oxide layer, indium antimonide is therefore better suited than indium arsenide to serve as a carrier material for topological quantum bits,” concludes Niels Schröter. However, he points out that in the search for the best materials for a topological quantum computer, other advantages and disadvantages must certainly be weighed against each other. “Our advanced spectroscopic methods will certainly be instrumental in the quest for the quantum computing materials,” says Strocov. “PSI is currently taking big steps to expand quantum research and engineering in Switzerland, and SLS is an essential part of that.”
Text: Paul Scherrer Institute/Laura Hennemann
The Paul Scherrer Institute PSI develops, builds and operates large, complex research facilities and makes them available to the national and international research community. The institute’s own key research priorities are in the fields of matter and materials, energy and environment and human health. PSI is committed to the training of future generations. Therefore about one quarter of our staff are post-docs, post-graduates or apprentices. Altogether PSI employs 2100 people, thus being the largest research institute in Switzerland. The annual budget amounts to approximately CHF 400 million. PSI is part of the ETH Domain, with the other members being the two Swiss Federal Institutes of Technology, ETH Zurich and EPFL Lausanne, as well as Eawag (Swiss Federal Institute of Aquatic Science and Technology), Empa (Swiss Federal Laboratories for Materials Science and Technology) and WSL (Swiss Federal Institute for Forest, Snow and Landscape Research). (Last updated in May 2020)
- Semiconductors reach the quantum world – press release from 22 December 2021
- Exploring the practical benefits of exotic materials – article from 1 September 2021
- New material also reveals new quasiparticles – press release from 7 May 2019
Dr. Vladimir N. Strocov
Research Group Spectroscopy of Novel Materials
Paul Scherrer Institute, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
Telephone: +41 56 310 53 11, e-mail: email@example.com [English, French, Russian]
Dr. Niels Schröter
Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany
Telephone: +49 345 5582 793, e-mail: firstname.lastname@example.org, email@example.com [German, English]
Prof. Dr. Gabriel Aeppli
Head of the Photon Science Division
Paul Scherrer Institute, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
and Department of Physics, ETH Zurich
and Topological Matter Laboratory, EPF Lausanne
Telephone: +41 56 310 42 32, e-mail: firstname.lastname@example.org [German, English, French]
Electronic structure of InAs and InSb surfaces: density functional theory and angle-resolved photoemission spectroscopy
Shuyang Yang Niels B. M. Schröter, V. N. Strocov, S. Schuwalow, M. Rajpalk, K. Ohtani, P. Krogstrup, G. W. Winkler, J. Gukelberger, D. Gresch, G. Aeppli, R. M. Lutchyn, N. Marom
Advanced Quantum Technologies 20. January 2022


Politicians are often excoriated for changing (or flip-flopping) their stances on various political topics. Changes in position often leads to defeat at the polls. In the field of quantum computing, however, it appears flip-flopping is a great idea. Jeremy Hsu (@jeremyhsu) reports, researchers from the U.S. and Australia have developed a flip-flop qubit that could make the construction of quantum computers much easier. For those unfamiliar with the term “qubit,” it’s shorthand for a quantum bit. In traditional computers, a bit is a piece of information that is either a “1” or a “0.” A qubit has the fascinating property of being able to be simultaneously a “1” and a “0.” This means a quantum computer using a qubit can perform two calculations at once. Watch the following video for a fuller explanation as to why quantum computers are going to be faster than traditional computers.
Qubits are notoriously difficult to create and maintain. Because they operate at the atomic or sub-atomic level, qubits can easily be disrupted (i.e., knocked out of their superposition states). To limit interference, qubits are often created in highly shielded, extremely low temperature environments. In their attempts to find the best qubit, scientists have tried numerous exotic materials. The flip-flop qubit, however, uses silicon. Hsu explains, “Australian and U.S. researchers have developed qubits based on either the nuclear or electron spin state of phosphorus atoms embedded in silicon. Their latest work has yielded the concept of a ‘flip-flop qubit’ that combines both electron and nuclear spin states — an approach that enables neighboring qubits to remain coupled together despite being separated by larger physical distances. In turn, that makes it much easier to build control schemes for the large arrays of qubits necessary for full-fledged quantum computing.” Watch the following video for more information.
“[T]he real challenge when trying to fabricate and operate 100, 1,000, or millions of qubits is how to lay out the classical components, such as interconnects and readout transistors,” Andrea Morello, a quantum physicist at the University of New South Wales, told Hsu. “So, having the qubits spaced out by 200 to 500 nanometers from each other means that we have all that space between them to intersperse all the classical control and readout infrastructure, while using fabrication technologies that are already commonplace in the semiconductor industry.” In other words, Morello believes quantum computers may someday be able to be manufactured in the same way as classical computers. He calls this breakthrough, “A stroke of genius.”
Quantum Computing Storage
Storage of quantum information is another challenge being worked on by scientists. Dominic John Galeon (@domgaleon) reports, “One of the challenges in quantum communications is extending how long entangled particles can hold information. Researchers from the Australian National University may have found a way to do this using erbium crystals.” In a press release, Australian National University (ANU) Research School of Physics associate professor Matthew Sellars stated, “We have shown that an erbium-doped crystal is the perfect material to form the building blocks of a quantum internet that will unlock the full potential of future quantum computers. We had this idea 10 years ago, but many of our peers told us that such a simple idea couldn’t work. Seeing this result, it feels great to know that our approach was the right one.” Kyree Leary (@KyreeLeary) reports the ANU team is not the only team working on quantum storage. “For the first time,” she writes, “researchers [from the California Institute of Technology] have developed nanoscale quantum memory chips that store information in individual photons. The chips were able to store data for 75 nanoseconds before release, with a success rate of 97 percent.” The ANU team might be impressed with the Caltech’s work with photons, but won’t be impressed with storage time of Caltech’s photons. “The ANU team [was] able to successfully store quantum information for 1.3 seconds. That’s a quantum memory that’s 10,000 times longer compared to other efforts. Plus, it eliminates the need for a conversion process since the erbium crystals operate in the same bandwidth as current fiber optic networks.” The Caltech team admits “in order to be a viable component in quantum networking, the chips will need to be able to retain the information for one millisecond.” Brooks Hays reports a team from Yale University has developed a system using sound to store quantum data. She writes, “Scientists have designed a new quantum computer chip that uses sound waves to store and convert quantum data. The device uses a bulk acoustic wave resonator to store, move and translate quantum information embedded in qubits, or quantum bits. The new, simple and more efficient method for quantum data storage could accelerate quantum computing technology.”
Every week new breakthroughs or new ways of doing things are announced in the field of quantum computing. Yet it seems a fully-functioning general-use quantum computer remains elusively out of reach. For some specific types of problems, quantum computers are humanities best hope of finding solutions. Traditional computing methods simply take too long to be workable. Even when one is successfully built and fully functional, don’t expect to see one on your desk. Quantum computers are extremely expensive to build and maintain. Nevertheless, there is a global-wide race to develop the first fully-functional, general-use quantum computer.
 Jeremy Hsu, “Flip-Flop Qubit Could Make Silicon the King of Quantum Computing,” IEEE Spectrum, 13 September 2017.
 Dominic John Galeon, “Scientists Just Successfully Stored Quantum Information 10,000 Times Longer Than Ever Before,” Futurism, 13 September 2017.
 Kyree Leary, “A New Computer Chip Can Store Quantum Information in the Form of Light,” Futurism, 12 September 2017.
 Brooks Hays, “New quantum computer chip uses sounds waves to store data,” UPI, 22 September 2017.


We are getting closer to the most spectacular early quantum algorithm – Shor’s algorithm for factoring large composite numbers which can be used to break the most widely used public key cryptography systems. But before we can tackle this algorithm, there is one more thing that we need to understand – the quantum Fourier transform.
The discrete Fourier transform
Let us leave the quantum world for a few moments and take a look at a classical area in mathematics – the discrete Fourier transform. To motivate the definition to come, let us for a moment assume that we are observing a time dependent signal, i.e. a function f(t) depending on the time t. Let us also assume that we are interested in processing this signal further, using a digital computer. This will force us to reduce the full signal to a finite number of values, i.e. to a set of values f(0), f(1), …, f(N-1) at N discrete points in time.
Now assume that we wanted to calculate the classical Fourier transform of this function, i.e. the function given by (the exact formula depends on a few conventions)
If we now replace the function f by the sum of those functions which have value f(i) over a range of length 1, i.e. by its discrete version, this formula turns into
Now we could code the discrete values f(0), f(1), .. as a sequence xk of numbers, and we could apply the same process to the Fourier transform and replace it by a sequence Xk of numbers, representing again the values at the discrete points 0, 1, …. The above formula then tells us that these numbers would be given by
We could now look at this formula as giving us an abstract mapping from the set of sequences of N numbers to itself, provided by (putting in a factor )
This transformation is called the discrete Fourier transform.
There is a different way to look at this which is useful to derive some of the properties of the discrete Fourier transform. For every k, we can build the N-dimensional complex vector with components by setting
These vectors are mutually orthogonal with respect to the standard hermitian product. In fact, the product of any two of these vectors is given by
If , this is clearly N. Otherwise, we can write this as
If q is not equal to one, i.e. if k is not equal to k’, then, according to the usual formula for a geometric series, this is equal to
However, this is zero, because due to the periodicity of the exponential function.
Using this orthogonal basis, we can write the formula for the discrete Fourier transform simply as the hermitian product
In particular, the discrete Fourier transform can be seen as the linear transformation that maps the k-th unit vector onto the vector . By adding a factor , this can be turned into a unitary transformation, i.e. in a certain sense the discrete Fourier transform is simply a rotation. This relation can also be used to easily derive a formula for the inverse of the discrete Fourier transform. In fact, if we expand the vector x into the basis , we obtain
Fourier transforms of a periodic sequence
For what follows, let us adapt and simplify our notation a bit. First, we add a factor to the formula for the Fourier coefficient, so that the Fourier transform is unitary. Second, we use the symbol to denote the N-th root of unity, i.e.
With this notation, the formula for the Fourier coefficient is then
and the inverse is given by
Let us now study a special case that is highly relevant for the applications to the factoring of large numbers – the Fourier transform of a periodic sequence. Thus, suppose there is a sequence xk and a number r called the period such that
for all values of s and t that lead to valid indices. Thus, roughly speaking, after r indices, the values of the sequence repeat themselves. It is also common to reserve the term period for the smallest number with this property. We call the number the frequency of the sequence.
Let us now assume that the frequency is a natural number, i.e. that the period divides N, and let us try to understand what this means for the Fourier transform. Using the periodicity, we can write the coefficients as follows.
where s runs from 0 to u-1. We can now write the first of the sums as follows.
Now this is again a geometric series with ! We can therefore conclude as above that this is zero unless q is one, i.e. unless k is a multiple of the frequency u. Thus we have shown that if the sequence xk is periodic with integral frequency u, then all Fourier coefficients Xk are zero for which k is not a multiple of the frequency.
In the later applications, this fact will be applied in a situation where we know that a sequence is periodic, but the period is unknown. We will then perform a Fourier transformation and inspect the coefficients Xk. We can then conclude that the unknown frequency u must be a common divisor of all those indices k for which Xk is different from zero. This is exactly true if the period divides N, and we will see later that in important cases, it is still approximately true if the period does not divide N.
A quantum algorithm to compute the discrete Fourier transform
Let us now carry over our considerations to the world of quantum computing. In a quantum computer with n qubits, the Hilbert space of states is N-dimensional with N=2n, and is spanned by the basis . Every vector can be described by the sequence of its coefficients when expanding it into this basis. Consequently, the discrete Fourier transform defines a unitary transformation on the Hilbert by applying the mapping
Now this is a unitary transformation, and as any such transformation, can be implemented by a quantum circuit. We will not go into details on this, see for instance , section 7.8 or section 5.1 of  (but be careful, these authors use a different sign convention for the Fourier transform). The important part, however, is that a quantum Fourier transform for N=2n can be realized with O(n2) quantum gates.
In contrast to this, the best known classical algorithms for computing the discrete Fourier transform are commonly known as fast Fourier transform and require O(Nn) steps. Thus it seems that we have again found a quantum algorithm which is substantially faster than its classical counterpart.
Unfortunately, this is not really true, as we have not yet defined our measurement procedure. In fact, if we measure the result of applying a quantum Fourier transform, we destroy the superposition. In addition, the coefficients in which we are interested are the amplitudes of the possible outcomes, and there is no obvious way to measure them – even if we perform the transformation several times and measure over and over again, we only obtain approximations to the probabilities which are the absolute values of the squared amplitudes, so we do not obtain the phase information. Therefore, it is far from clear whether a quantum algorithm can help to compute the Fourier transform more efficiently than it is possible with a classical algorithm.
However, most applications of the Fourier transform in quantum algorithms are indirect, using the transform as a sort of amplification step to focus amplitudes on interesting states. In the next post, we will look at Shor’s algorithm with exploits the periodicity properties of the Fourier transform to factor large composite numbers.


- Introduction & Top Questions
- Electromagnetic waves and the electromagnetic spectrum
The first two decades of the 20th century left the status of the nature of light confused. That light is a wave phenomenon was indisputable: there were countless examples of interference effects—the signature of waves—and a well-developed electromagnetic wave theory. However, there was also undeniable evidence that light consists of a collection of particles with well-defined energies and momenta. This paradoxical wave-particle duality was soon seen to be shared by all elements of the material world.
In 1923 the French physicist Louis de Broglie suggested that wave-particle duality is a feature common to light and all matter. In direct analogy to photons, de Broglie proposed that electrons with momentum p should exhibit wave properties with an associated wavelength λ = h/p. Four years later, de Broglie’s hypothesis of matter waves, or de Broglie waves, was experimentally confirmed by Clinton Davisson and Lester Germer at Bell Laboratories with their observation of electron diffraction effects.
A radically new mathematical framework for describing the microscopic world, incorporating de Broglie’s hypothesis, was formulated in 1926–27 by the German physicist Werner Heisenberg and the Austrian physicist Erwin Schrödinger, among others. In quantum mechanics, the dominant theory of 20th-century physics, the Newtonian notion of a classical particle with a well-defined trajectory is replaced by the wave function, a nonlocalized function of space and time. The interpretation of the wave function, originally suggested by the German physicist Max Born, is statistical—the wave function provides the means for calculating the probability of finding a particle at any point in space. When a measurement is made to detect a particle, it always appears as pointlike, and its position immediately after the measurement is well defined. But before a measurement is made, or between successive measurements, the particle’s position is not well defined; instead, the state of the particle is specified by its evolving wave function.
The quantum mechanics embodied in the 1926–27 formulation is nonrelativistic—that is, it applies only to particles whose speeds are significantly less than the speed of light. The quantum mechanical description of light was not fully realized until the late 1940s (see below Quantum electrodynamics). However, light and matter share a common central feature—a complementary relation between wave and particle aspects—that can be illustrated without resorting to the formalisms of relativistic quantum mechanics.
The same interference pattern demonstrated in Young’s double-slit experiment is produced when a beam of matter, such as electrons, impinges on a double-slit apparatus. Concentrating on light, the interference pattern clearly demonstrates its wave properties. But what of its particle properties? Can an individual photon be followed through the two-slit apparatus, and if so, what is the origin of the resulting interference pattern? The superposition of two waves, one passing through each slit, produces the pattern in Young’s apparatus. Yet, if light is considered a collection of particle-like photons, each can pass only through one slit or the other. Soon after Einstein’s photon hypothesis in 1905, it was suggested that the two-slit interference pattern might be caused by the interaction of photons that passed through different slits. This interpretation was ruled out in 1909 when the English physicist Geoffrey Taylor reported a diffraction pattern in the shadow of a needle recorded on a photographic plate exposed to a very weak light source, weak enough that only one photon could be present in the apparatus at any one time. Photons were not interfering with one another; each photon was contributing to the diffraction pattern on its own.
In modern versions of this two-slit interference experiment, the photographic plate is replaced with a detector that is capable of recording the arrival of individual photons. Each photon arrives whole and intact at one point on the detector. It is impossible to predict the arrival position of any one photon, but the cumulative effect of many independent photon impacts on the detector results in the gradual buildup of an interference pattern. The magnitude of the classical interference pattern at any one point is therefore a measure of the probability of any one photon’s arriving at that point. The interpretation of this seemingly paradoxical behaviour (shared by light and matter), which is in fact predicted by the laws of quantum mechanics, has been debated by the scientific community since its discovery more than 100 years ago. The American physicist Richard Feynman summarized the situation in 1965:
We choose to examine a phenomenon which is impossible, absolutely impossible, to explain in any classical way, and which has in it the heart of quantum mechanics. In reality, it contains the only mystery.
In a wholly unexpected fashion, quantum mechanics resolved the long wave-particle debate over the nature of light by rejecting both models. The behaviour of light cannot be fully accounted for by a classical wave model or by a classical particle model. These pictures are useful in their respective regimes, but ultimately they are approximate, complementary descriptions of an underlying reality that is described quantum mechanically.
Quantum optics, the study and application of the quantum interactions of light with matter, is an active and expanding field of experiment and theory. Progress in the development of light sources and detection techniques since the early 1980s has allowed increasingly sophisticated optical tests of the foundations of quantum mechanics. Basic quantum effects such as single photon interference, along with more esoteric issues such as the meaning of the measurement process, have been more clearly elucidated. Entangled states of two or more photons with highly correlated properties (such as polarization direction) have been generated and used to test the fundamental issue of nonlocality in quantum mechanics (see quantum mechanics: Paradox of Einstein, Podolsky, and Rosen). Novel technological applications of quantum optics are also under study, including quantum cryptography and quantum computing.


Researchers from MIT and elsewhere have recorded, for the first time, the “temporal coherence” of a graphene qubit—meaning how long it can maintain a special state that allows it to represent two logical states simultaneously. The demonstration, which used a new kind of graphene-based qubit, represents a critical step forward for practical quantum computing, the researchers say.
Superconducting quantum bits (simply, qubits) are artificial atoms that use various methods to produce bits of quantum information, the fundamental component of quantum computers. Similar to traditional binary circuits in computers, qubits can maintain one of two states corresponding to the classic binary bits, a 0 or 1. But these qubits can also be a superposition of both states simultaneously, which could allow quantum computers to solve complex problems that are practically impossible for traditional computers.
The amount of time that these qubits stay in this superposition state is referred to as their “coherence time.” The longer the coherence time, the greater the ability for the qubit to compute complex problems.
Recently, researchers have been incorporating graphene-based materials into superconducting quantum computing devices, which promise faster, more efficient computing, among other perks. Until now, however, there’s been no recorded coherence for these advanced qubits, so there’s no knowing if they’re feasible for practical quantum computing.
In a paper published today in Nature Nanotechnology, the researchers demonstrate, for the first time, a coherent qubit made from graphene and exotic materials. These materials enable the qubit to change states through voltage, much like transistors in today’s traditional computer chips—and unlike most other types of superconducting qubits. Moreover, the researchers put a number to that coherence, clocking it at 55 nanoseconds, before the qubit returns to its ground state.
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The work combined expertise from co-authors William D. Oliver, a physics professor of the practice and Lincoln Laboratory Fellow whose work focuses on quantum computing systems, and Pablo Jarillo-Herrero, the Cecil and Ida Green Professor of Physics at MIT who researches innovations in graphene.
“Our motivation is to use the unique properties of graphene to improve the performance of superconducting qubits,” says first author Joel I-Jan Wang, a postdoc in Oliver’s group in the Research Laboratory of Electronics (RLE) at MIT. “In this work, we show for the first time that a superconducting qubit made from graphene is temporally quantum coherent, a key requisite for building more sophisticated quantum circuits. Ours is the first device to show a measurable coherence time—a primary metric of a qubit—that’s long enough for humans to control.”
There are 14 other co-authors, including Daniel Rodan-Legrain, a graduate student in Jarillo-Herrero’s group who contributed equally to the work with Wang; MIT researchers from RLE, the Department of Physics, the Department of Electrical Engineering and Computer Science, and Lincoln Laboratory; and researchers from the Laboratory of Irradiated Solids at the École Polytechnique and the Advanced Materials Laboratory of the National Institute for Materials Science.
A pristine graphene sandwich
Superconducting qubits rely on a structure known as a “Josephson junction,” where an insulator (usually an oxide) is sandwiched between two superconducting materials (usually aluminum). In traditional tunable qubit designs, a current loop creates a small magnetic field that causes electrons to hop back and forth between the superconducting materials, causing the qubit to switch states.
But this flowing current consumes a lot of energy and causes other issues. Recently, a few research groups have replaced the insulator with graphene, an atom-thick layer of carbon that’s inexpensive to mass produce and has unique properties that might enable faster, more efficient computation.
To fabricate their qubit, the researchers turned to a class of materials, called van der Waals materials—atomic-thin materials that can be stacked like Legos on top of one another, with little to no resistance or damage. These materials can be stacked in specific ways to create various electronic systems. Despite their near-flawless surface quality, only a few research groups have ever applied van der Waals materials to quantum circuits, and none have previously been shown to exhibit temporal coherence.
For their Josephson junction, the researchers sandwiched a sheet of graphene in between the two layers of a van der Waals insulator called hexagonal boron nitride (hBN). Importantly, graphene takes on the superconductivity of the superconducting materials it touches. The selected van der Waals materials can be made to usher electrons around using voltage, instead of the traditional current-based magnetic field. Therefore, so can the graphene—and so can the entire qubit.
When voltage gets applied to the qubit, electrons bounce back and forth between two superconducting leads connected by graphene, changing the qubit from ground (0) to excited or superposition state (1). The bottom hBN layer serves as a substrate to host the graphene. The top hBN layer encapsulates the graphene, protecting it from any contamination. Because the materials are so pristine, the traveling electrons never interact with defects. This represents the ideal “ballistic transport” for qubits, where a majority of electrons move from one superconducting lead to another without scattering with impurities, making a quick, precise change of states.
How voltage helps
The work can help tackle the qubit “scaling problem,” Wang says. Currently, only about 1,000 qubits can fit on a single chip. Having qubits controlled by voltage will be especially important as millions of qubits start being crammed on a single chip. “Without voltage control, you’ll also need thousands or millions of current loops too, and that takes up a lot of space and leads to energy dissipation,” he says.
Additionally, voltage control means greater efficiency and a more localized, precise targeting of individual qubits on a chip, without “cross talk.” That happens when a little bit of the magnetic field created by the current interferes with a qubit it’s not targeting, causing computation problems.
For now, the researchers’ qubit has a brief lifetime. For reference, conventional superconducting qubits that hold promise for practical application have documented coherence times of a few tens of microseconds, a few hundred times greater than the researchers’ qubit.
But the researchers are already addressing several issues that cause this short lifetime, most of which require structural modifications. They’re also using their new coherence-probing method to further investigate how electrons move ballistically around the qubits, with aims of extending the coherence of qubits in general.
Massachusetts Institute of Technology
Joel I-Jan Wang et al. Coherent control of a hybrid superconducting circuit made with graphene-based van der Waals heterostructures, Nature Nanotechnology (2018). DOI: 10.1038/s41565-018-0329-2
This visualisation shows layers of graphene used for membranes
Credit: University of Manchester