A new light source for quantum computers
20.02.13 - Researchers have discovered a new way of emitting photons one at a time. They have constructed semiconductor nanowires with "quantum dots" of unprecedented quality - a discovery with implications for the future of quantum computing.
In a future of quantum computing, data will be treated and transmitted by lasers. The quantum properties of light will endow machines with gigantic computing potential and an incredible execution rate. However, much work remains to be done. In order to exploit the "quantum" potential of light it is necessary, among other things, to be able easily to emit photons one by one.
The "natural" creation of a photon filter
At the heart of the Laboratory of Semiconductor Materials (LMSC) of Institute of Materials, the team of Anna Fontcuberta i Morral has discovered a new method for creating a miniscule and extremely high-performance single-photon source. She has found that "quantum dots", or nanocrystals, appear naturally on a certain kind of semiconductor nanowire during their fabrication process. The final structure can then emit photos one by one, after having absorbed light. Her discovery is the subject of an article in Nature materials.
The hidden qualities of nanowires
Nanowires, with a diametre of around a millionth of a millimetre (between 20 and 100 nanometres) are very efficient at absorbing and manipulating light. By endowing them with nanocrystals or "quantum dots" it is possible to make them emit unique photons, by charging them with a laser beam of a particular frequency.
The only hitch is that generating quantum dots on a nanowire is notoriously difficult. The existing methods, which involve the use of a regular modulation of the composition of the nanowire all along its length, are hard to reproduce and result in strucures with a relatively low output of photons.
Discovery through observation
Scientists at the LMSC discovered that perfectly functional quantum dots formed "naturally" on the surface of certain nanowires during the fabrication process. These dots appeared all by themselves at the interface between two basic components: Gallium Arsenide (GaAs) and Aluminium Arsenide (AlAs). "No doubt many scientists working on nanowires have created dots, without realising it," states Anna Fontcuberta i Morral.
Adjusting nanocristals for size
Many tests have been carried out in the light of this discovery, in order to prove the efficiency of this new single-photon source. "The calculations and simulations were carried out on the supercomputers of EPFL by the Laboratory of the Theory and Simulation of Materials (THEOS) of Nicola Marzari," says Prof. Morral. As a result, these structures showed a great working stability, which is rare when talking about nanotechnology. What is more they are hard wearing and very bright, which means that their rate of photon output is incredibly high. Even better, by controlling the fabrication of the nanowires, the size of the dots can be modulated and adapted to measure. The wavelength of the emitted photons, which is directly dependent on the size of the dots, can therefore be changed. It is then possible for the nanowire to receive a laser beam of a certain wavelength or "colour", in order to generate photons of a certain colour - infrared, for example.
A hitherto unexplained phenomenon
At the present time the phenomenon of the natural creation of dots is not understood by scientists. The study will therefore proceed in the following way: "It is also about seeing if it is possible to stimulate dots not only with lasers but electrically, in order to make them as compatible as we can with all kinds of machine," explains Anna Fontcuberta i Morral.
It is worth noting that these photon sources could also be used in the domain of molecule detection, or for the perfection of methods of quantum encryption for the protection of data.
* * *
In a traditional computer, calculations are based on the "bit", which can have one of two values: 0 or 1. This is the essence of binary language. In a quantum computer the "qbit" (for example a photon) can have several states at once, states of superposition. It can be either 0 or 1 or both at the same time. The aim is maintain photons in their state of superposition so that the computer can carry out multiple calculations in parallel, simultaneously, which will drastically increase the speed of data manipulation. However this capacity for finding several states at once can not be achieved with a photon unless it is isolated: sources of unique photons are therefore much sought after.
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Java in Soft Computing
The human senses interpret perceived external information, which is incomplete and imprecise, and try to form reasoning vital for survival. Fuzzy set theory provides a system to deal with such information linguistically and performs numerical computation using linguistic labels stipulated by membership functions. Selection of fuzzy if-then rules forms the key component of a fuzzy inference systems (FIS) that can appropriately model human expertise in a specific application. FIS has a structured knowledge representation in the form of if-then rules. FIS lacks the adaptability to deal with changing external environments, thus when FIS is incorporated with Neural Network or Evolutionary Computation (such as Genetic Algorithms), the resulting hybrid system is adaptive.
According to Lotfi A. Zadeh, the founder of Fuzzy Set and Fuzzy Logic: "Soft Computing is an emerging approach to computing which parallels the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision."
The major components of Soft Computing are:
- Fuzzy Set and Fuzzy Logic -- the subject of this discussion.
- Artificial Neural Network -- This is the modeling of the brain as a continuous-time non-linear dynamic system in connectionist architectures that are expected to mimic brain mechanisms to simulate intelligent behavior.
- Evolutionary Computation -- Simulating complex biological evolutionary processes lead to understanding of how living systems acquired higher-level intelligence. Genetic Algorithm (GA) is based on the evolutionary principle of natural selection. Immune Modeling is based on the assumption that chemical and physical laws may be able to explain living intelligence. Artificial Life is a similar discipline to Immune Modeling but also attempts to realize lifelike behavior by imitating the processes that occur in the development of life.
- Bayesian Learning and Statistical Reasoning -- Bayesian reasoning is an approach that provides a probabilistic nature to inference. A Bayes model is based on the assumption that the quantities of interest are governed by probability distributions and that optimal decisions can be made by reasoning about these probabilities together with observed data. Bayesian reasoning provides the basis for learning algorithms that directly manipulate probabilities, as well as framework for analyzing the operation of other algorithms that do not explicitly manipulate probabilities.
The field of Soft Computing is changing and evolving rapidly with new techniques and applications constantly proposed. Although software can be developed in either one of the individual components of soft computing, there is a tendency to combine two or more components so that one will complement the shortfall of the other. Hybrid systems such as Neuro-Fuzzy (Neural Net and Fuzzy Systems), Genetic Neural Network ( Neural Net and Genetic Algorithm ), Fuzzy-Bayesian Network (Fuzzy-Logic and Bayesian Belief Network) are common these days.
Where Is Java Now in Computational Intelligence and Soft Computing?
Java has become very popular as a language for writing software in computational and machine intelligence these days. Java is moving fast to be on a plane with traditional artificial intelligence languages such as Lisp and Prolog to be the first choice for writing AI-based software. There is currently an important draft at the Java Community Process (JCP), Java Specification Request 73, an API for data-mining. The proposed name for this package is javax.datamining, but it has yet not been finalized. The specification lead for this expert group is from Oracle, and it is excellent to see leaders in statistical software such as SPSS and the SAS Institute get involved in drafting this specification.
What is data-mining? The main goal of data-mining is to automate the extraction of hidden predictive information and patterns from (large) databases. Data-mining applies the algorithm of machine learning (computational intelligence) and soft-computing such as artificial neural network, decision trees and belief networks, fuzzy-logic if-then rules, and rule induction. There has been confusion about the meaning of data-mining among the IT community. It is not data warehousing, SQL-queries, and report or data visualization.
Data-mining is a major component of today's enterprise software, such as ERP (Enterprise Resource Planning) and CRM (Customer Relational Management). The expert comment from publications in Intelligent Enterprise (a Web site for enterprise business intelligence) predicts that business intelligence enterprise software such as CRM that does not have analytical functionality will not compete well in the market. A CRM with analytical capability that only has statistical analysis is not as good as one with both statistical plus soft-computing and computational intelligence.
The underlying algorithm of data-mining involves number crunching numeric computation, and it's a good move by Sun to develop such an API to make software development easier for mid-level or even entry-level Java developers who need to be involved in a data-mining project. Java developers just need to understand, by reading the API docs, what parameters need to be passed to a specific method, which removes the need to understand the complex mathematics implemented in a data-mining algorithm.
Data-mining projects have always involved people who have a background and a deep knowledge of mathematics, statistics, and artificial intelligence at the Ph.D. or M.Sc. level. The upcoming javax.datamining API package will pull in Java developers from all levels, expert down to entry level for any data-mining project. Thus, one mathematician is enough to lead a group, which eliminates the need to assemble a team of Ph.D. developers. There are already a number of freeware Java software and APIs in soft computing and machine learning available online as GPL open source, with a new one available almost daily. This shows the explosive popularity of Java in the field of machine intelligence and soft computing.
Evolution of Logic
The following are the different types of logic and their implications or potential applications for technology:
- Bivalent Logic: This is the conventional "either-or", "true-false" formulated by Aristotle; it is logic of our modern day computers. A logic gate output can be either 1 or 0 and there is no number in the middle such as 0.7. There is no such thing as uncertainty or imprecision in Bivalent Logic.
- Multi-valued Logic (Fuzzy Set and Fuzzy Logic): Although modern computers and software operate using bivalent logic, it is inefficient in modeling human concepts, which tend to be imprecise and uncertain. Fuzzy Logic allows logic values to have any value between 0 and 1. ("X is a beautiful person. Y is more beautiful than X. Z is very, very beautiful.")
- Quantum Logic: It is quite different from bivalent and fuzzy logic in that the truth-values interfere with each other, leading to co-existence at the same time of different values. A quantum logic gate can exist in both states at the same time or even more states concurrently. Quantum Computation explores massive parallel computing. What has used to be science fiction decades ago now becomes science fact in today's technology. Peter Shor of AT&T invented the "Shor" quantum algorithm in 1994 and showed that factoring a large integer (400-digit number or more) into prime numbers can be done very fast using a quantum computer (around 1 year time span) in comparison with billions of years using the fastest super-computer of today. Since the emergence of Shor algorithm, financial institutions and government agencies, such as the N.S.A, are aware of the potential threat of this technology. It is no surprise that the U.S. government is at the forefront of research into quantum cryptography and encryption. Even a working group at Microsoft has been established to research this alternative model of computing. When the age of Quantum Computing matures, branches of software engineering such as Data Warehousing will become obsolete, because quantum computers will do searches of millions or even billions of database records and produce reports in a matter of seconds.
Speed is the limitation of the application of machine intelligence and soft computing in today's computers. One day, quantum computing will solve this limitation. In the field of Computer Vision (software that is trained to recognize the difference between a bicycle and a tree from an image, for example), today's computer are not yet fast enough to recognize figures from an image. The pattern matching of current vision technology is reasonable if the number of images to be matched is reasonably low. When the search is to be done on a massive image database, the retrieval process is going to be slow. Java is fast establishing itself in all areas of technical computation from scientific and engineering to business. With the release of Java Advanced Imaging plus Java3D, I have seen Java GPL projects that use soft computing and Computer Vision for scientific and medical imaging.
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secrets ride phone lines
Technology Research News
The ability to safeguard secret messages
using the quirks of quantum physics has been thoroughly demonstrated in
the laboratory. Now field tests of quantum cryptography are showing that
the technology can withstand the rigors of real-world communications.
Researchers in Switzerland have used this type of cryptography,
which represents bits of information using single photons, to send theoretically
perfectly secure messages between the cities of Geneva and Lausanne, which
are 67 kilometers apart.
Quantum cryptography provides perfect security because it allows users
to tell for sure whether the key they are using to encrypt and decrypt
a message has been compromised.
Researchers at Los Alamos National Laboratory previously proved that a
quantum signal could travel 50 kilometers. But that was over a spooled
fiber-optic line contained in a laboratory, said Nicolas Gisin, a physics
professor at the University of Geneva. "In our case the two end points
were really spatially separated," he said.
More importantly, the Swiss experiment used existing fiber-optic phone
lines. The fibers were "nothing special," said Gisin. They were not in
commercial use during the experiment, but were part of a cable containing
many fibers that were, he said.
Key encryption schemes use a unique mathematical key to mask each message.
The sender and intended recipient use the key to encrypt a message, send
it over unsecured channels, then decrypt it. The trick to keeping the
message secret is making sure no one but the sender and receiver have
access to the key.
The quantum cryptography scheme sends encryption keys over fiber-optic
lines in a perfectly secure way by representing each bit with only one
photon. Using two or more photons per bit makes it possible for an eavesdropper
to siphon off some extra photons in order to peek at the key without being
detected. Using only one photon per bit means that an eavesdropper would
have to replace the photons she intercepted, but it is impossible to replicate
all of the photons correctly.
This is because any given photon, or particle of light, can have one or
more attributes, including polarization, which has to do with how the
photon vibrates, and wave phase.
The researchers' quantum cryptography scheme generates photons in one
of four states based on their wave phases. The system splits each photon,
sends the halves down short pieces of fiber of slightly different lengths,
and then joins the two halves. Because the halves travel different distances,
their waves are out of phase, meaning the crests and troughs are out of
sync by a particular amount.
The photons' four phase states come in two types: those whose waves match
or are exactly opposite, and those whose waves are half way out of phase
with one wave ahead of the other. Each type can be used to represent the
1s and 0s of digital information.
It is a quirk of quantum physics -- the Heisenberg uncertainty principle
-- that makes the scheme perfectly secure: you can't look for both of
the pairs of states at the same time, and you only get one look before
the photon disappears. If you measure a photon to see if it is a 1 or
0 based on one pair of states, but it was generated in one of the other
two states, you're out of luck. Your measuring device has absorbed the
photon during your first look so you will never know whether it represented
a 1 or 0.
This means an eavesdropper would only be able to correctly measure half
of the photons he intercepts and would have to guess at the other half
to produce substitutes. And he would only get about half the missing half
right by chance, meaning one quarter of the substitute bits would be wrong.
The sender and receiver can check the error rate and so detect the eavesdropper
by comparing a few bits. If the key has been compromised, they can throw
it out and send another until they get an uncompromised key to encrypt
their data. To form a key, the receiver measures the photons by randomly
picking one of the two sets of states. Then they compare notes and the
sender tells the receiver which photons he measured correctly. They then
use those bits as the key.
The researchers' quantum key distribution system can only be used across
relatively short distances because its performance drops off as the distance
increases. At 10 kilometers the system can transmit quantum keys at 4,000
bits per second. At 20 kilometers the bit rate drops to 1,500 per second,
and at 50 kilometers it drops to 100 bits per second. An ordinary modem
transmits 56,000 bits per second. Once the users have an uncompromised
key, however, the encrypted data can be sent over fast communications
lines that include repeaters.
Today's fiber-optic communication systems compensate for diminishing signal
strength -- and thus span great distances -- by using repeaters, which
copy and retransmit fading light pulses. Repeaters can't be used to send
quantum keys because they would intercept photons in the same manner as
The company id Quantique in Geneva, a spinoff from Gisin's laboratory,
is marketing the quantum key distribution system. It consists of a pair
of 18-inch-wide boxes that connect to personal computers via USB ports,
and to each other over a fiber-optic line.
Gisin's research colleagues were Damien Stucki and Hugo Zbinden of the
University of Geneva, and the Olivier Guinnard and Grégoire Ribordy of
id Quantique SA. They published the research in the July 12, 2002 issue
of the journal New Journal of Physics. The research was funded by the
TRN Categories: Quantum Computing and Communications; Cryptography
Story Type: News
Related Elements: Technical paper, "Quantum Key distribution
over 67 km with a plug & play system," New Journal of Physics, July 12,
Ultimate memory demoed
makes bugs sing
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For sending information across continents and around the globe, light is the medium of choice. The ability to send multiple wavelengths at high speeds within fibers has transformed communications. But light could do even better, much better, if it weren’t hobbled by the electronic switches, routers and other devices of optical communications technology.
Since they operate by converting optical signals to electronics and back again, these devices considerably reduce the efficiency of current optical networks. Is it possible to create all-optical circuitry — something analogous to the microcircuitry of “chips” but that doesn’t require converting light to electrical current? It’s a challenge many scientists worldwide are addressing.
Shanhui Fan and Fatih Yanik, Stanford University
Using LeMieux, PSC’s terascale system, to simulate how light behaves, applied physicist Shanhui Fan of Stanford and graduate student Mehmet Fatih Yanik have made notable progress. Using all 3,000 LeMieux processors, they showed that it’s possible to stop light and hold it captured — in an optical holding cell — until a subtle shift in optical features releases it. Unlike earlier attempts to capture light, their finding — reported in 2004 — suggests it may be possible to corral complicated light pulses and, moreover, to do it in a way that integrates easily with existing chip technology.
So far, Yanik and Fan’s device exists only in simulation, but they have teamed with a laboratory group at Stanford to build and demonstrate their scheme. Because of the powerful ability of their simulations to accurately predict how light behaves in fascinating materials called “photonic crystals,” the researchers are confident the laboratory work will yield an all-optical device to stop light in its tracks.
It made news in 2001 when researchers brought light to a standstill for the first time. Two groups at Harvard demonstrated a technique that captured light in clouds of gaseous atoms. But these systems of atomic gases are impractical for an all-optical circuit.
Rather than gases, the Stanford team’s approach relies on photonic crystals — layered materials, often silicon or other semiconductors, made with cavities in patterns within the crystal. Because such a device will operate at room temperature and be only microns in length, it could easily integrate with traditional microcircuitry.
By careful design of irregularities in the patterns of the cavities, photonic crystals can allow — or forbid — the passage of certain wavelengths of light. This handy trick makes them attractive filters, with the potential to act as gatekeepers that allow only selected wavelengths to pass through the crystal on prescribed paths. Exactly which wavelength, or band of wavelengths, can travel through or not depends on the properties of the crystal.
Yanik stumbled on the light-stopping mechanism while using LeMieux to simulate the impact of changing one property of a crystal, the index of refraction — the ratio of light’s speed in a vacuum (well established at 186,000 miles per second) to its speed in a medium, where it travels more slowly. His original goal was a tunable switch — a crystal that could be prompted, by small changes in the refractive index, to allow safe passage to different wavelengths of light.
This graphic from simulation shows snapshots of the positive (red) and negative (blue) electric fields as an optical pulse propagates (left to right) through a photonic crystal, shown in three segments at four times (top to bottom). Resonant frequencies of the cavities (black dots) are tuned to stop the pulse during the time interval shown in the second and third snapshots, until the cavities are detuned and the pulse is released.
For one possible design of such a switch, the simulations indicated the effect could be quite strong. Small changes in refractive index allowed a large change in the bandwidth of allowed wavelengths. And that wasn’t all. “I saw an optical signature very similar to the ones observed in atomic media,” says Yanik. “So the question became, could we use the cavities in the crystal to store electromagnetic pulses, just as they were stored in atomic media? If somehow we could get light into this structure, and then change the properties of the entire structure while the light was inside, we could change the properties of light as well and trap it.”
The idea depends on a phenomenon called optical resonance, which is similar to why long and short pipes in an organ produce notes of different frequency. In an organ, each pipe is cut to the length required to amplify sound waves of a desired frequency. The sound energy bounces back and forth inside the pipe and establishes an unmoving wave pattern, or resonance, at the desired frequency. In the Stanford team’s approach, the role of the organ pipe is played by a waveguide — either an empty channel or closely spaced cavities inside the crystal that allow light to propagate.
Prior to this work, many groups had used optical resonators to trap light of a single wavelength. Optical communication, however, uses light pulses to encode and transmit information, with each pulse composed of many wavelengths. Trapping such a multi-wavelength pulse in a single resonator would lose the information carried by the pulse.
Yanik and Fan’s idea, however, goes a crucial step further by tuning all of the wavelengths within a pulse to the same frequency and, at the same time, adjusting the crystal to resonate at that frequency. They do this by adjusting the index of refraction once the pulse has entered the crystal. As all the frequency components are collapsed to a single frequency, the information becomes encoded by the phase and intensity of light along the waveguide.
Changing the resonance of the crystal, Yanik explains, is like adjusting the spacing of stepping stones across a river. Shifting the crystal’s index of refraction is similar to spreading the stones out, so that photons — the tiniest energy chunks of light — of a particular frequency can no longer hop from stone to stone. They have been trapped. When the pulse needs to be released, the index of refraction is shifted back, the stones move closer together, and the photons zip away.
“The entire idea,” says Yanik, “from refractive-index switches to light-trapping devices, was first realized on a supercomputer.” Once he and Fan identified the light-stopping possibility, Yanik adapted software he’d already written to simulate it. Using almost every one of LeMieux’s 3,000 processors, they simulated a series of possibilities until arriving at a 100-micron waveguide with 120 side-cavities.
“A hundred microns,” says Fan, “fits on a chip, a small distance in practice, but a long distance to simulate.” The beauty of photonics simulations, he explains, is the ability to use the full form of Maxwell’s equations. This set of four equations, named for James Clerk Maxwell, a 19th century Scottish physicist, governs most optical and electromagnetic phenomena. Not so long ago, notes Fan, limitations in computing technology required clever approximations to apply these equations.
“With a system like LeMieux,” he says, “we have the ability to solve the entire set exactly.” This means that the computational experiments precisely mimic physical reality and give the researchers high confidence that their predictions can be realized in the laboratory.
To exploit the large-scale parallelism of LeMieux’s 3,000 processors, Yanik’s software parceled separate parts of the crystal waveguide to separate processors. It took 10 simulations to describe the light-trapping behavior, with each simulation of a light pulse entering the wave guide requiring two hours, which Yanik estimates as a year’s worth of computing on a desktop PC.
The simulations showed that shifting the index of refraction around the pulse forces the wavelengths to adopt a single frequency, and traps the pulse in and between cavities. In the 100 micron, 120 side-cavity waveguide, a 1/10,000th shift in the index of refraction is enough to capture the information in commonly used pulses of light.
Another surprising result of the simulations, says Fan, is that if the index of refraction were tuned beyond the point where the light pulse screeches to a halt, the pulse would not merely stop, but reverse in its tracks, backing out of the crystal as though it were a train reversing direction to re-emerge, caboose first, from a tunnel. This time reversal effect, he says, might prove useful in repairing signal degradation.
Efforts to build the device in the lab, in collaboration with Stanford colleagues Martin Fejer and James Harris, are now running parallel to more simulations. “What we’ve done so far is a two-dimensional simulation,” says Fan, “as a proof of principle. We are now extending it to a three-dimensional simulation to arrive at the exact structure the device needs to take.”
For optical networks, a device that can catch and hold light for an arbitrary length of time offers promise to alleviate the congestion that happens when too many pulses arrive simultaneously at a network junction. Beyond that, there’s the promise of quantum computing, the vision of transistors that manipulate single photons rather than electrons. It’s a future, perhaps sooner than we think, in which circuits will be a thousand times smaller and faster. Yanik and Fan’s simulations with LeMieux bring us a step closer.


The demand for faster Internet speeds now and in the future is a given, specifically when considering the popularity of streaming services such as Netflix and the explosion of the Internet of things. Specifically, with the increasing trend to utilise big data sets to systematically extract and analyse information, faster Internet and more broadband are in demand. Thanks to the latest fibre optic technology, for some fortunate users, broadband speeds could soon be significantly faster than anything today.
Researchers from the Royal Melbourne Institute of Technology (RMIT) in Australia, developed a nanophotonic device that can encode more data, and process it incredibly fast using a special form of ‘twisted’ light. The technology comprises a miniscule detector which replaces current readers as big as ‘dining tables’.
This new development in fibre optics involves detecting light that has been twisted which could result in Internet speeds up to 100 times faster. The scientists, who published the results in the journal Nature Communications, indicate that the technology can be used to upgrade existing networks and significantly boost efficiency.
Existing broadband fibre optics carry information on pulses at the speed of light, but the encoding and decoding of data affects data speeds. Fibre optic cables transmit information as pulses of light, but it can only be stored through the colour of the light consisting of horizontal or vertical waves. However, by twisting light into a spiral, a third dimension of light to carry information is created. This is referred to as the level of orbital angular momentum, or spin. Min Gu from RMIT states: “It’s like DNA, if you look at the double helix spiral,” “The more you can use angular momentum the more information you can carry.”
The technology thus uses the oscillation, or shape, of light waves to encode data by making use of light invisible to the naked eye thereby increasing bandwidth. The light waves twisted into a spiral is known as light in a state of orbital angular momentum (OAM). According to Gu, the detector can also be used to receive quantum information sent via twisting light, meaning it could have applications in a whole range of cutting-edge quantum communications and quantum computing research.
While researchers in the US had created a fibre that could twist light previously, Gu’s team is the first to create a detector that can read the information it holds. “We could produce the first chip that could detect this twisting and display it for mobile application,” Gu said. The nanophotonic device can encode more data, and process it incredibly fast using a special form of ‘twisted’ light to unlock super-fast, ultra-broadband communications.
The nanophotonic device is required to overcome the “capacity crunch” of current fibre optic technology, which according to Dr Haoran Ren from RMIT’s School of Science, co-lead author of the paper, “fail to keep up with the ever-increasing demands of Big Data.” Ren said, “Our miniature OAM nano-electronic detector is designed to separate different OAM light states in a continuous order and to decode the information carried by twisted light.” Gu also estimates that the nano-electronic device will unlock the full potential of twisted light for future optical and quantum communications.”
Prof Min Gu from RMIT indicated that the technology would be compatible with existing silicon-based materials and can thus be applied to broadband networks. “This technology’s high performance, low cost and tiny size makes it a viable application for the next generation of broadband optical communications,” he said. He further stated that “It fits the scale of existing fibre technology and could be applied to increase the bandwidth, or potentially the processing speed, of that fibre by over 100 times within the next couple of years. This easy scalability and the massive impact it will have on telecommunications is what’s so exciting.” The OAM nano-electronic detector can be compared to an ‘eye’ that can ‘see’ information carried by twisted light and decode it to be understood by electronics. Gu said that this technology’s high performance, low cost and tiny size makes it a viable application for the next generation of broadband optical communications.
Despite the possibility that the technology could be used to upgrade fibre optic networks, the use of fibre optics instead of copper wire is still contentious. Many households receive the cheaper option of fibre to the node which produces slower speed. With fibre to the node, optic fibre cable only runs as far as a central point in the neighbourhood, and copper wire connects that node to each home.
An interesting fact is that original ADSL connections use an average of 2.5km of copper wire per connection, fibre to the node uses 500 metres, fibre to the curb uses 30 metres, and fibre-to-the-premises uses none. In south Africa, fibre optic technology offers a viable alternative specifically since it is prone to recurring copper theft.
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A superconductor is a material that achieves superconductivity, which is a state of matter that has no electrical resistance and does not allow magnetic fields to penetrate. An electric current in a superconductor can persist indefinitely.
Superconductivity can only typically be achieved at very cold temperatures. Superconductors have a wide variety of everyday applications, from MRI machines to super-fast maglev trains that use magnets to levitate the trains off the track to reduce friction. Researchers are now trying to find and develop superconductors that work at higher temperatures, which would revolutionize energy transport and storage.
Who discovered superconductivity?
The credit for the discovery of superconductivity goes to Dutch physicist Heike Kamerlingh Onnes. In 1911, Onnes was studying the electrical properties of mercury in his laboratory at Leiden University in The Netherlands when he found that the electrical resistance in the mercury completely vanished when he dropped the temperature to below 4.2 Kelvin — that's just 4.2 degrees Celsius (7.56 degrees Fahrenheit) above absolute zero.
To confirm this result, Onnes applied an electric current to a sample of supercooled mercury, then disconnected the battery. He found that the electric current persisted in the mercury without decreasing, confirming the lack of electrical resistance and opening the door to future applications of superconductivity.
History of superconductivity
Physicists spent decades trying to understand the nature of superconductivity and what caused it. They found that many elements and materials, but not all, become superconducting when cooled below a certain critical temperature.
In 1933, physicists Walther Meissner and Robert Ochsenfeld discovered that superconductors "expel" any nearby magnetic fields, meaning weak magnetic fields can't penetrate far inside a superconductor, according to Hyper Physics, an educational site from the Georgia State University department of physics and astronomy. This phenomenon is called the Meissner effect.
It wasn't until 1950 that theoretical physicists Lev Landau and Vitaly Ginzburg published a theory of how superconductors work, according to Ginzburg's biography on The Nobel Prize website. While successful in predicting the properties of superconductors, their theory was "macroscopic," meaning it focused on the large-scale behaviors of superconductors while remaining ignorant of what was going on at a microscopic level.
Finally, in 1957, physicists John Bardeen, Leon N. Cooper and Robert Schrieffer developed a complete, microscopic theory of superconductivity. To create electrical resistance, the electrons in a metal need to be free to bounce around. But when the electrons inside a metal become incredibly cold, they can pair up, preventing them from bouncing around. These electron pairs, called Cooper pairs, are very stable at low temperatures, and with no electrons "free" to bounce around, the electrical resistance disappears. Bardeen, Cooper and Schrieffer put these pieces together to form their theory, known as BCS theory, which they published in the journal Physical Review Letters.
How do superconductors work?
When a metal drops below a critical temperature, the electrons in the metal form bonds called Cooper pairs. Locked up like this, the electrons can't provide any electrical resistance, and electricity can flow through the metal perfectly, according to the University of Cambridge.
However, this only works at low temperatures. When the metal gets too warm, the electrons have enough energy to break the bonds of the Cooper pairs and go back to offering resistance. That is why Onnes, in his original experiments, found that mercury behaved as a superconductor at 4.19 K, but not 4.2 K.
What are superconductors used for?
It's very likely that you've encountered a superconductor without realizing it. In order to generate the strong magnetic fields used in magnetic resonance imaging (MRI) and nuclear magnetic resonance imaging (NMRI), the machines use powerful electromagnets, as described by the Mayo Clinic. These powerful electromagnets would melt normal metals due to the heat of even a little bit of resistance. However, because superconductors have no electrical resistance, no heat is generated, and the electromagnets can generate the necessary magnetic fields.
Similar superconducting electromagnets are also used in maglev trains, experimental nuclear fusion reactors and high-energy particle accelerator laboratories.Superconductors are also used to power railguns and coilguns, cell phone base stations, fast digital circuits and particle detectors.
Essentially, any time you need a really strong magnetic field or electric current and don't want your equipment to melt the moment you turn it on, you need a superconductor.(opens in new tab)
"One of the most interesting applications of superconductors is for quantum computers," said Alexey Bezryadin, a condensed matter physicist at the University of Illinois at Urbana-Champaign. Because of the unique properties of electrical currents in superconductors, they can be used to construct quantum computers.
"Such computers are composed of quantum bits or qubits. Qubits, unlike classical bits of information, can exist in quantum superposition states of being '0' and '1' at the same time. Superconducting devices can mimic this," Bezryadin told Live Science. "For example, the current in a superconducting loop can flow clockwise and counterclockwise at the same time. Such a state constitutes an example of a superconducting qubit."
What's the latest in superconductor research?
The first challenge for today's researchers is "to develop materials that are superconductors at ambient conditions, because currently superconductivity only exists either at very low temperatures or at very high pressures," said Mehmet Dogan, a postdoctoral researcher at the University of California, Berkeley. The next challenge is to develop a theory that explains how the novel superconductors work and predict the properties of those materials, Dogan told Live Science in an email.
Superconductors are separated into two main categories: low-temperature superconductors (LTS), also known as conventional superconductors, and high-temperature superconductors (HTS), or unconventional superconductors. LTS can be described by the BCS theory to explain how the electrons form Cooper pairs, while HTS use other microscopic methods to achieve zero resistance. The origins of HTS are one of the major unsolved problems of modern-day physics.
Most of the historical research on superconductivity has been in the direction of LTS, because those superconductors are much easier to discover and study, and almost all applications of superconductivity involve LTS.
HTS, in contrast, are an active and exciting area of modern-day research. Anything that works as a superconductor above 70 K is generally considered an HTS. Even though that's still pretty cold, that temperature is desirable because it can be reached by cooling with liquid nitrogen, which is far more common and readily available than the liquid helium needed to cool to the even lower temperatures that are needed for LTS.
The future of superconductors
The "holy grail" of superconductor research is to find a material that can act as a superconductor at room temperatures. To date, the highest superconducting temperature was reached with extremely pressurized carbonaceous sulfur hydride, which reached superconductivity at 59 F (15 C, or about 288 K), but required 267 gigapascals of pressure to do it. That pressure is equivalent to the interior of giant planets like Jupiter, which makes it impractical for everyday applications.
Room-temperature superconductors would allow for the electrical transmission of energy with no losses or waste, more efficient maglev trains, and cheaper and more ubiquitous use of MRI technology. The practical applications of room-temperature superconductors are limitless — physicists just need to figure out how superconductors work at room temperatures and what the "Goldilocks" material to allow for superconductivity might be.


Google’s recent announcement that its quantum computer had achieved “quantum supremacy” has garnered significant global attention. And for good reason. Sycamore, Google’s 53-bit quantum computer reportedly performed a 200-second calculation in the time it would have taken the world’s fastest supercomputer, the IBM Summit, 10,000 years. Beyond conventional silicon computers, quantum computers represent a new era in the evolution of computational technology. Nonetheless, the challenges confronting the field suggest that there is a very long way to go.
Born out of the thinking of Max Planck, Niels Bohr, and Albert Einstein, quantum theory offers new and unexplored potential for driving the evolution of computer science. Quantum computers operate on completely different principles compared to their conventional counterparts. Where classical computers are fast and efficient, they are simply not very good at problems that involve exponential complexity. Quantum researchers utilize the properties of electrons as an engine for performing exponentially fast calculations.
Quantum computers are expected to transform cryptography, pattern matching, drug discovery and ultimately boost artificial intelligence (AI) training. However, the current generation of quantum computers are extremely sensitive to perturbations, noise, and other environmental effects that can cause their “quantum state” to waver and disappear— an effect referred to as decoherence.
Contemporary quantum computers require exacting demands of stability and temperature for maintaining quantum states. In fact, researchers have only been able to maintain a quantum state for a tiny fraction of a second— not long enough to carry out a useful algorithm. This instability remains the biggest challenge facing quantum computing.
Designing a quantum computer with qubits
Research on quantum computing remains at a very early stage. Much like the traditional computers introduced in the 1950s, quantum computers remain big, clunky machines. The most common design of quantum computers rely on multiple layers of superconducting circuits sequestered in a controlled environment and cooled step-wise to temperatures colder than deep space.
Where a conventional computer uses transistors as a substrate for information processing, quantum computers can use anything that demonstrates quantum behavior. This can include an atom, a molecule, or more commonly, an electron. Due to “superposition”, quantum computers can perform multiple calculations at once, giving them the potential to be exponentially more powerful than conventional computers.
Superposition is best understood as the capacity for electrons to be at different positions at the same time. Quantum computers leverage the superposition of quantum states to manage calculations on orders of magnitude faster than silicon processors. As demonstrated by the famous double-slit experiment involving a single photon of light, photons may produce a wavelike interference pattern or superposition of all available paths.
The most common quantum computers today leverage electrons to move beyond the binary logic of silicon computing. In conventional computing, information is stored as bits and exist as either ones or zeros. Unlike a conventional bit, the quantum bit or qubit can store and manipulate much more information than just ones and zeros. For example, A 10-qubit quantum computer can process 1,024 possible inputs at once (instead of analyzing them one at a time).
The magic of qubits is that they can exist in superposition, or in multiple states at once. Using the example of Schrödinger’s cat, any given qubit can hold a 0 and a 1 at the same time. Thus, a single qubit can represent far more information than a binary bit. As an example, a four-qubit computer register can hold 16 different numbers simultaneously.
Using code to manipulate electrons, many engineers are hoping to develop quantum algorithms to exploit the vast computational potential of quantum computers. Generally, the goal is to encode parts of a problem into a complex quantum state using qubits. Then, manipulating that state in order to drive it towards something that will eventually represent the solution. Solutions can be measured by collapsing the superpositions into deterministic sequences of zeros and ones.
The race for high-performance quantum computers
Quantum computers hold the promise of virtually limitless supercomputing power, pushing the envelope on supercomputing or high-performance computing (HPC). However, the current state of noisy quantum computers have a coherence time of a mere 100 microseconds. This is the maximum length of time in which an experiment can be run on a quantum processor before errors take over.
The most common quantum computer designs today consist of superconductor computers and spin computers. Superconductors are the most well-established method for maintaining a quantum state: Metallic superconductors are used at near-zero temperatures in order to conduct electrons. Electrons must be free from all radiation or light particles and kept at a freezing temperature. Google’s quantum computer, for example, is cooled to an astonishing 460 degrees below zero.
The more recent spin method of quantum computing uses a single electron within silicon to create qubits. Only a few nanometers in size, these electrons are called quantum dots and can operate at higher temperatures. In fact, a new silicon chip capable of manipulating the spin of a single electron could ultimately allow future quantum computers to be built using conventional electronic technology.
Thanks largely to research by IBM, Google, Microsoft and others, the United States remains the leader in patents related to quantum computers. In the future, quantum computers are expected to become very good at highly specific problem-solving. Quantum computing performs best in probabilistic situations such as weather prediction, market forecasting, and breaking encryption.
In the U.S., IBM and Google are racing to create the first truly useful quantum computer. In July 2016, Google engineers used a quantum device to simulate a hydrogen molecule. IBM is also working on developing quantum computing technologies and recently introduced the IBM Quantum Experience, a quantum computing platform delivered via the Cloud. Since 2016, IBM has provided researchers with a five-qubit cloud-based quantum computer and made its 20-qubit system available online at the end of 2017.
In addition to IBM and Google, D-Wave, a Canadian company based in Vancouver has also been a leader in developing an early-stage quantum computer. D-Wave utilizes a method known as quantum annealing. Running adiabatic quantum computing algorithms, D-Wave’s machine finds a “good enough” or “local minima” solution. Volkswagen has leveraged D-Wave’s quantum annealing technology, using it to carry out research on traffic flow optimization in Beijing with 2,000 qubits.
One very promising application of quantum technology is quantum communications. Researchers are working towards creating ultra-secure communication networks that could form the basis of a quantum internet. Where sensitive data is currently encrypted and transmitted using digital “keys” (1 and 0s), quantum communications has already demonstrated the capacity to secure encrypted information using qubits. Quantum key distribution (QKD), for example, combines digitally encrypted data with keys that are encoded and transmitted using quantum state using qubits.
China has become a global leader in the drive to develop quantum communication technologies. Pouring vast sums of money into quantum research, China filed almost twice as many patents as the United States in the field of quantum technology in 2017 alone. That same year, the country launched a dedicated quantum communications satellite called Micius, staging the world’s first quantum key distribution-secured video conference between Beijing and Vienna.
An arcane field only a decade ago, quantum computing has matured at an astonishing pace. As countries around the world continue to move the needle on supercomputing, we will likely see revolutionary applications in the field of quantum technology. Nonetheless, the mainstream application of quantum computing remains decades away. Quantum computing represents a revolution in computational technologies; that goes without saying. But there remains significant work ahead.


Unlike Bilbo’s magic ring, which entangles human hearts, engineers have created a new micro-ring that entangles individual particles of light, an important first step in a whole host of new technologies.
Entanglement – the instantaneous connection between two particles no matter their distance apart – is one of the most intriguing and promising phenomena in all of physics. Properly harnessed, entangled photons could revolutionize computing, communications, and cyber security. Though readily created in the lab and by comparatively large-scale optoelectronic components, a practical source of entangled photons that can fit onto an ordinary computer chip has been elusive.
New research, reported today in The Optical Society‘s (OSA) new high-impact journal Optica, describes how a team of scientists has developed, for the first time, a microscopic component that is small enough to fit onto a standard silicon chip that can generate a continuous supply of entangled photons.
The new design is based on an established silicon technology known as a micro-ring resonator. These resonators are actually loops that are etched onto silicon wafers that can corral and then reemit particles of light. By tailoring the design of this resonator, the researchers created a novel source of entangled photons that is incredibly small and highly efficient, making it an ideal on-chip component.
“The main advantage of our new source is that it is at the same time small, bright, and silicon based,” said Daniele Bajoni, a researcher at the Università degli Studi di Pavia in Italy and co-author on the paper. “The diameter of the ring resonator is a mere 20 microns, which is about one-tenth of the width of a human hair. Previous sources were hundreds of times larger than the one we developed.”
From Entanglement to Innovation
Scientists and engineers have long recognized the enormous practical potential of entangled photons. This curious manifestation of quantum physics, which Einstein referred to as “spooky action at a distance,” has two important implications in real-world technology.
First, if something acts on one of the entangled photons then the other one will respond to that action instantly, even if it is on the opposite side of a computer chip or even the opposite side of the Galaxy. This behavior could be harnessed to increase the power and speed of computations. The second implication is that the two photons can be considered to be, in some sense, a single entity, which would allow for new communication protocols that are immune to spying.
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Miniaturization, the process of making components smaller, has been key to the realization of Moore’s Law in computing. Moore’s original observation began with noticing the trend that the number of components integrated into a semiconductor circuit would double each year.1 With this doubling came the increase in computational power that means chips less than a centimeter in size can now perform calculations that would have been unachievable by room-sized supercomputers in the past.
Image Credit: Macro photo/Shutterstock.com
While the continued exponential scaling of the component number of Moore’s Law appears to have reached its limit2, the huge rise and success of information technology over this period is due to the successful miniaturization of electronic components. The limits being reached now are issues with the thermal load on components and the challenge and costs associated with the machining of chips on this scale and further shrinkage will require the use of new architectures beyond just silicon components.
Increasingly, light is now being used in applications that would have previously utilized electrons and therefore been covered by the domain of electronics. Examples of this include the use of optical fibers and light transmission for faster information exchange and light is an emerging technology for use in quantum computing architectures.3 Light has the advantage of being able to travel faster than electrons and can support greater bandwidth in data transfer.
However, if photonics is truly to replace electronics, there is a need for photonic chips that can perform logic operations with photons similar to the electronic transistor. Such chips need to be sufficiently small so that they can be incorporated into devices without requiring a significant footprint.
Atoms have proved an invaluable tool for quantum information. The ability to prepare atoms in a superposition of states and control this at will using laser fields has made them a popular tool in quantum computers, sensors, and devices.4 However, systems based on optical trapping of atoms require extensive amounts of cooling and often require specially designed buildings and laboratories to manage vibration levels to ensure experiments can remain interferometrically stable.
While such technologies seem to be scalable,4 these technologies are a long way from the compact photonics chip. However, recent research from the University of Illinois at Urbana-Champaign has demonstrated a way of creating simple, compact circuits that uses sound waves to control the stability of light.5
The new stabilization scheme is designed to be compatible with atomic control systems and work as an isolator to improve the stability of such experiments. Scaling down large atom-based experiments has proved challenging to date but with the use of these new isolators, smaller-scale quantum devices may now be a possibility.
When light interacts with matter, it can undergo several processes. Those include absorption, which can promote atoms into a superposition of states needed for quantum information processes, but also other unwanted processes such as scattering. Even from well-collimated point sources such as lasers, light can be difficult to control with issues such as beam divergence and aberrations introduced by optical components, adding further challenges.
The development of optical fibers and waveguides has opened new possibilities for the control of light, including mode selection. Improvements in optics and manufacturing processes have made it possible to create resonators with minimal light loss.
A resonator is an optical cavity that allows a beam to travel in a closed path. These are used routinely in laser systems to allow for multiple passes of light over a given distance, enabling greater levels of light amplification. In waveguide-resonator systems, the resonator is coupled to a waveguide and any light that is far detuned from the resonance of the absorber will pass through the waveguide without interruption. For a critical coupling regime, there will be strong absorption of the light by the resonator.
By using a chiral waveguide-resonator system, the team created a system that completely blocks light passing in one direction, but not the other. Normally, the waveguide would be transparent to non-resonant wavelengths but they could potentially be transmitted in both directions along the guide were there to be any back reflections.
Unidirectionality of this kind has been previously achieved in waveguide-resonator systems but using magnetic fields which often requires additional bulky equipment.
The achievement of unidirectionality by switching the resonator material for a chiral substrate removes the need for such fields and is an important step towards the miniaturization of atom-based devices.
Back reflections can cause damage to optical components as well as unwanted behavior in optical components, so being able to suppress these also improves the device performance. As the system is compatible with 780 nm light, it is ideal for use with rubidium-atom-based systems that are currently being widely explored for quantum sensors.
References and Further Reading
- Mack, C. A. (2011). Fifty Years of Moore’s Law. IEEE Transactions on Semiconductor Manufacturing, 24(2), 202–207. https://doi.org/10.1109/TSM.2010.2096437
- Theis, T. N., & Wong, H. P. (2017). The End of Moore’s Law: A New Beginning for Information Technology. Computing in Science & Engineering, 19(2), 41–50. https://doi.org/10.1109/MCSE.2017.29
- Kok, P., Dowling, J. P., & Milburn, G. J. (2007). Linear optical quantum computing with photonic qubits. Reviews of Modern Physics, 79, 135–174. https://doi.org/10.1103/RevModPhys.79.135
- Cirac, J. I., & Zoller, P. (2000). A scalable quantum computer with ions in an array of microtraps. Nature, 404, 579–581.
- Sohn, D. B., Örsel, O. E., & Bahl, G. (2021). Electrically driven optical isolation through phonon-mediated photonic Autler – Townes splitting. Nature Photonics. https://doi.org/10.1038/s41566-021-00884-x


Quantum communication systems offer the promise of virtually unbreakable encryption.
Unlike classical encryption, which is used to send secure data over networks today and whose security depends on the difficulty of solving mathematical problems like the factoring of large numbers, most quantum encryption schemes keep the encryption key separate from the data. This approach ensures that an eavesdropper with access only to the data could not decipher the key. However, researchers have recently demonstrated that even quantum encryption may be susceptible to hacking.
In a presentation next month at the Conference on Lasers and Electro-Optics (CLEO: 2013) in San Jose, Calif., Renato Renner of the Institute for Theoretical Physics in Zurich will discuss how he and his team of theoretical physicists are working on new ways to calculate the failure probability of certain quantum encryption schemes. The numbers would allow users to estimate how likely it would be that an adversary could read their secret messages—information that is critical for ensuring the overall security of quantum communications.
Quantum key distribution (QKD) is a kind of quantum encryption in which a secret password is shared between two distant parties (usually named Alice and Bob in thought experiments). The secret password, or key, is distributed as bits of quantum data, so that if an eavesdropper (usually named Eve) tries to intercept the message, the bits will be disturbed and Alice and Bob will know the transmission has been compromised. If the key is not disturbed, it can be used to encode messages that are sent over an insecure channel.
“The security of Quantum Key Distribution systems is never absolute,” says Renner. He notes that the security of QKD systems depends on three assumptions: the initial secrecy of the password, the correctness and completeness of quantum theory, and the reliability of the devices in the quantum communication system.
Recent work by other research groups has illustrated how real-world devices that are not 100 percent reliable can leave weaknesses in quantum communication schemes that may be exploited by a clever hacker. For example, the photon detectors used in QKD should click with a certain probability whenever a photon is detected, but in practice the devices can be “blinded” by a strong light pulse and not click. “In fact, an adversary may use strong light pulses to ‘remotely control’ the detector,” says Renner.
Since such bright light hacking techniques were first demonstrated in 2010, physicists have been keen to find ways to calculate the security of quantum encryption schemes without making assumptions about the reliability of the devices. The quest has generated a lot of interest in a field called device-independent cryptography.
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The search for ever higher computer performance is a strong motivation for scientists. The computers of tomorrow will be quantum, allowing rapid and extremely complex calculations, the complete simulation of molecules, or the development of innovative materials. However, before accessing it, it is first necessary to create the components of these supercomputers. Recently, engineers in Sydney demonstrated a quantum integrated circuit made of silicon, made up of 10 phosphorus atoms. This represents an important step in the development of useful quantum computing in real conditions. By precisely controlling the quantum states of atoms — the different energy levels of the electrons belonging to the atom — the new silicon processor can simulate the structure and properties of an organic molecule with astonishing precision.
The atomic-scale integrated circuit milestone is the culmination of 20 years of research led by Scientia’s Michelle Simmons, founder of UNSW start-up Silicon Quantum Computing (SQC). In 2012, his team had created the very first “quantum transistor”.
Transistors are small electronic components that store bits of information. They are made with semiconductor materials, allowing a switching effect and the encoding of information. This is because in semiconductors there is a large group of electrons. However, according to quantum mechanics, an electron can only occupy certain energy levels. This is how the levels of the electrons making up the semiconductor correspond to “bands” or variations in permitted energy values. When a transistor is turned on — the electrical voltage is in the energy band — current flows and the computer detects the value “1”. When a transistor is in off mode—the electrical voltage is outside the permitted energy band—current no longer flows and the computer interprets this as a “0” value.
Remember that a quantum computer is the equivalent of classical computers, but performing its calculations using the laws of quantum physics directly. While a classical computer manipulates bits of information, which are either 0s or 1s, a quantum computer uses qubits. These are generalizations of the classical bits, which are sort of a simultaneous superposition of these two states.
Thus, recently, a team of quantum computing physicists from UNSW Sydney, in partnership with the start-up Silicon Quantum Computing, designed an atomic-scale quantum processor to simulate the behavior of a small organic molecule, mimicking its structure and energy states. This represents a major milestone in the race to build the world’s first quantum computer, and demonstrates the team’s ability to control the quantum states of electrons and atoms in silicon to a level never before achieved. Their results are published in the journal Nature.
Imitate nature, but in a very demanding way
This technological innovation addresses a challenge first postulated by pioneering theoretical physicist Professor Richard Feynman in his famous 1959 lecture. Plenty of Room at the Bottom. During this lecture, Feynman asserted that in order to understand how nature works, it is essential to be able to control matter at the same length scales from which matter is constructed — that is, to be able to controlling matter on the atomic scale.
Scientia Professor Michelle Simmons, lead researcher on the study, said in a statement: And so that’s what we do, we literally build it from the bottom up, where we mimic the polyacetylene molecule by putting atoms in the silicon with the exact distances that represent the single and double carbon-carbon bonds “. This molecule has the advantage of being well known by researchers. They can therefore immediately determine the consistency of the result, and by extension the reliability of the chip.
To design the first quantum integrated circuit, the team had to perform three distinct technological feats of atomic engineering, in near-absolute vacuum. Indeed, at this scale, a single hydrogen atom can compromise the whole manipulation.
The first feat was to create small dots of uniformly sized atoms, so their energy levels would line up and electrons could easily pass through them. These dots, called Quantum Dots (QD), are dots of phosphorus atoms. By configuring their layouts, they can behave like real quantum transistors. In the present study, the quantum integrated circuit includes a chain of 10 quantum dots to simulate the precise location of atoms in the polyacetylene chain.
Nevertheless, the tolerable energy band, as mentioned earlier for conventional transistors, is extremely small. This is where the second technological feat comes in, the ability to adjust the energy levels of each point individually, but also of all the points collectively. So, using a nanometric precision system, they added six control electrodes (G1 to G6 in the image below) to adjust the energy levels. This gives complete control of where electrons exist in the polyacetylene chain. By adding source (S) and drain (D) conductors, they could then measure the current flowing through the device as electrons passed through the string of 10 quantum dots.
Finally, the third technical challenge was to achieve the ability to control distances between points with sub-nanometer precision. If they are too close, the energy produced is too powerful to be mastered. If they are too far apart, interactions between them become risky. The points must therefore be close enough, but remain independent, to allow the coherent transport of electrons through the chain.
To be doubly sure of this consistency of the results produced by the circuit, the researchers simulated two different strands of the polymer chains at 10 points of the molecule.
In the first device they cut a piece of chain to leave double bonds at the end giving 10 peaks in the current. In the second device, they cut a different fragment of the chain to leave single bonds at the end, resulting in only two peaks in the current. The current through each chain was therefore radically different due to the different bond lengths of the atoms at the end of the chain.
Professor Simmons explains: “ What this shows is that you can literally mimic what is actually going on in the molecule. And that’s why it’s exciting because the signatures of the two chains are very different. Most other quantum computing architectures lack the ability to engineer atoms with sub-nanometer precision or allow atoms to be that close. This means that we can now begin to understand increasingly complicated molecules by putting the atoms in place as if they were mimicking the real physical system. “.
And now ? Quantum biology…
According to Professor Simmons, it is not by chance that a carbon chain of 10 atoms was chosen, because it is within the size limit of what a conventional computer is able to calculate, with up to 1024 distinct interactions of electrons in this system. Increasing it to a chain of 20 points would see the number of possible interactions increase exponentially, making it difficult for a typical computer to solve.
She says: ” We are approaching the limit of what conventional computers can do, so this is like a step into the unknown. […] We are going to be able to understand the world in a different way, by addressing fundamental questions that we have never been able to answer before “.
Moreover, we are talking about quantum biology. This recent disciplinary field deals with the study of processes at work in living organisms involving the laws of quantum physics. Photosynthesis, the orientation of migratory birds or even bioluminescence are governed by quantum processes. Understanding these phenomena paves the way for many innovations in the field of biomimicry.
The team believes that the development of quantum computers is on a trajectory comparable to the evolution of classical computers — from a transistor in 1947 to an integrated circuit in 1958, then small computer chips that have been integrated into commercial products, like calculators or so, five years later. Incidentally, the production of this atomic-scale integrated circuit, which functions as an analog quantum processor, came less than a decade after the team declared (in 2012) that they had made the first transistor. single atom in the world, completed two years ahead of schedule.
Finally, using fewer components in the circuit to control the qubits minimizes the amount of any interference with quantum states, allowing devices to be scaled up to create more complex and powerful quantum systems.


What’s Under the Hood of A Quantum Computer?
(PhysicsToday) The separation between hardware and user interface is the product of decades of development. Now quantum computer developers are navigating similar terrain.
The quantum computing stack is everything that lies between a user and the physical qubits. The stack needs to perform essential functions; for instance, it must facilitate user interaction, turn inputs into hardware manipulation, and correct for numerous error sources.
There’s no one right way to divide those tasks into discrete levels, though, and researchers and technology companies are still pursuing different visions for future quantum architectures.
Harrison Ball, Michael Biercuk, and Michael Hush present the quantum computing stack proposed by Q-CTRL in this articl
NOTE: The following is a summary by IQT-News’ of this article’s description of each of the key components of a quantum computer. The summary and the original article are both worth the time to read. Additional sources are provided in the original article as well for indepth followup.
Classical computers store information as bits that each take a value of 0 or 1. Underlying those bits are field-effect transistors that act as switches; each can take a value of either 0 or 1 depending on whether the switch is on or off. At the most basic level, everything a computer does—save information, execute calculations, run programs—is just manipulating the values of those billions of bits with small electrical voltages.
Quantum computers instead rely on qubits that can be in one of two states, ∣0〉 or ∣1〉, or a linear superposition.
Whereas classical computing has largely settled on one type of bit hardware, qubits still come in many varieties. Any two-level quantum system—a nuclear spin, a photon’s polarization, or a quantum dot’s spin, to name a few—can be used as a qubit. The usefulness of a particular system, however, depends on things such as how easily the qubits are to manipulate and entangle, how long they remain in desired quantum states, and how prone they are to having their states destroyed by outside noise.
Qubits are prone to errors. All sorts of environmental factors—thermal fluctuations, electromagnetic radiation, magnetic fields—can knock a qubit out of its intended state. That degradation of information is known as decoherence and can occur in a fraction of a second. Despite the use of refrigeration to reduce thermal fluctuations, decoherence eventually creeps in and produces hardware errors, like accidentally flipping a qubit’s state from ∣0〉 to ∣1〉. (The commonly used refrigeration systems, like the one shown above from IBM, are what many people picture when they imagine a quantum computer.) The number of operations that can be performed with a qubit is limited by the qubit’s decoherence time. Moreover, every set of qubit hardware has its own unique deviations from ideal performance.
Hardware-aware quantum compiler
The hardware-aware quantum compiler, also known as a transpiler, is responsible for figuring out how to complete a set of logic operations in a manner that accounts for the physical connections between qubits. Although physical qubits can’t easily be moved, the states of two qubits can be swapped for an effective rearrangement. The transpiler works out how to implement an arbitrary operation between qubits given the hardware constraints, such as which qubits are directly connected to each other. It also decides which qubits to use for each operation—for instance, if a particular qubit is known to be faulty, information might need to be routed around it.
Quantum Error Corrector
Correcting qubit errors with QEC is inherently resource intensive—some current schemes use tens of physical qubits per logical block—and will likely require more qubits than are available in existing devices to provide any real benefit. Accordingly, QEC is more important in the long term than it is for current machines. Quantum firmware aims to reduce the burden on QEC routines by dealing with more predictable noise, thereby improving QEC’s resource efficiency.
Logical-level compilation and circuit optimization
A single algorithm can be represented by multiple logically equivalent circuits, and the goal of circuit optimization is to find the one requiring the fewest operations or timesteps. Executing fewer operations enables the algorithm to run faster—an important goal for any quantum computer, whether or not it is using QEC.
Quantum algorithms and applications
Quantum algorithms play the same role as classical algorithms: They provide step-by-step instructions for completing a computational task. Although a regular algorithm could in principle be run on a quantum computer, a true quantum algorithm takes advantage of the underlying hardware’s quantum nature.
A variational quantum algorithm is a compromise between classical and quantum ones. It breaks up a computation into a small quantum component and a larger classical optimization problem and therefore requires a much smaller quantum computer than, say, the quantum Fourier transform. Such algorithms are promising for solving problems in finance, logistics, and chemistry.
User interface, QAAS, and operating system
Most people who want to use quantum computers aren’t going to build or even buy one—at least not anytime soon. To facilitate access to the limited existing quantum computing resources, companies have put together cloud-based infrastructures that allow remote operation. As in a classical computer, the highest level of the quantum computing stack provides the interface that users interact with.


A new phase of matter was observed in a quantum computer after physicists pulsed light on its qubits in a pattern inspired by the Fibonacci sequence.
If you think this is mind-boggling, then this peculiar quirk of quantum mechanics behaves as if it has two time dimensions, rather than one; A trait that the scientists say makes qubits more powerful and able to remain stable for the duration of the experiment.
This stability is called quantum coherence, and it is one of the main goals of an error-free quantum computer—and one of the most difficult to achieve.
The work represents “a completely different way of thinking about the phases of matter,” according to computational quantum physicist Felipe Domitrescu of the Flatiron Institute, and lead author of a new research paper describing the phenomenon.
“I’ve been working on these theoretical ideas for over five years, and seeing them actually come true in experiments is exciting.”
Quantum computing is based on qubits, the quantum equivalent of computing qubits. However, when bits process information in one of two states, 1 or 0, they can be qubits at once, a condition known as quantum superposition.
The mathematical nature of this superposition can be incredibly powerful from a computational point of view, making short problem solving under the right conditions.
But the uncertain and unstable nature of a series of qubits also depends on how their oscillating states relate to each other – a relationship called entanglement.
Frustratingly, qubits can get entangled with almost anything in their environment, which leads to errors. The more sensitive a qubit’s fuzzy state is (or the more messy its environment), the greater the risk that it will lose this coherence.
Improving coherence to a point of feasibility is likely a multi-tactic approach to removing a major hurdle standing in the way of a functional quantum computer – every little bit makes a difference.
“Even if you keep all of the atoms under tight control, they can lose their quantity by talking to their environment, heating up or interacting with things in ways they didn’t plan for,” Domitrescu explained.
“In practice, experimental devices contain many error sources that can degrade coherence after a few laser pulses.”
One way to protect qubits from decoherence is to enforce symmetry. Rotate an ordinary old square ninety degrees, and it’s still effectively the same shape. This symmetry protects it from certain rotational effects.
Clicking the qubits with evenly spaced laser pulses ensures symmetry that does not depend on space, but rather in time. Domitrescu and colleagues wanted to see if they could increase this effect by adding, not symmetric periodic, but asymmetric quasi-periodic.
They assumed that this would not add a one-time symmetry, but a one-time symmetry; One is actually buried inside the other.
The idea was based on previous work by the team that proposed creating a so-called quasicrystalline in time, rather than space. When a crystal consists of a symmetrical network of atoms that repeat in space, such as a square lattice forest gym or honeycomb, the pattern of atoms on a semi-crystal is non-repetitive, like a Penrose tiling, yet still ordered.
The team conducted their experiment on a high-end commercial quantum computer designed by Quantinuum, a quantum computing company. This monster employs 10 atoms of ytterbium (one of the favorite elements of atomic clocks). These atoms are kept in an electric ion trap, through which laser pulses can be used to control or measure them.
Domitrescu and colleagues created a series of laser pulses based on Fibonacci numbers, with each part representing the sum of the previous two parts. This results in an ordered, but not repeating, sequence, just like a quasicrystal.
Semi-crystalline crystals can be described mathematically as lower dimensional sections of higher dimensional lattices. Penrose tiling can be described as a two-dimensional slice of a five-dimensional cube.
In the same way, the team’s laser pulses can be described as a one-dimensional representation of a two-dimensional pattern. In theory, this meant that it would likely impose two time symmetries on the qubits.
The team tested their work by flashing lasers into a ytterbium qubit, first in symmetrical sequences, then semi-periodically. Then they measured the coherence of two qubits on either side of the trap.
For the periodic sequence, the qubits were stable for 1.5 s. For the quasi-periodic sequences, they remained stable for 5.5 s – the duration of the experiment.
The additional time symmetry added another layer of protection against quantum decoherence, the researchers said.
“With this quasi-periodic sequence, there is a complex evolution that eliminates all the errors that live on the edge,” Domitrescu said.
“Because of that, the edge stays quantum mechanically coherent a lot, much longer than you’d expect.”
The researchers said the work is nowhere near ready to be integrated into functional quantum computers, but it does represent an important step toward that goal.
The search was published in temper nature.
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The IBM lab responsible for inventing the scanning tunneling microscope and the atomic force microscope has invented another critical tool for helping us understand the nanoscale.
Accurately measuring the temperature of objects at the nanoscale has been challenging scientists for decades. Current techniques are not accurate and they typically generate artifacts, limiting their reliability.
In the 1980s, IBM scientists Gerd Binnig and the late Heinrich Rohrer wanted to directly explore a surface’s electronic structure and imperfections. The instrument they needed to take such measurements didn’t exist, yet. So they did what any good scientist would do: they invented one. It became known as the scanning tunneling microscope (STM), opening the door to nanotechnology. Just a few years later, the invention was recognized with the highest of honors, the Nobel Prize for Physics in 1986.
More than 30 years later IBM scientists continue to follow in the footsteps of Binnig and Rohrer and with their latest invention.
Dr. Fabian Menges, an IBM post-doc and co-inventor of the technique said, “We started back in 2010 and simply never gave up. Previous research was focused on a nanoscale thermometer, but we should have been inventing a thermometer for the nanoscale — an important distinction. This adjustment led us to develop a technique which combines local thermal sensing with the measuring capability of a microscope — we call it scanning probe thermometry.”
IBM scientist Fabian Menges with his invention.
How it Works: A Scanning Probe Thermometry
The most common technique to measure temperature on the macroscale is to bring a thermometer into thermal contact with the sample. This is how a fever thermometer works. Once it’s placed under our tongue it equilibrates to our body temperature so that we can determine our temperature at a healthy 37 degrees C. Unfortunately, it gets a little more challenging when using a thermometer to measure a nanoscopic object.
For example, it would be impossible to use a typical thermometer to measure the temperature of an individual virus. The size of the virus is too small and the thermometer cannot equilibrate without significantly disturbing the virus temperature.
To solve this challenge, IBM scientists developed a single scan non-equilibrium contact thermometry technique to measure the temperature of nanoscopic objects using a scanning probe.
As the scanning probe thermometer and the object cannot thermally equilibrate at the nanoscale, two signals are measured simultaneously: a small heat flux, and its resistance to heat flow. Combining these two signal the temperature of nanoscopic objects can then be quantified for an accurate result.
IBM scientist Dr. Bernd Gotsmann and co-inventor explains, “The technique is analogous to touching a hot plate and inferring its temperature from sensing the heat flux between our own body and the heat source. Essentially, the tip of the probe is our the hand. Our perception to hot and cold can be very helpful to get an idea of an objects temperature, but it can also be misleading if the resistance to heat flow is unknown.”
Previously, scientists weren’t accurately including this resistance dependence; but only measuring the rate of the thermal energy transfer through the surface, know as heat flux. In the paper, the authors included the effects of local variations of thermal resistance to measure the temperature of an indium arsenide (InAs) nanowire, and a self-heated gold interconnect with a combination of a few-miliKelvin and few-nanometer spatial resolution.
Menges adds, “Not only is the scanning probe thermometer accurate, it meets the trifecta for tools: it’s easy to operate, simple to build, and very versatile, in that it can be used to measure the temperature of nano- and micro-sized hot spots that can locally effect the physical properties of materials or govern chemical reactions in devices such as transistors, memory cells, thermoelectric energy converters or plasmonic structures. The applications are endless.”
From left to right, IBM scientists Nico Mosso, Bernd Gotsmann, Fabian Motzfeld and Fabian Menges in the Noise Free Lab.
Noise Free Labs
It’s no coincidence that the team began to see improvements in the development of the scanning probe thermometer 18 months ago when they moved their research into the new Noise Free Labs — six meters underground at the Binnig and Rohrer Nanotechnology Center on the campus of IBM Research-Zurich.
This unique environment, which shields the experiments from vibration, acoustic noise, electromagnetic signals and temperature fluctuations, helped the team achieve sub-milli Kelvin precision.
“While we had the benefit of this unique room, the technique can also produce reliable results in normal environment,” said Menges.
“We hope the paper will produce both a lot of excitement and relief for scientists, who like us, have been searching for such a tool,” said Gotsmann. “Similar to the STM, we hope to license this technique to tool manufacturers who can then bring it to market as an additional function to their microscopy product line.”
The scientists would like to thank the 7th Program Framework for its support under the NANOHEAT project and the Swiss National Science Foundation.
A team formed by IBM Research scientist Dr. Leo Gross, University Regensburg professor Dr. Jascha Repp, and University Santiago de Compostela professor Dr. Diego Peña Gil has received a European Research Center (ERC) Synergy Grant for their project “Single Molecular Devices by Atom Manipulation” (MolDAM).
In the paper “Coherent spin manipulation of individual atoms on a surface,” published in the journal Science, our team demonstrated the use of single atoms as qubits for quantum information processing. This is the first time a single-atom qubit has been achieved using a Scanning Tunneling Microscope.
Our team at IBM Research developed a new technique to control the magnetism of a single copper atom, a technology that could one day allow individual atomic nuclei to store and process information. In a paper published today in the journal Nature Nanotechnology, our team demonstrated that we can control the magnetism of a single […]