Researchers have fashioned ultrathin silicon nanoantennas that trap and redirect light, for applications in quantum computing, LIDAR and even the detection of viruses.
Light is notoriously fast. Its speed is crucial for rapid information exchange, but as light zips through materials, its chances of interacting and exciting atoms and molecules can become very small. If scientists can put the brakes on light particles, or photons, it would open the door to a host of new technology applications.
Now, in a paper published on Aug. 17, in Nature Nanotechnology , Stanford scientists demonstrate a new approach to slow light significantly, much like an echo chamber holds onto sound, and to direct it at will. Researchers in the lab of Jennifer Dionne , associate professor of materials science and engineering at Stanford, structured ultrathin silicon chips into nanoscale bars to resonantly trap light and then release or redirect it later. These "high-quality-factor" or "high-Q" resonators could lead to novel ways of manipulating and using light, including new applications for quantum computing, virtual reality and augmented reality; light-based WiFi; and even the detection of viruses like SARS-CoV-2.
"We’re essentially trying to trap light in a tiny box that still allows the light to come and go from many different directions," said postdoctoral fellow Mark Lawrence , who is also lead author of the paper. "It’s easy to trap light in a box with many sides, but not so easy if the sides are transparent - as is the case with many Silicon-based applications."
Make and manufactureBefore they can manipulate light, the resonators need to be fabricated, and that poses a number of challenges.
A central component of the device is an extremely thin layer of silicon, which traps light very efficiently and has low absorption in the near-infrared, the spectrum of light the scientists want to control. The silicon rests atop a wafer of transparent material (sapphire, in this case) into which the researchers direct an electron microscope "pen" to etch their nanoantenna pattern. The pattern must be drawn as smoothly as possible, as these antennas serve as the walls in the echo-chamber analogy, and imperfections inhibit the light-trapping ability.
"High-Q resonances require the creation of extremely smooth sidewalls that don’t allow the light to leak out," said Dionne, who is also Senior Associate Vice Provost of Research Platforms/Shared Facilities. "That can be achieved fairly routinely with larger micron-scale structures, but is very challenging with nanostructures which scatter light more."
Pattern design plays a key role in creating the high-Q nanostructures. "On a computer, I can draw ultra-smooth lines and blocks of any given geometry, but the fabrication is limited," said Lawrence. "Ultimately, we had to find a design that gave good-light trapping performance but was within the realm of existing fabrication methods."
High quality (factor) applicationsTinkering with the design has resulted in what Dionne and Lawrence describe as an important platform technology with numerous practical applications.
The devices demonstrated so-called quality factors up to 2,500, which is two orders of magnitude (or 100 times) higher than any similar devices have previously achieved. Quality factors are a measure describing resonance behavior, which in this case is proportional to the lifetime of the light. "By achieving quality factors in the thousands, we’re already in a nice sweet spot from some very exciting technological applications," said Dionne.
For example, biosensing. A single biomolecule is so small that it is essentially invisible. But passing light over a molecule hundreds or thousands of times can greatly increase the chance of creating a detectable scattering effect.
Dionne’s lab is working on applying this technique to detecting COVID-19 antigens - molecules that trigger an immune response - and antibodies - proteins produced by the immune system in response. "Our technology would give an optical readout like the doctors and clinicians are used to seeing," said Dionne. "But we have the opportunity to detect a single virus or very low concentrations of a multitude of antibodies owing to the strong light-molecule interactions." The design of the high-Q nanoresonators also allows each antenna to operate independently to detect different types of antibodies simultaneously.
Though the pandemic spurred her interest in viral detection, Dionne is also excited about other applications, such as LIDAR - or Light Detection and Ranging, which is laser-based distance measuring technology often used in self-driving vehicles - that this new technology could contribute to. "A few years ago I couldn’t have imagined the immense application spaces that this work would touch upon," said Dionne. "For me, this project has reinforced the importance of fundamental research - you can’t always predict where fundamental science is going to go or what it’s going to lead to, but it can provide critical solutions for future challenges."
This innovation could also be useful in quantum science. For example, splitting photons to create entangled photons that remain connected on a quantum level even when far apart would typically require large tabletop optical experiments with big expensive precisely polished crystals. "If we can do that, but use our nanostructures to control and shape that entangled light, maybe one day we will have an entanglement generator that you can hold in your hand," Lawrence said. "With our results, we are excited to look at the new science that’s achievable now, but also trying to push the limits of what’s possible."
Additional Stanford co-authors include graduate students David Russell Barton III and Jefferson Dixon, research associate Jung-Hwan Song, former research scientist Jorik van de Groep, and Mark Brongersma, professor of materials science and engineering. Jen is also an associate professor, by courtesy, of radiology and a member of the Wu Tsai Neurosciences Institute and Bio-X.


Till 2025, the collective sum of the world’s data will grow from 33 zettabytes this year to a 175ZB by 2025. The security and privacy of such sensitive data remain a big concern.
Emerging quantum communication and the latest computation technologies offer a promising solution. However, it requires powerful quantum optical circuits that can securely process the massive amounts of information we generate every day.
To help enable this technology, scientists in USC’s Mork Family Department of Chemical Engineering and Materials Science have made a breakthrough in quantum photonics.
A quantum optical circuit uses light sources to generate photons on-demand in real-time. The photons act as information-carrying bits (qubits).
These light sources are nano-sized semiconductor “quantum dots”–tiny manufactured collections of tens of thousands to a million atoms packed within a volume of linear size less than a thousandth of the thickness of typical human hair buried in a matrix of another suitable semiconductor.
They have so far been demonstrated to be the most flexible on-demand single-photon generators. The optical circuit requires these single-photon sources to be masterminded on a semiconductor chip. Photons with an almost identical wavelength from the sources should then be delivered a guided way. This permits them to be controlled to shape collaborations with different photons and particles to transmit and process information.
Until now, there has been a significant barrier to the development of such circuits. The dots have different sizes, and shapes mean that the photons they release do not have uniform wavelengths. This and the lack of positional order make them unsuitable for use in the development of optical circuits.
In this study, scientists showed that single photons could be emitted uniformly from quantum dots arranged precisely. Scientists used the method of aligning quantum dots to create single-quantum dot, with their remarkable single-photon emission characteristics.
It is expected that the ability to align uniformly-emitting quantum dots precisely will enable the production of optical circuits, potentially leading to novel advancements in quantum computing and communications technologies.
Jiefei Zhang, currently a research assistant professor in the Mork Family Department of Chemical Engineering and Materials Science, said, “The breakthrough paves the way to the next steps required to move from lab demonstration of single-photon physics to chip-scale fabrication of quantum photonic circuits. This has potential applications in quantum (secure) communication, imaging, sensing, and quantum simulations and computation.”
The corresponding author Anupam Madhukar said, “it is essential that quantum dots be ordered in a precise way so that photons released from any two or more dots can be manipulated to connect on the chip. This will form the basis of building unit for quantum optical circuits.”
“If the source where the photons come from is randomly located, this can’t be made to happen.”
“The current technology that allows us to communicate online, for instance using a technological platform such as Zoom, is based on the silicon integrated electronic chip. If the transistors on that chip are not placed in exact designed locations, there would be no integrated electrical circuit. It is the same requirement for photon sources such as quantum dots to create quantum optical circuits.”
Evan Runnerstrom, program manager, Army Research Office, an element of the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory, said, “This advance is an important example of how fundamental solving materials science challenges, like how to create quantum dots with precise position and composition, can have big downstream implications for technologies like quantum computing. This shows how ARO’s targeted investments in basic research support the Army’s enduring modernization efforts in areas like networking.”
Using a method called SESRE (substrate-encoded size-reducing epitaxy), scientists created a precise layout of quantum dots for the circuits. They then fabricated regular arrays of nanometer-sized mesas with a defined edge orientation, shape, and depth on a flat semiconductor substrate composed of gallium arsenide (GaAs). Quantum dots are then created on top of the mesas by adding appropriate atoms using the following technique.
Zhang said, “This work also sets a new world-record of ordered and scalable quantum dots in terms of the simultaneous purity of single-photon emission greater than 99.5%, and in terms of the uniformity of the wavelength of the emitted photons, which can be as narrow as 1.8nm, which is a factor of 20 to 40 better than typical quantum dots.”
“That with this uniformity, it becomes feasible to apply established methods such as local heating or electric fields to fine-tune the photon wavelengths of the quantum dots to exactly match each other, which is necessary for creating the required interconnections between different quantum dots for circuits.”
“We now have an approach and a material platform to provide scalably and ordered sources generating potentially indistinguishable single-photons for quantum information applications. The approach is general and can be used for other suitable material combinations to create quantum dots emitting over a wide range of wavelengths preferred for different applications, for example, fiber-based optical communication or the mid-infrared regime, suited for environmental monitoring and medical diagnostics.”
- Jiefei Zhang, Qi Huang, Lucas Jordao, Swarnabha Chattaraj, Siyuan Lu, Anupam Madhukar. Planarized spatially-regular arrays of spectrally uniform single quantum dots as on-chip single-photon sources for quantum optical circuits. APL Photonics, 2020; 5 (11): 116106 DOI: 10.1063/5.0018422


Many scientists like to trace the start of nanoscience as a field of study back to “There’s Plenty of Room at the Bottom,” Richard Feynman’s address to the American Physical Society in 1959. Feynman envisioned building molecules and devices from the bottom up, much like putting together a Lego castle. He did not think this approach would tell us much about fundamental physics, but it would deliver many new technological applications. While researchers have come a long way in using nanoscience to probe physics and especially quantum physics, this month we focus on applications that would certainly have shocked Feynman. These range from origami robots smaller than a grain of sand and reconstructing how cells duplicate DNA to quantum computing, nonlinear optics, and even mind control.
Origami robot smaller than a grain of sand
Paul McEuen and Itai Cohen of the Kavli Institute at Cornell for Nanoscale Science are known for using origami to create nanobots. Their new creation takes this to the extreme. One-twentieth the size of a grain of sand (60 microns wide), their new origami bird snaps into shape in just 100 milliseconds. All it takes is a single jolt of electricity and the platinum-titanium-titanium dioxide bird will fold itself up and hold that shape indefinitely. Applying a negative voltage returns the 30-atom-thick bird (3,300 times thinner than a sheet of paper) back into its original shape. Next on the agenda, the researchers want to implant semiconductor circuitry, sensors, and a source of power — they have used solar cells in the past — so the robot can perform some basic tasks.
We copy a light-year’s worth of DNA over our lifetimes. Scientists know a lot about the process, but we are far from understanding how the proteins that orchestrate this process place the right molecules in the right place at the right time to do it. Nynke Dekker, a member of Kavli Institute of Nanoscience Delft, and a team of researchers at the Francis Crick Institute, have unscrambled at least one part of the mystery. They started by attaching fluorescent labels to the origin recognition complex (ORC) protein, which begins the construction of the cell’s DNA replication machinery. They then watched as ORC diffused along strands of DNA—and came to a halt when it reached certain DNA sequences to kick off the process. They also discovered that a key “motor” for the replication machinery, know as MCM, exists in a previously unknown mobile form when not attached to ORC. By building this knowledge, Dekker hopes to one day construct a living synthetic cell from the ground up.
Counting to millions of qubits
Researchers have used several technologies to create circuits with up to 53 qubits (the quantum equivalents of transistors). Yet there is no clear way to scale these systems into the equivalent of digital chips packed with hundreds of millions or even billions of transistors. Now, Menno Veldhorst, a member of the Kavli Institute of Nanoscience Delft, thinks he may have found a technique that would scale up to millions of quantum elements. It is based on quantum dots, a technology often associated with high-definition televisions. In quantum computing, these artificial nanocrystals are used to trap an electron that researchers then entangle. So far, they have been unable to entangle more than two qubits. Veldhorst, however, switched to a system that works with holes (missing electrons) in germanium. Using simple circuitry, he has created a four-qubit grid that entangles easily and with very good control. Moreover, germanium is a well characterized semiconductor that interfaces well with other materials (like superconductors) being considered for quantum devices.
A simpler way to control nonlinear devices
Nonlinear devices are systems where one plus one does not equal two. Take, for example, nonlinear optical devices, which change light from one frequency to another. When you add strong nonlinearity to resonators, which trap and circulate light for lasers and other devices, the result is something Kavli Nanoscience Institute at Caltech member Alireza Marandi calls a “rich physics regime." That is another way of saying “really, really complicated,” which is the opposite of what engineers need if they want to design useful nonlinear resonators. Yet Marandi may have a solution for them. While testing a nonlinear resonator made of optical fiber and a nonlinear waveguide, he found that at certain lengths, the light entering the system made abrupt transitions to other frequencies (colors). This finding would enable engineers to tune a nonlinear resonator using just one variable. One day, Marandi speculates, this could lead to optical computers that would count colors the way digital computers count electric charges.
For years, researchers have pursued technologies that would improve the ability of people who are paralyzed to communicate and control their surroundings by controlling a keyboard or robotic arm with their minds alone. The problem has always been reading the mind with enough precision to do something useful. Implanted electrodes in the brain do this, but they are invasive and potentially dangerous. Functional MRI works but requires costly machinery. Electroencephalography (EEG) is neither bulky nor expensive, but it lacks resolution. Now, a collaborative team that includes Kavli Nanoscience Institute at Caltech member Mikhail Shapiro, has developed a way to use ultrasound to read the part of the brain that controls motion planning. It works by measuring the movement of blood flowing through the brain. While still in its infancy, the technology can predict the arm and eye movements of two monkeys with good accuracy. The researchers hope they can develop the technology so that it is one day practical.


What is 15
15 (fifteen) customary number following 14 preceding 16.
what is 15 is:
- A lucky number
- A triangular number.
- A hexagon number.
- A pentatope number.
Along with 13, one of the two numbers within the juvenile number range (13-19) so as not to use a single-digit number in their name prefix (the first syllable before the juvenile suffix); Instead, use the adjective form of five (fifth) as a prefix.
The fifth bell number (i.e. the number of partitions for a set of size 4).
a composite number; its correct divisors are 1, 3, and 5.
the number of supersingular primes.
A repdigit in binary (1111) and quaternary (33). In hexadecimal representation and all high bases, what is 15 is represented as F.
the smallest number that can be factored in using Shor’s quantum algorithm.
the magic constant of the standard magic square of single order-3:
There are what is 15 perfect matches of the complete K6 graph and 15 binary trees with roots and four labelled leaves, both among the object types counted by double factorials. With only two exceptions, all prime quadruplets enclose a multiple of 15, and 15 itself is surrounded by the quadruple (11, 13, 17, 19).
what is 15? perfect combinations of K6
Since what is 15 is the product of various Fermat primes 3 and 5, a regular what is 15 -sided polygon can be constructed using an unmarked compass and ruler.
what is 15 is expressible by square roots.
If a positive definite matrix of integers quadratic form represents all positive integers up to 15, it means all positive integers through the what is 15 sets 15 and 290.
what is 15 contains the decimal digits 1 and 5 and is the result of adding the whole numbers from 1 to 5 (1 + 2 + 3 + 4 + 5 = 15)
NCERT Solutions for Class 10 Maths Subdivision what is 15
The branch of mathematics that involves numerical descriptions of the outcome of an event, or whether or not it is actual, is called probability. In this regard, what is 15 vedantu provides accurate NCERT solutions for class 10 mathematical probabilities that include different types of sums that you can expect in exams. This is a scoring but tricky chapter of grade 10 math, so you should know the tips and tricks needed to solve number problems quickly.
NCERT Maths Class 10 what is 15 Chapter Solution Guide is selected according to the latest CBSE Board Rules. The summaries of each topic are resolved with precision to clarify your concepts. Also, it is available in PDF format, and you can study it on the Vedantu website. Alternatively, you can even download it for free. You can also download NCERT Grade 10 Scientific Solutions and use them in your preparation.
Access NCERT Solutions for Grade 10 Mathematics Chapter what is 15 – Probability
- Complete the following statements:
- Probability of event E + Probability of event “not E” = _____.
If the probability of an event is pp, then the probability of “no event” is 1−p1−p. So the sum is p+1-p=1p+1-p=1.
- The probability that an event will not occur is _____.
The probability that an event will not occur is always 00.
iii. The probability that an event occurs is _____.
The probability that an event occurs is 11. Such an event is called a safe event.
- The sum of the prospects of all elementary events in an experiment is _____.
The quantity of the likelihoods of all specific events in an experiment is 11.
- The probability of an event is inordinate than or equal to and less than or
is equal to _____.
The probability of an event is greater than or equal to 00 and less than or equal to 11.
- Which of the following experiments has equally likely outcomes? To explain.
- A driver tries to start a car. The car does not start or start.
Equally likely outcomes defined as when each result is as expected to occur as the others. Therefore, the products are not equally likely.
- A player efforts to shoot a basketball. He shoots or misses the shot.
The outcomes are not equally likely.
An attempt to answer true/false question made. The answer is correct or incorrect.
Outcomes are equally likely outcomes.
(iv) A baby is born. She is a boy or a girl.
The outcomes are not equally likely.
- Why is flipping a coin considered a fair way to decide which team should receive the ball at the start of a soccer game?
We already know that there are only two sides to a coin, heads and tails so when we flip a coin, we get heads or tails there is no chance of the currency falling on edge and on the other hand, the opportunities of heads and tails are also equal. From this, it can be conclud that tossing a coin is a fair way to decide the outcome as it cannot be biase, and both teams have an equal chance of winning.


Professor Philip Walther – Indefinite Causal Order: Faster Computers and Fundamental Questions
Quantum mechanics has greatly improved the speeds at which computers make calculations, but new research shows that quantum computers can be made to run even faster. Professor Philip Walther and his team at the University of Vienna have shown that the very orders in which quantum computers carry out operations can be superimposed, essentially meaning that two or more operations can be carried out at the same time. This work could give rise to even more efficient quantum computers in the near future, but also leaves some baffling questions about our physical understanding of the Universe.
A Computational Revolution
Over recent decades, research into quantum computing has laid the foundation for devices that will greatly improve the efficiency of classical computers. In regular digital computers, data is encoded into binary digits in two definite states – 0 and 1. These ‘bits’ of data are processed by logic gates, which output further bits whose states depend on those of the input bits. When sequences of these logic gates are arranged into circuits, they output information that acts as instructions to the computer, telling it what to do next.
In quantum computers, however, quantum properties can be exploited to superimpose multiple states onto individual particles. These particles, known as quantum bits, or ‘qubits’, can essentially carry multiple 0s and 1s at the same time. As they pass through a quantum logic gate, all 0s and 1s are processed simultaneously.
Quantum algorithms are designed so that the co-existence of 0s and 1s is affected by destructive and constructive interference, until only the 0s and 1s that are the sole output of the calculation are left. This approach offers huge advantages over regular computers, as instead of every bit of data corresponding to a single input state, many input states can be encoded onto just a few qubits, enabling the co-existence or ‘superposition’ of many different input states.
This massive parallelism of input data and the possibility of having quantum circuits allows for quantum algorithms that require significantly fewer steps than classical algorithms in conventional computers. Overall, this means that quantum computers allow operations to be carried out far more efficiently, which reduces not only computational speeds, but energy consumption, and therefore costs.
‘It is truly remarkable that quantum physics keeps surprising us about possible concepts and applications for which quantum mechanical features can be exploited – and I am sure that we are still at the beginning of this journey’
However, Professor Philip Walther and his team at the University of Vienna believe that more complex quantum mechanical processes can be exploited to improve the efficiency of quantum computers even further.
Even Faster Speeds for Quantum Computers
In a 2015 study, Professor Walther and his colleagues showed that quantum mechanics allows for the superposition of not just quantum states on a single particle, but of entire circuits of quantum gates. This means that the order in which operations are carried out on sequences of quantum gates is indefinite. In other words, multiple operations could essentially be carried out at the same time.
The team demonstrated that if a gate can be used multiple times, fewer gates need to be used overall, increasing the efficiency of the computer. By superimposing multiple circuits, the researchers could control which circuit was applied to an input qubit. Therefore, they could test whether the superposition of multiple circuits really improved computation speed by calculating the reduction in ‘query complexity’ (calculated from the smallest number of queries required to calculate a function) compared with conventional quantum computers.
To implement their ideas experimentally, Professor Walther’s team created a simple quantum circuit, consisting of two logic gates they named Alice and Bob. Typically, an input qubit would either be sent from Alice to Bob or from Bob to Alice, resulting in two possible paths. However, the researchers added a layer of complexity to the scenario by encoding two qubits into the same photon (light particle) by using its path and polarisation as the variable parameters. The two qubits were named the ‘control qubit’, which would be acted upon by the scientists, and the ‘target qubit’, which would itself pass through the logic gates.
The control qubit acts on the target qubit by defining the order of gate operations through which the target (input) qubit will propagate. When the control qubit is in one state, then the target qubit will first pass through Alice, and then through Bob, while when the control qubit is in the other state, the target qubit will pass through Bob first, and then Alice. Now, when the control bit is prepared in the superposition of both states, then the target qubit will have superimposed or indefinite orders: both Alice to Bob, and Bob to Alice. Therefore, the path taken by the target qubit depends entirely on the preparation of the control qubit.
How Much Faster?
When Alice and Bob are quantum gates, then this superposition of quantum gate orders is indefinite and does not allow us to know, even in principle, if one operation occurred before another operation, or the other way around. This means that two quantum logic gates A (for Alice) and B (for Bob) can be applied in both orders at the same time. In other words, gate A acts before B and B acts before A. Professor Walther’s team designed an experiment in which the two quantum logic gates were applied to single photons in both orders.
The results of their experiment confirm that it is impossible to determine which gate acted first – but the experiment was not simply a curiosity. In fact, they were able to run a quantum algorithm to characterise the gates more efficiently than any previously known algorithm. From a single measurement on the photon, they probed a specific property of the two quantum gates thereby confirming that the gates were applied in both orders at once.
For future developments as more gates are added to the task, this new method for quantum computers becomes even more efficient compared to previous techniques.
The idea of ‘causality’ is fundamental to our understanding of how the Universe works. It defines the link between physical events that follow each other chronologically. If one event happens before a second event it’s linked to, it seems logical to us that the first event was the cause of the second – in other words, a ‘definite causal order’.
However, in their exploration of the properties of quantum mechanics that allowed them to achieve faster computational speeds, Professor Walther’s team realised that their experiment appeared to utilise ‘indefinite causal order’. In their initial experiment, Professor Walther’s team could not observe indefinite causal order directly. The researchers had confirmed and quantified its apparent consequences with the faster computational speeds achieved, but they hadn’t yet measured the quantum mechanical properties that would confirm whether the causal order of the use of Alice and Bob was truly indefinite.
To do this, they had to go significantly beyond the previous experiment by experimentally superimposing more complex processes for A and B. These processes included quantum measurements acting on the target bit when passing through Alice. Importantly, for enabling this in a circuit, the order of multiple quantum operations can be superimposed, and both possible outcomes of Alice were processed into Bob, or vice versa.
From then on, there would be no chance to ever read the outcome of the initial gate – a measurement could only be made at the very end of the process, meaning it could never be determined which path was actually taken. This allowed the team to characterise the indefinite causal order by acquiring information from inside (where the superposition of causal orders take place) and outside, where the result after the processing through the circuit can be measured.
As Professor Walther’s team mention in their paper, ‘this can lead to disconcerting consequences, forcing one to question concepts that are commonly viewed as the main ingredients of our physical description of the world. But these effects can be exploited to achieve improvements in computational complexity and quantum communications.’ It’s a somewhat startling idea. On a quantum scale, the comfortable notion that an outcome can be directly attributed to distinct previous events does not always hold, and yet this mysterious property can be exploited for our benefit.
The work of Professor Walther and his colleagues has opened up a wide avenue of possibilities in quantum computing. There is now much progress both in increasing speeds and reducing costs of quantum computers in the near future – a significant step towards making them widely commercially available.
Meet the researcher
Professor Philip Walther
Faculty of Physics
University of Vienna
Professor Philip Walther completed his PhD in Physics at the University of Vienna in 2005, after which he took a post as a postdoctoral researcher at Harvard University. He returned to Vienna in 2009, and is now a tenured Professor at the Faculty of Physics. His areas of research include various fields in the development of quantum computing, and investigating the interface between quantum physics and gravity. Professor Walther co-founded the TURIS research platform in 2017. He has received a variety of prestigious awards for his work and has been elected as a member of the Young Academy at the Austrian Academy of Sciences and as fellow of the American Physical Society.
G Rubino, LA Rozema, A Feix, M Araújo, JM Zeuner, LM Procopio, Č Brukner, P Walther, Experimental verification of an indefinite causal order, Science Advances, 2017, 3, e1602589.
LM Procopio, A Moqanaki, M Araújo, F Costa, I Alonso Calafell, EG Dowd, DR Hamel, LA Rozema, Č Brukner, P Walther, Experimental superposition of orders of quantum gates, Nature Communications, 2015, 6, 7913.


Quantum computing promises to harness the strange properties of quantum mechanics in machines that will outperform even the most powerful supercomputers of today. But the extent of their application, it turns out, isn’t entirely clear.
To fully realize the potential of quantum computing, scientists must start with the basics: developing step-by-step procedures, or algorithms, for quantum computers to perform simple tasks, like the factoring of a number. These simple algorithms can then be used as building blocks for more complicated calculations.
Prasanth Shyamsundar, a postdoctoral research associate at the Department of Energy’s Fermilab Quantum Institute, has done just that. In a preprint paper released in February, he announced two new algorithms that build upon existing work in the field to further diversify the types of problems quantum computers can solve.
“There are specific tasks that can be done faster using quantum computers, and I’m interested in understanding what those are,” Shyamsundar said. “These new algorithms perform generic tasks, and I am hoping they will inspire people to design even more algorithms around them.”
Shyamsundar’s quantum algorithms, in particular, are useful when searching for a specific entry in an unsorted collection of data. Consider a toy example: Suppose we have a stack of 100 vinyl records, and we task a computer with finding the one jazz album in the stack.
Classically, a computer would need to examine each individual record and make a yes-or-no decision about whether it is the album we are searching for, based on a given set of search criteria.
“You have a query, and the computer gives you an output,” Shyamsundar said. “In this case, the query is: Does this record satisfy my set of criteria? And the output is yes or no.”
Finding the record in question could take only a few queries if it is near the top of the stack, or closer to 100 queries if the record is near the bottom. On average, a classical computer would locate the correct record with 50 queries, or half the total number in the stack.
A quantum computer, on the other hand, would locate the jazz album much faster. This is because it has the ability to analyze all of the records at once, using a quantum effect called superposition.
With this property, the number of queries needed to locate the jazz album is only about 10, the square root of the number of records in the stack. This phenomenon is known as quantum speedup and is a result of the unique way quantum computers store information.
The quantum advantage
Classical computers use units of storage called bits to save and analyze data. A bit can be assigned one of two values: 0 or 1.
The quantum version of this is called a qubit. Qubits can be either 0 or 1 as well, but unlike their classical counterparts, they can also be a combination of both values at the same time. This is known as superposition, and allows quantum computers to assess multiple records, or states, simultaneously.
“If a single qubit can be in a superposition of 0 and 1, that means two qubits can be in a superposition of four possible states,” Shyamsundar said. The number of accessible states grows exponentially with the number of qubits used.
Seems powerful, right? It’s a huge advantage when approaching problems that require extensive computing power. The downside, however, is that superpositions are probabilistic in nature — meaning they won’t yield definite outputs about the individual states themselves.
Think of it like a coin flip. When in the air, the state of the coin is indeterminate; it has a 50% probability of landing either heads or tails. Only when the coin reaches the ground does it settle into a value that can be determined precisely.
Quantum superpositions work in a similar way. They’re a combination of individual states, each with their own probability of showing up when measured.
But the process of measuring won’t necessarily collapse the superposition into the value we are looking for. That depends on the probability associated with the correct state.
“If we create a superposition of records and measure it, we’re not necessarily going to get the right answer,” Shyamsundar said. “It’s just going to give us one of the records.”
To fully capitalize on the speedup quantum computers provide, then, scientists must somehow be able to extract the correct record they are looking for. If they cannot, the advantage over classical computers is lost.
Amplifying the probabilities of correct states
Luckily, scientists developed an algorithm nearly 25 years ago that will perform a series of operations on a superposition to amplify the probabilities of certain individual states and suppress others, depending on a given set of search criteria. That means when it comes time to measure, the superposition will most likely collapse into the state they are searching for.
But the limitation of this algorithm is that it can be applied only to Boolean situations, or ones that can be queried with a yes or no output, like searching for a jazz album in a stack of several records.
Scenarios with non-Boolean outputs present a challenge. Music genres aren’t precisely defined, so a better approach to the jazz record problem might be to ask the computer to rate the albums by how “jazzy” they are. This could look like assigning each record a score on a scale from 1 to 10.
Previously, scientists would have to convert non-Boolean problems such as this into ones with Boolean outputs.
“You’d set a threshold and say any state below this threshold is bad, and any state above this threshold is good,” Shyamsundar said. In our jazz record example, that would be the equivalent of saying anything rated between 1 and 5 isn’t jazz, while anything between 5 and 10 is.
But Shyamsundar has extended this computation such that a Boolean conversion is no longer necessary. He calls this new technique the non-Boolean quantum amplitude amplification algorithm.
“If a problem requires a yes-or-no answer, the new algorithm is identical to the previous one,” Shyamsundar said. “But this now becomes open to more tasks; there are a lot of problems that can be solved more naturally in terms of a score rather than a yes-or-no output.”
A second algorithm introduced in the paper, dubbed the quantum mean estimation algorithm, allows scientists to estimate the average rating of all the records. In other words, it can assess how “jazzy” the stack is as a whole.
Both algorithms do away with having to reduce scenarios into computations with only two types of output, and instead allow for a range of outputs to more accurately characterize information with a quantum speedup over classical computing methods.
Procedures like these may seem primitive and abstract, but they build an essential foundation for more complex and useful tasks in the quantum future. Within physics, the newly introduced algorithms may eventually allow scientists to reach target sensitivities faster in certain experiments. Shyamsundar is also planning to leverage these algorithms for use in quantum machine learning.
And outside the realm of science? The possibilities are yet to be discovered.
“We’re still in the early days of quantum computing,” Shyamsundar said, noting that curiosity often drives innovation. “These algorithms are going to have an impact on how we use quantum computers in the future.”
This work is supported by the Department of Energy’s Office of Science Office of High Energy Physics QuantISED program.
The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit science.energy.gov.
Source: Fermilab /


In what may best be described as a quantum leap, a group of researchers from the University of Sussex have unveiled what they claim is the first realistic blueprint for the construction of a large scale quantum computer.
As detailed in a paper published Wednesday in Science Advances, the quantum computer designed by the Sussex team leverages a new device they've created that allows quantum information to pass from one microchip of the quantum computer, to another using electric fields instead of fiber optic cables. This would allow for connection speeds between the microchips that are up to 100,000 times faster than those currently achievable using fiber optics.
"There have been many studies where people made certain innovations to put us one step closer to a quantum computers," Winfried Hensinger, the head of the Ion Quantum Technology Group at the University of Sussex, told me. "But what we have done is quite a bit different: we've developed a nuts and bolts construction plan to build a large scale quantum computer."
In other words, the Sussex group has taken a number of separate innovations in the field of quantum computing and brought them together to create a fine-grained blueprint for what is needed to build the first large scale quantum computer, covering everything from the back-of-house electronics to the power requirements for the machine.
A large scale quantum computer would revolutionize the world of computing due to its ability to perform calculations that are impossible for a classical, binary computer to solve. This is a result of the way that quantum computers process information—unlike a classical computer, which stores information in bits (either a 1 or a 0), a quantum computer traffics in qubits, which can either be a 0, 1, or a combination of these two states at the same time (a property known as superposition). Experts worry that quantum computers will be able to easily break some of our most widely used forms of encryption today, and are preparing for that eventuality now.
There are a number of proposals for how to actually go about building a quantum computer, but the most promising—and the kind described by the Sussex blueprint—is known as a trapped ion quantum computer. As its name suggests, this type of quantum computer makes use of ions (an atom or molecule with an electric charge) that are 'trapped' in electromagnetic fields.
By changing the state of these ions—using microwaves to move an atom's electrons from one energy level to another—researchers make them function as qubits, or vessels of quantum information. To create a quantum computer, these qubits must interact with one another either by being physically moved from one location to another with lasers, or by emitting photons which are then transported through a fiber optic cable.
In the Sussex blueprint, the quantum computer consists of a collection of hand-sized microchip modules, each of which, according to Hensinger, will be capable of trapping around 2,500 ions. When voltages are applied to these microchips, they create the electric field which traps the ions and levitates them above the microchip. Yet rather than having ions from one microchip interact with ions on another microchip using a complicated setup involving lasers or fiber optic cables, Hensinger and his colleagues have invented a device which uses the electric field itself to transport the ions from one microchip to an adjacent microchip.
"They're trying to make the mechanisms for controlling and manipulating the qubits a lot easier," said Michele Mosca, a co-founder of the Institute for Quantum Computing at the University of Waterloo, who was not involved with the blueprint. "So instead of having countless lasers addressing individual ions, they want to use this [electric field] approach. It's impressive work."
Aside from a much faster connection speed than using fiber optics to connect the microchip modules, the Sussex team's device offers another key improvement: much simpler and cheaper technology. Lasers work great when you're talking about a quantum computer that is only manipulating a handful of ions, but a large scale quantum computer that would be capable of, say, breaking the encryption standards used today would consist of millions of ions. This in turn would require millions of lasers, making it impractical with current technologies. Indeed, so far researchers have struggled to build trapped ion devices which are capable of manipulating more than about a dozen qubits.
The Sussex blueprint, on the other hand, should be achievable with currently available technologies and able to manipulate far more qubits. Hesinger hopes that he and his colleagues will be able to build a small prototype over the next two years to prove the feasibility of their design. The prototype would only consist of only two microchips, but if it works, it could be the basis of a large scale quantum computer consisting of millions of ions and occupying a space the size of a football field (not to mention costing upwards of $120 million).
How long it will take to get to a large scale quantum computer is anybody's guess. Just as a classical computer wasn't made in a day, the quantum computer will emerge in increments—the point, according to Hesinger, is that it's time to start building it. "This is not something we can do overnight, but our blueprint specifies what needs to be done," said Hesinger. "It won't be cheap or easy, but I think we're at a place now where we can think about engineering we need to do to build this machine."
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Most experts agree that quantum computing is still in an experimental era. The current state of quantum technology has been compared to the same stage that classical computing was in during the late 1930s.
Quantum computing uses various computation technologies, such as superconducting, trapped ion, photonics, silicon-based, and others. It will likely be a decade or more before a useful fault-tolerant quantum machine is possible. However, a team of researchers at MIT Lincoln Laboratory has developed a vital step to advance the evolution of trapped-ion quantum computers and quantum sensors.
Most everyone knows that classical computers perform calculations using bits (binary digits) to represent either a one or zero. In quantum computers, a qubit (quantum bit) is the fundamental unit of information. Like classical bits, it can represent a one or zero. Still, a qubit can also be a superposition of both values when in a quantum state.
Superconducting qubits, used by IBM and several others, are the most commonly used technology. Even so, trapped-ion qubits are the most mature qubit technology. It dates back to the 1990s and its first use in atomic clocks. Honeywell and IonQ are the most prominent commercial users of trapped ion qubits.
Trapped-Ion quantum computers
Honeywell and IonQ both create trapped-ion qubits using an isotope of rare-earth metal called ytterbium. In its chip using integrated photonics, MIT used an alkaline metal called strontium. The process to create ions is essentially the same. Precision lasers remove an outer electron from an atom to form a positively charged ion. Then, lasers are used like tweezers to move ions into position. Once in position, oscillating voltage fields hold the ions in place. One main advantage of ions lies in the fact that it is natural instead of fabricated. All trapped-ion qubits are identical. A trapped-ion qubit created on earth would be the perfect twin of one created on another planet.
Dr. Robert Niffenegger, a member of the Trapped Ion and Photonics Group at MIT Lincoln Laboratory, led the experiments and is first author on the Nature paper. He explained why strontium was used for the MIT chip instead of ytterbium, the ion of choice for Honeywell and IonQ. “The photonics developed for the ion trap are the first to be compatible with violet and blue wavelengths,” he said. “Traditional photonics materials have very high loss in the blue, violet and UV. Strontium ions were used instead of ytterbium because strontium ions do not need UV light for optical control.”
All the manipulation of ions takes place inside a vacuum chamber containing a trapped-ion quantum processor chip. The chamber protects the ions from the environment and prevents collisions with air molecules. In addition to creating ions and moving them into position, lasers perform necessary quantum operations on each qubit. Because lasers and optical components are large, it is by necessity located outside the vacuum chamber. Mirrors and other optical equipment steer and focus external laser beams through the vacuum chamber windows and onto the ions.
The largest number of trapped-ion qubits being used in a quantum computer today is 32. For quantum computers to be truly useful, millions of qubits are needed. Of course, that means many thousands of lasers will also be required to control and measure the millions of ion qubits. The problem becomes even larger when two types of ions are used, such as ytterbium and barium in Honeywell’s machine. The current method of controlling lasers makes it challenging to build trapped-ion quantum computers beyond a few hundred qubits.
Rather than resorting to optics and bouncing lasers off mirrors to aim beams into the vacuum chamber, MIT researchers have developed another method. They have figured out how to use optical fibers and photonics to carry laser pulses directly into the chamber and focus them on individual ions on the chip.
A trapped-ion strontium quantum computer needs lasers of six different frequencies. Each frequency corresponds to a different color that ranges from near-ultraviolet to near-infrared. Each color performs a different operation on an ion qubit. The MIT press release describes the new development this way, “Lincoln Laboratory researchers have developed a compact way to deliver laser light to trapped ions. In the Nature paper, the researchers describe a fiber-optic block that plugs into the ion-trap chip, coupling light to optical waveguides fabricated in the chip itself. Through these waveguides, multiple wavelengths [colors] of light can be routed through the chip and released to hit the ions above it.”
In other words, rather than using external mirrors to shine lasers into the vacuum chamber, MIT researchers used multiple optical fibers and photonic waveguides instead. A block equipped with four optic fibers delivering a range of colors was mounted on the quantum chip’s underside. According to Niffenegger, “Getting the fiber block array aligned to the waveguides on the chip and applying the epoxy felt like performing surgery. It was a very delicate process. We had about half a micron of tolerance, and it needed to survive cool down to 4 Kelvin.”
I asked Dr. Niffenegger his thoughts about the long-term implications of his team’s development. His reply was interesting.
“I think many people in the quantum computing field think that the board is set and all of the leading technologies at play are well defined. I think our demonstration, together with other work integrating control of trapped ion qubits, could tip the game on its head and surprise some people that maybe the rules aren’t what they thought. But really I just hope that it spurs more out of the box ideas that could enable quantum computing technologies to break through towards practical applications.”
- Integrating optical waveguides into ion traps represents a step forward toward the goal of building a useful quantum computer with thousands to millions of qubits.
- MIT’s technique also provides a development path for portable trapped-ion quantum sensors and clocks.
- Integrated photonics is inherently resistant to vibrations. With external lasers, vibrations cause pulses to miss the ion. Integrated optics should eliminate most effects of vibrations.
- The stability offered by integrated photonics will help qubits maintain quantum states longer so that deeper and more complex computations can be performed.
- Initially I had some concerns about loss of optical power due to compromises that may have been made in the grating coupler to accommodate different wavelengths. Keep in mind there are four fibers and six colors. The shortest of the six laser wavelengths is 405 nm and the longest is 1092 nm. Dr. Niffenegger pointed out there are separate gratings for the shortest and longest wavelengths. He also said there are some power losses, but they are in the path from where light enters the optical waveguide to where it exits the coupler grating. Despite this minor optical power loss, tighter focus provided by the existing diffraction gratings provides enough power for operations on the ions.
- Dr. Niffenegger and the MIT research team will focus future research on reducing two qubit gate errors caused by heating of the motional state of ion qubits. The rate at which ions heat up is much higher in traps with integrated photonic chips than traditional surface traps without photonics
Note: Moor Insights & Strategy writers and editors may have contributed to this article.


Quantum Computing is based on physical materials that deal with very low temperature close to absolute zero, today, in order to increase abilities of Quantum Computers and to make them more convenient, the most important question is to handle is temperature problem. Semiconducting materials are the best choices to solve low temperature problem by approaching room temperature conditions. Yet, since many semiconducting materials can have many quantum
degrees of freedom, the qubits may interact and dechore quickly. Thanks to growing atomic engineering and advanced semiconductor fabrication technologies, these effects are reducing day by day. Hence, in this presentation, I’m going to talk about semiconductor roles in Q.C. and how to people solve (their approach to solve) the interaction problem with qubits.
Today, compared to classical computing (e.g. classical computers), quantum computing is the most effective way to store and manipulate information. For instance, instead of capacitors in classical computers where we store information such as empty ones (0’s) and filled ones (1’s), in quantum computing we are using quantum states (quantum bits – qubits) with quantum mechanical properties. Hence, we don’t only use zeros and ones as binary states from classical computers but we also use quantum states that represents zeros and ones at the same time.
(a) Quantum Mechanical Properties of a Qubit
In quantum computing, we owe the quantum mechanical properties that provides the best ability to store and manipulate information such as; -superposition-, -entanglement-, interference- .
✦ Quantum superposition: If we add two or more quantum states, their result will also be a quantum state.
✦ Quantum entanglement: If you have two identical particles and if you separate them (very far distance), a situation effects also effects the very distant one.
✦ Quantum interference: A particle can’t be more than one place at the same time, but sometimes it crosses its own trajectory and interfere its own path.
(b) Structure of Solid State Q.C.
✦ (1) & (5) are the amplifiers that capture and process read out signals.
✦ (2) & (3 ) transmits the input and output qubits respectively .
✦ (4) enables qubits signals to go forward while preventing noise from compromising qubit quality.
✦ (6) the quantum processor sits inside a shield that protects it from electromagnetic radiation.
✦ (7) provides the necessary cooling power.
(c) Heat in Solid State Q.C.
As I mentioned in part (b), we are dealing with very low temperatures close to absolute zero (~ 0°K). Since dealing with qubits, we don’t want them to dechore quickly. While the temperature is getting lower, the degrees of freedom is getting reduced. If we consider %75 of a Q.C. as a refrigerator, it seems impossible to operate Q.C. in room temperature before qubit decoherence occurs.
How to Run a Q.C. in Room Temperature Conditions ?
In 2013, Canadian researches stored a qubit in room temperature for 39 minutes by stored quantum information in the nuclear spins of phosphorous-31 atoms in a silicon-28 crystal. Since, phosphorous atoms in silicon at room temperature tend to give up their electrons and become positive ions, at first they cooled its crystal to 4.2 °K and used laser and radio frequency (RF) pulses to put neutral phosphorous atoms into specific quantum states. A laser pulse then ionized the atoms before the crystal was warmed up to room temperature (~ 298°K). [Original article is placed to bottom]
As a result, RF pulses were used to perform a “spin echo” (refocusing of spin magnetisation by a pulse of resonant electromagnetic radiation) measurement of the coherence time, which was found to be 39 minutes. Thus, imagine that, what if we reduce %75 cooler part of the quantum computer and optimize for our daily life ? With that much computational power, our classical computers would be like todays calculators. Imagine that simulations that takes months with performed by classical computers would be done in hours at your home. Therefore, it’s the future obviously. However, except the advantages of using nuclear spin qubits, there are also disadvantages.
Challenges of Using Semiconductor Qubits
In semiconductors many quantum degrees of freedom are present, and all tend to interact with each other. Thus, semiconductor qubits may decohere rapidly and in order to store and manipulate information quantum logic operations must be performed on a qubit before decoherence occurs. In order to avoid decoherence, devices must be engineered at or near the atomic level with respect to spin-orbit interaction.
Most effective semiconductor fabrication techniques, to avoid spin-orbit interaction problems, are SRT-Embedded Heterostructures and Quantum Dot Arrays. Heterostructures are basically semiconductor structures where chemical composition changes with respect to position. In our case, it’s beneficial to use SRT (Spin Resonance Transistors) Embedded Heterostructures, since it enables quantum entanglement between qubits. Hence, we can use them for quantum logic gates (e.g. CNOT gates) on the surface of the semiconductor heterostructure. The other remarkable technique is the Quantum Dot Arrays where we can use them to lower electron tunneling barrier when two qubits couple, placed on top of a semiconductor heterostructure. Therefore, we can use them as entanglement switches such as, when the electrical field is turned off, the quantum dot qubits entangle.
To sum up, Quantum Computing is the most efficient way for computational operations today and also future. However, since it operates low temperatures close to absolute zero and to provide that temperature conditions, its %75 of the structure is consistent of cooler mechanisms and its cost due to that needs don’t make them the number one choice. However, in 2013, the Canadian researchers stored a quantum state (a.k.a qubit) in room temperature conditions and show that we can optimize a solid state quantum computer by solving the low temperature problem. If we solve the problem, quantum computers would be smaller (like classical computers) and will be inevitable to operate that much computational power at our homes. Yet, since optimizing the computer structure and dealing with spin-orbit interactions, semiconductor structure must be fabricated at atomic level. The most effective fabrication processes are SRT- Embedded Heterostructures and Quantum Dot Arrays to provide quantum mechanical properties of qubits and computational needs (store-manipulate) of the computer. Thus, in the future we would be using our quantum computers, since researches are accelerated that much.
Semiconductor Qubits for Quantum Computation, presentation by Matthias Fehr (TU Munich) JASS (2005) St.Petersburg/Russia — Semiconductor devices for Quantum Computing, presentation by Bruce Kane(University of Maryland) (2004) — http://www.ibm.com/quantum-computing/ — http://www.semiengineering.com/quantum-computing-becoming-real –www.eetimes.com/purdue-builds-quantum-computing-semiconductor-chip/# — Room-Temperature Quantum Bit Storage Exceeding 39 Minutes Using Ionized Donors in Silicon-28. Saeedi, Simmons, Salvail, et al. Science 342 (6160): 830-833 (2013)


Quantum computing is based on quantum mechanics, which governs how nature works at the smallest scales. The smallest classical computing element is a bit, which can be either 0 or 1. The quantum equivalent is a qubit, which can also be 0 or 1 or in what’s called a superposition — any combination of 0 and 1. Performing a calculation on two classical bits (which can be 00, 01, 10 and 11) requires four calculations. A quantum computer can perform calculations on all four states simultaneously.
This scales exponentially: 1,000 qubits would, in some respects, be more powerful than the world’s most powerful supercomputer.
In this digital-oriented world, hackers are evolving in parallel to technological advancements. Fortunately, engineers, mathematician and physicists are simultaneously working on innovative concepts that harness the progression of classical encryption methods. New devices are utilizing principles of quantum physics and deploying sophisticated and powerful algorithms for safe communication.
What is cryptography?
Cryptography is a means of securing data and information to dodge malicious hackers. Thanks to cryptographic methods, everything from web conferences to individual browsing history remain privileged and safe. Data are protected using algorithms that require a unique key for decryption and encryption. Utilization of the same private key, i.e. a specific string of bits for decryption and encryption, is called symmetric cryptography. Utilization of public keys for encryption and private keys for decryption — each of which are created by algorithm-fuelled random number generators — is called asymmetrical cryptography.
Genuine randomness is considered unachievable by purely classical means, but can be accomplished with the added application of quantum physics.
Quantum key distribution
There are two methods by which large-scale quantum and classical computers can obscure private information.
• Method #1: Recover the key generated during the key agreement phase.
• Method #2: Interrupt the encryption algorithm.
Quantum key distribution (QKD) is a quantum cryptographic primitive designed to generate unbreakable keys. QKD ensures key agreement, including well-known BB84 and E91 algorithms. In 2017, a Chinese team successfully demonstrated that satellites can perform safe and secure communications with the help of symmetrical cryptography and QKD.
Still, it’s clear that QKD alone can’t satisfy all protection requirements, but there are other mechanisms for security enhancement by utilizing “quantum-safe” encryption algorithms based on solving mathematical problems instead of laws of quantum physics.
An optimistic view of quantum-computing obstacles
The most immediate challenge is accomplishing the most sufficient number of fault-tolerant qubits to boost quantum computing’s computational promises. Tech giants such as Google, Amazon, IBM and Honeywell are taking this problem under consideration and investing in it to come up with a solid solution.
Currently, quantum computers are programmed for individual quantum logic gates. This might be acceptable for small-scale quantum computers, but less so once we come across a large number of qubits.
Organizations such as IBM and Classiq are developing more and more abstract layers in the programming stack, allowing developers to nurture incredible and powerful quantum applications to provide solutions to real-world problems.
For the implementation of complex problems including error-correction schemes, organizations need to prove that they can control numerous qubits. This control must have low latency and it must come from adaptive-feedback control circuits based on CMOS. Ultimately, the issue of “fan-out” must be addressed. The question that needs to be answered is how to pace up a number of qubits within a quantum chip. Multiple lasers or control wires are currently required, but it’s hard to see how we can develop multiple qubit chips with millions of wires connected to the circuit board or coming out of the cryogenic measurement chamber.
Applying quantum computing to cybersecurity
In recent years, researchers and analysts have been striving for the development of quantum-safe encryption. According to American Scientist, the United States National Institute of Standards and Technology is presently evaluating 69 new methods known as “post-quantum cryptography,” or PQC. Quantum computing offers an eminent, potential solution to cybersecurity and encryption threats. Any security-forward organization ought to develop an understanding of crypto agility.
Quantum revolution is uncertain. While the intense impact of extensive fault-tolerant quantum computers may be far off, near-time quantum computers still present enormous advantages in enhancing levels of communication privacy and security. All organizations must consider developing innovative strategies around the long-term benefits and risks of quantum technology and computing, and be ready for the forthcoming quantum revolution.
Today’s classical computers use two primary classes of algorithms for encryption: symmetric and asymmetric.
• In symmetric encryption, the same key is used to encrypt and decrypt a given piece of data. The Advanced Encryption Standard (AES) is an example of a symmetric algorithm. Adopted by the US government, the AES algorithm supports three key sizes: 128 bits, 192 bits, and 256 bits. Symmetric algorithms typically are used for bulk encryption tasks, such as enciphering major databases, file systems, and object storage.
• In asymmetric encryption, data is encrypted using one key (usually referred to as the public key) and is decrypted using another key (usually referred to as the private key). Although the private key and public key are different, they are mathematically related. The widely employed Rivest, Shamir, Adleman (RSA) algorithm is an example of an asymmetric algorithm. Even though it is slower than symmetric encryption, asymmetric algorithms solve the problem of key distribution, which is an important issue in encryption.
Quantum risks to cybersecurity
The advent of quantum computing will lead to changes to encryption methods. Currently, the most widely used asymmetric algorithms are based on difficult mathematical problems, such as factoring large numbers, which can take thousands of years on today’s most powerful supercomputers.
However, research conducted by Peter Shor at MIT more than 20 years ago demonstrated the same problem could theoretically be solved in days or hours on a large-scale quantum computer. Future quantum computers may be able to break asymmetric encryption solutions that base their security on integer factorization or discrete logarithms.
Although symmetric algorithms are not affected by Shor’s algorithm, the power of quantum computing necessitates a multiplication in key sizes. For example, large quantum computers running Grover’s algorithm, which uses quantum concepts to search databases very quickly, could provide a quadratic improvement in brute-force attacks on symmetric encryption algorithms, such as AES.⁵
To help withstand brute-force attacks, key sizes should be doubled to support the same level of protection. For AES, this means using 256-bit keys to maintain today’s 128-bit security strength.
Even though large-scale quantum computers are not yet commercially available, initiating quantum cybersecurity solutions now has significant advantages. For example, a malicious entity can capture secure communications of interest today. Then, when large-scale quantum computers are available, that vast computing power could be used to break the encryption and learn about those communications.
Eclipsing its potential risks, quantum cybersecurity can provide more robust and compelling opportunities to safeguard critical and personal data than currently possible. It is particularly useful in quantum machine learning and quantum random number generation.
Why create a Quantum computer?
The reasons are not only to improve the processing capacity and solve the problems that cannot be done with traditional computers. In the last 20 years, the complexity and number of transistors in a single CPU have increased exponentially. It seems that we found the limits of the transistor technology in the integrated circuit.
The extreme miniaturization of electronic doors is causing the effects of a phenomenon that become much more significant, such as Electromigration and the Sub-threshold. These obstacles are, among other factors, that make researchers study new computing methods, such as the quantum computer.
Preparing for The Quantum Future
The quantum revolution is upon us. Although the profound impact of large-scale fault-tolerant quantum computers may be a decade off, near-term quantum computers will still yield tremendous benefits.
We are seeing substantial investment in solving the core problems around scaling qubit count, error correction and algorithms. From a cybersecurity perspective, while quantum computing may render some existing encryption protocols obsolete, it has the promise to enable a substantially enhanced level of communication security and privacy.
Organizations must think strategically about the longer-term risks and benefits of quantum computing and technology and engage in a serious way today to be ready for the quantum revolution of tomorrow. If you want more updates on latest technologies, please follow deeptechknowledge.com where we post about the upcoming technologies and their uses.


Research team develops tiny low-energy device to rapidly reroute light in computer chips
Researchers at the National Institute of Standards and Technology (NIST) and their colleagues have developed an optical switch that routes light from one computer chip to another in just 20 billionths of a second—faster than any other similar device. The compact switch is the first to operate at voltages low enough to be integrated onto low-cost silicon chips and redirects light with very low signal loss.
The switch's record-breaking performance is a major new step toward building a computer that uses light instead of electricity to process information. Relying on particles of light—photons—to transport data within a computer offers several advantages over electronic communications. Photons travel faster than electrons and don't waste energy by heating up the computer components. Managing that waste heat is a major barrier to improving computer performance. Light signals have been used for decades to transmit information over great distances using optical fibers, but the fibers take up too much room to be used to carry data across a computer chip.
The new switch combines nanometer-scale gold and silicon optical, electrical and mechanical components, all densely packed, to channel light into and out of a miniature racetrack, alter its speed, and change its direction of travel. (One nanometer is a billionth of a meter, or about one-hundred-thousandth the width of a human hair.) The NIST-led international team describes the device online today in Science.
The device has myriad applications, notes study co-author Christian Haffner of NIST, ETH Zurich and the University of Maryland. In driverless cars, the switch could rapidly redirect a single light beam that must continually scan all parts of the roadway to measure the distance to other automobiles and pedestrians. The device could also make it easier to use more powerful light-based circuits instead of electricity-based ones in neural networks. These are artificial intelligence systems that simulate how neurons in the human brain make decisions about such complex tasks as pattern recognition and risk management.
The new technology also uses very little energy to redirect light signals. This feature may help realize the dream of quantum computing. A quantum computer processes data stored in the subtle interrelations between specially prepared pairs of subatomic particles. However, these relationships are extremely fragile, requiring that a computer operate at ultralow temperatures and low power so that the particle pairs are disturbed as little as possible. Because the new optical switch requires little energy—unlike previous optical switches—it could become an integral part of a quantum computer.
Haffner and his colleagues, who include Vladimir Aksyuk and Henri Lezec of NIST, say their findings may come as a surprise to many in the scientific community because the results contradict long-held beliefs. Some researchers have thought that opto-electro-mechanical switches would not be practical because they would be bulky, operate too slowly and require voltages too high for the components of a computer chip to tolerate.
The switch exploits the wave nature of light. When two identical light waves meet, they can superpose such that the crest of one wave aligns or reinforces the crest of the other, creating a bright pattern known as constructive interference. The two waves may also be exactly out of step, so that the valley of one wave cancels the crest of the other, resulting in a dark pattern—destructive interference.
In the team's setup, a light beam is confined to travel inside a miniature highway—a tube-shaped channel known as a waveguide. This linear highway is designed so that it has an off-ramp—some of the light can exit into a racetrack-shaped cavity, just a few nanometers away, etched into a silicon disk. If the light has just the right wavelength, it can whip around the racetrack many times before leaving the silicon cavity.
The switch has one other crucial component: a thin gold membrane suspended just a few tens of nanometers above the silicon disk. Some of the light traveling in the silicon racetrack leaks out and strikes the membrane, inducing groups of electrons on the membrane's surface to oscillate. These oscillations, known as plasmons, are a kind of hybrid between a light wave and an electron wave: The oscillating electrons resemble the incoming light wave in that they vibrate at the same frequency, but they have a much shorter wavelength. The shorter wavelength lets researchers manipulate the plasmons over nanoscale distances, much shorter than the length of the original light wave, before converting the oscillations back into light. This, in turn, allows the optical switch to remain extremely compact.
By changing the width of the gap between the silicon disk and the gold membrane by only a few nanometers, the researchers could delay or advance the phase of the hybrid light wave—the point in time when the wave reaches a crest or valley. Even minuscule variations in the width of the gap, which the team accomplished by electrostatically bending the gold membrane, dramatically altered the phase.
Depending on how much the team had advanced or delayed the phase of the wave, when it recombined with light still traveling in the tube-shaped highway, the two beams interfered either constructively or destructively (see animation). If the light beams match up to interfere constructively, the light will continue in its original direction, traveling down the tube. But if the light beams interfere destructively, canceling each other out, that pathway is blocked. Instead, the light must move in another direction, determined by the orientation of other waveguides, or routes, placed close to the blocked pathway. In this way, the light can be switched at will to any of hundreds of other computer chips.
Scientists had once thought that a plasmonic system would greatly attenuate light signals because photons would penetrate the interior of the gold membrane, where electrons would absorb much of the light energy.
But the researchers have now proved that assumption wrong. The compactness of the device and a design that ensured that few photons would penetrate the membrane resulted in a loss of just 2.5% of the light signal, compared with 60% with previous switches. That puts the switch, although still a prototype, within reach of commercial applications.
The team is now working to make the device even smaller by shortening the distance between the silicon disk and the gold membrane. This would further reduce signal loss, making the technology even more appealing to industry.


A quantum bit, or qubit, is that the elementary unit of data for a quantum computer almost likes a touch in normal machines. A qubit may be a two-state (or two-level) quantum-mechanical system, one among the only quantum systems displaying the peculiarity of quantum physics. Examples include the spin of the electron during which the 2 levels are often taken as spin up and spin down; or the polarization of one photon during which the two states are often taken to be the vertical polarization and therefore the horizontal polarization.
A quantum bit, or qubit, has two quantum states, analogous to the classical binary states. While the qubit are often in either state, it also can exist during a “superposition” of the 2.These states are often represented in so-called Dirac notation, where the state’s label is written between a | and a ⟩. Thus, a qubit’s two component, or “basis,” states are generally written as | 0⟩ and | 1⟩. Any given qubit wave function could also be written as a linear combination of the 2 states, each with its own complex coefficient ai: | ψ⟩ = a0 | 0⟩+ a1 | 1⟩. Since the probability of reading a state is proportional to the square of its coefficient’s magnitude, | a0 | 2 corresponds to the probability of detecting the state | 0⟩, and | a1 | 2 to the probability of detecting | 1⟩. The sum of the possibilities of every possible output state must be 100 percent, mathematically expressed during this case as | a0 | 2 + | a1 | 2 = 1.
Bit versus qubit
Though a classical bit is entirely specified either as 1 or 0, a qubit is specified by the continuum of the values a0 or a1, which are literally analog—that is, the relative contribution from each possible state are often any value between zero and one, provided the entire probability is one. Of course, this richness exists before the qubit’s state is measured, or “read out.” The results of a measurement looks a bit like a classical bit, a 0 or a 1, with the associated probability of getting each value proportional to the square of absolutely the value of the coefficient of the corresponding state, | a0| 2 or | a1| 2.
A digit, characterized as 0 or 1, is employed to represent information in classical computers. When averaged over both of its states (0,1), a digit can represent up to at least one little bit of Shannon information, where a touch is that the basic unit of data. However, during this article, the word bit is synonymous with a digit.
In classical computer technologies, a processed bit is implemented by one among two levels of low DC voltage, and whilst switching from one among these two levels to the opposite, a so-called “forbidden zone” between two logic levels must be passed as fast as possible, as electrical voltage cannot change from one level to a different instantaneously.
There are two possible outcomes for the measurement of a qubit—usually taken to possess the worth “0” and “1”, sort of a bit or digit. However, whereas the state of a touch can only be either 0 or 1, the overall state of a qubit consistent with quantum physics are often a coherent superposition of together. Moreover, whereas a measurement of a classical bit wouldn’t disturb its state, a measurement of a qubit would destroy its coherence and irrevocably disturb the superposition state. It’s possible to completely encode one bit in one qubit. However, a qubit can hold more information, e.g., up to 2 bits using super dense coding.
For a system of n components, an entire description of its state in classical physics requires only n bits, whereas in physics it requires (2n – 1) complex numbers.
Operations on qubits
There are various sorts of physical operations that will be performed on qubits.
Quantum logic gates, building blocks for a quantum circuit during a quantum computer, operate a group of qubits (a register); mathematically, the qubits undergo a (reversible) unitary transformation described by the quantum gates’ unitary matrix.
Quantum measurement is an irreversible operation during which information is gained about the state of one qubit (and coherence is lost). The results of the measurement of one qubit with the state ψ = α | 0 ⟩ + β | 1 ⟩ are going to be either | 0 (with probability | α | 2 (with probability | β |. Measurement of the state of the qubit alters the magnitudes of α and β. as an example, if the results of the measurement is | 1 ⟩ α is modified to 0 and β is modified to the phase factor e i ϕ not experimentally accessible. When a qubit is measured, the superposition state collapses to a basis state (up to a phase) and therefore the relative phase is rendered inaccessible (i.e., coherence is lost). Note that a measurement of a qubit state that’s entangled with another quantum system transforms the qubit state, a pure state, into a mixed state (an incoherent mixture of pure states) because the relative phase of the qubit state is rendered inaccessible.
This operation collapses the quantum state (exactly like with measurement), which can successively if the qubit is entangled, collapse the state of other qubits. Initialization to | 0 ⟩ could also be implemented logically or physically: Logically as a measurement, followed by the appliance of the Pauli-X gate if the result from the measurement was | 1 ⟩.Physically, for instance if it’s a superconducting phase qubit, by lowering the energy of the quantum system to its state.


Technology allows us to communicate instantly with people in our neighborhoods or around the globe. This innovation not only keeps us connected but can help us live safer and healthier lives.
Other ways technology is seen to have a positive effect on society include increased knowledge and understanding, improvements in industry and jobs and an interconnectedness of the world as a result of globalization.
Technology affects almost every aspect of 21st century life, from transport efficiency and safety, to access to food and healthcare, socialization and productivity. The power of the internet has enabled global communities to form and ideas and resources to be shared more easily.
Technology provides students with easy-to-access information, accelerated learning, and fun opportunities to practice what they learn. It enables students to explore new subjects and deepen their understanding of difficult concepts, particularly in STEM.
Technology is constantly advancing. This gives rise to new jobs and industries, such as coding and artificial intelligence. Technology provides a makers education in AI, IT, design, and many STEM fields. … All of this is beneficial because it’s estimated that AI will replace 40 percent of jobs in the future.
Technology has the ability to enhance daily living from appliances to mobile devices and computers, technology is everywhere. … In the rise of digital communication, technology can actually help communication skills because it allows people to learn written communication to varying audiences.
Technology is one of the essential parts of our life which makes this world easier to live and give us more freedom and a lot of ways to live differently. … So, Technology has a significant role in our life especially in making a prosperous life, unlimited communication, and treatment of incurable diseases.
A key positive impact of technology on education is that it brings students together through discussion and collaboration tools, who might never have considered, or had the opportunity, to communicate or help each other offline.
It can provide empowerment, knowledge, awareness, access, and community. As we develop the technology of the future, we can work towards creating a better world long term. This means many different things as technology merges with all parts of our lives.
In terms of classroom administration, for example, technology can provide enhanced record keeping, greatly improving the teacher’s analysis of student performance, especially the identification of skills which could be improved by deliberate practice. This is where technology can really help.
“The overall survey results show that higher levels of technology use and technoference adds up to significantly less time spent together as a couple, less satisfaction and connection, and higher levels of depression and anxiety,” he said.
In fact, some research indicates that technology can improve both the teaching and learning aspects of education. It also encourages active engagement and interactivity that students are so accustomed to outside of class, and miss when having to pay attention to lesson materials.
Artificial Intelligence (AI) Artificial intelligence is probably the most important and ground-breaking trend in technology today. The fact that we have created machines and systems that can think for themselves is truly astounding, and the trend shows no signs of slowing down.
Second: Technology provides teachers and students with access to a variety of educational resources that inspire creativity, critical thinking, communication, and collaboration. … This in turn promotes a global awareness, which is an essential component to a 21st century education.
Potential benefits of technology for teens
easily access information to inform and educate themselves. maintain and develop supportive relationships. form their identities (through self-expression, learning and talking)
Technology helps relationships last over time and distance.
For friends who can’t always meet in person, technology helps them stay connected. In the pre-digital days, Hampton explains, if you moved out of town for a new job or switched schools, it was a real challenge to stay in touch, no matter how close you were.
Selecting the phone/tv/computer over your loved ones can put a lot of stress on the relationship, alienate affection, and leave your partner feeling unappreciated. Stretch this out over months or years, and it can lead to greater conflict, dissatisfaction, and possibly the end of the relationship.
Technology plays a significant role in the way that young people communicate and develop friendships. The findings reveal that many children and young people are using a variety of online platforms on a daily basis to communicate with their friends, as well as to create new friendships and maintain existing ones.
Experts have found that in addition to making our lives more convenient, but there’s a negative side to technology — it can be addicting and it can hurt our communication skills. Extended screen time can result in health ramifications like insomnia, eyestrain, and increased anxiety and depression.
The Internet is the most important new technology which will solve all the major problems existing in the world including all major social issues such as high population rate, poverty, hunger, hygiene problems and much more by spreading awareness about all these major social issues.
Whether it is clean energy, robotics, quantum computing, synthetic biology, telemedicine, AI, or cloud education and NUI software, it can solve all the biggest problems confronting mankind. Creating value means coming up with something people will pay for in the real world.
Use digital resources well: Schools can use digital resources in a variety of ways to support teaching and learning. Electronic grade books, digital portfolios, learning games, and real-time feedback on teacher and student performance, are a few ways that technology can be utilized to power learning.
Because they are at the center of the network of their families, Internet helps them to organize their lives. Also, it helps them to overcome their isolation, particularly in patriarchal societies. The Internet also contributes to the rise of the culture of autonomy.
Technology Encourages Individual Learning – Technology personalizes the learning experience and provides greater opportunities for students with varying needs. They can learn at their own speed, go back to lessons and get online instructions to support the learning process.


Last week, D-Wave announced a new version of its quantum annealing computer. The new machine includes a number of technical improvements, as well as a significant change to the physical arrangement of the board. What does all this mean? Combined with D-Wave's online resources, a tool that verges on useful is starting to take form.
Making a smooth computer
Before we reach the gooey chocolate center, we have to deal with the crusty outer coating: what is a quantum annealer? Most computers work in a straightforward manner: to add two numbers together, you construct a set of logical gates that will perform addition. Each of these gates performs a set of specific and clearly defined operations on its input.
But that is not the only way to perform computation. Most problems can be rewritten so that they represent an energy minimization problem. In this picture, the problem is an energy landscape, and the solution is the lowest-possible energy of that landscape. The trick is finding the combination of bit values that represents that energy.
To do this, we start with an energy landscape that is flat: we can start all the bits in the lowest energy of this flat landscape. Then we carefully and slowly modify the landscape around the bits until it represents our problem. If we have done that correctly, the bits are still in their lowest energy state. We obtain a solution by reading off the bit values.
Although this works without anything quantum being involved, D-Wave does this with quantum bits (qubits). That means the qubits are correlated with each other—this is called quantum entanglement. As a result, they change value together, rather than independently.
This allows something called quantum tunneling. Imagine a qubit stuck in a high energy state. Nearby, there is a lower energy state that the qubit would prefer to be in. But to get to that low energy state, it first has to go to an even higher energy state. In a classical system, this creates a barrier to reaching the lower energy state. But in a quantum system, the qubit can tunnel through the energy barrier to enter the lower energy state.
These two properties may allow a computer like the one that D-Wave operates to obtain solutions for some problems more quickly than its classical counterpart.
The devil, however, is in the details. Within the computer, an energy landscape is produced by the coupling (physical connection) among qubits. The coupling controls how strongly the value of one qubit influences the value of the rest of them.
This has always been the major sticking point of the D-Wave machine. Under ideal circumstances, every qubit would have couplers that link it directly to every other qubit. That many connections, however, is impractical.
A qubit all alone
The consequences of the lack of connectivity are severe. Some problems simply cannot be represented by D-Wave machines. Even in cases where they can, the computation can be inefficient. Imagine that a problem required qubits one and three to be connected, but they are not directly connected. In that case, you have to search for qubits that are common to both. Say qubit one is linked to qubit five, while qubit two is linked to qubits five and three. Logical qubit one is then one and five combined. Logical qubit three is qubits two and three linked together. D-Wave refers to this as a chain length of, in this case, two.
Chaining costs physical qubits, which are combined to create logical qubits, making fewer available for the computation.
D-Wave's development path has been one of engineering ever more complicated arrangements of qubits to increase the connectivity. By increasing the connectivity, the chain lengths become shorter, leaving a larger number of logical qubits. When qubits are tied together to create more connectivity, a larger number of problems can be encoded.
The efficiency of structuring some problems is going to be very, very low, meaning that the D-Wave architecture is simply not suited to those problems. But as the connectivity increases, the number of unsuitable problems goes down.
In the previous iteration of this machine, the qubits were structured in blocks of eight, such that connectivity between diagonal blocks was improved compared to two versions ago (see the animated gif). This introduced a small improvement in chain lengths.
Now D-Wave has moved on to a Pegasus graph. I don't know how to describe it, so I'm going to describe it incorrectly in the strict graph theory sense but in a way I think will make more sense overall. Instead of a single basic unit of eight qubits, there are now two basic units: a block of eight and a pair.
In the eight qubit blocks, the qubits are arranged as before, with an inner loop and an outer loop. But, as you can see below, the inner and outer loops have an extra connection. That means that each qubit has five connections within that small block.
The blocks are no longer arranged in a regular grid, either, and the interconnections between the qubits from separate blocks are much denser. Whereas the previous generation connected outer loop qubits to outer loop qubits, now each qubit is connected to both inner and outer loops of neighboring blocks.
Then, on top of that, there is a new network of long-range connections between different blocks. Each qubit has a long-range connection to another qubit in a distant block. The density of the long-range connectivity is increased by the second basic building block: connected pairs. The pairs are placed around the outside of the main block pattern to complete the long-range connectivity.
The idea, I think, is to ensure that the eight qubit groupings near the sides of the chip still have nearly the same connectivity as inner groups, unlike in the chimera graphs.
Make the chains shorter
What does all this mean? First of all, the similarity between the chimera and pegasus graphs means that code developed for chimera should still work on pegasus. The increased connectivity means the chain lengths are significantly reduced, making calculations more reliable.
To give you an idea of how much the new graph improves the situation, a square lattice with diagonal interconnects requires a chain length of six in the chimera graph and chain length of two in the pegasus implementation. In general, chain lengths are reduced by a factor of two or more. The run times are reduced by 30 to 75 percent on the new machine.
Aside from the new graph, D-Wave has improved at a technical level: the qubits have lower noise, and there is a much larger number of qubits. The plan is that the new architecture will eventually get D-Wave to 5,000 qubits (up from 2,000). Using the chimera architecture, this would be a nice (but not stellar) upgrade. Adding the changes in architecture means many more of those physical qubits can be used as independent logical qubits, making this a much more significant upgrade.