Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. Computers that perform quantum computations are known as quantum computers. Quantum computers are believed to be able to solve certain computational problems, such as integer factorization (which underlies RSA encryption), substantially faster than classical computers. The study of quantum computing is a subfield of quantum information science.
Quantum computing began in the early 1980s, when physicist Paul Benioff proposed a quantum mechanical model of the Turing machine. Richard Feynman and Yuri Manin later suggested that a quantum computer had the potential to simulate things that a classical computer could not. In 1994, Peter Shor developed a quantum algorithm for factoring integers that had the potential to decrypt RSA-encrypted communications. Despite ongoing experimental progress since the late 1990s, most researchers believe that “fault-tolerant quantum computing [is] still a rather distant dream.” In recent years, investment into quantum computing research has increased in both the public and private sector. On 23 October 2019, Google AI, in partnership with the U.S. National Aeronautics and Space Administration (NASA), published a paper in which they claimed to have achieved quantum supremacy. While some have disputed this claim, it is still a significant milestone in the history of quantum computing.
There are several models of quantum computing, including the quantum circuit model, quantum Turing machine, adiabatic quantum computer, one-way quantum computer, and various quantum cellular automata. The most widely used model is the quantum circuit. Quantum circuits are based on the quantum bit, or “qubit”, which is somewhat analogous to the bit in classical computation. Qubits can be in a 1 or 0 quantum state, or they can be in a superposition of the 1 and 0 states. However, when qubits are measured the result is always either a 0 or a 1; the probabilities of these two outcomes depend on the quantum state that the qubits were in immediately prior to the measurement. Computation is performed by manipulating qubits with quantum logic gates, which are somewhat analogous to classical logic gates.
There are currently two main approaches to physically implementing a quantum computer: analog and digital. Analog approaches are further divided into quantum simulation, quantum annealing, and adiabatic quantum computation. Digital quantum computers use quantum logic gates to do computation. Both approaches use quantum bits or qubits.:2–13 There are currently a number of significant obstacles in the way of constructing useful quantum computers. In particular, it is difficult to maintain the quantum states of qubits as they are prone to quantum decoherence, and quantum computers require significant error correction as they are far more prone to errors than classical computers.
Any computational problem that can be solved by a classical computer can also, in principle, be solved by a quantum computer. Conversely, quantum computers obey the Church–Turing thesis; that is, any computational problem that can be solved by a quantum computer can also be solved by a classical computer. While this means that quantum computers provide no additional power over classical computers in terms of computability, they do in theory provide additional power when it comes to the time complexity of solving certain problems. Notably, quantum computers are believed to be able to quickly solve certain problems that no classical computer could solve in any feasible amount of time—a feat known as “quantum supremacy.” The study of the computational complexity of problems with respect to quantum computers is known as quantum complexity theory.
Besides factorization and discrete logarithms, quantum algorithms offering a more than polynomial speedup over the best known classical algorithm have been found for several problems, including the simulation of quantum physical processes from chemistry and solid state physics, the approximation of Jones polynomials, and solving Pell’s equation. No mathematical proof has been found that shows that an equally fast classical algorithm cannot be discovered, although this is considered unlikely. However, quantum computers offer polynomial speedup for some problems. The most well-known example of this is quantum database search, which can be solved by Grover’s algorithm using quadratically fewer queries to the database than that are required by classical algorithms. In this case, the advantage is not only provable but also optimal, it has been shown that Grover’s algorithm gives the maximal possible probability of finding the desired element for any number of oracle lookups. Several other examples of provable quantum speedups for query problems have subsequently been discovered, such as for finding collisions in two-to-one functions and evaluating NAND trees.
Problems that can be addressed with Grover’s algorithm have the following properties:
- There is no searchable structure in the collection of possible answers,
- The number of possible answers to check is the same as the number of inputs to the algorithm, and
- There exists a boolean function which evaluates each input and determines whether it is the correct answer
For problems with all these properties, the running time of Grover’s algorithm on a quantum computer will scale as the square root of the number of inputs (or elements in the database), as opposed to the linear scaling of classical algorithms. A general class of problems to which Grover’s algorithm can be applied is Boolean satisfiability problem. In this instance, the database through which the algorithm is iterating is that of all possible answers. An example (and possible) application of this is a password cracker that attempts to guess the password or secret key for an encrypted file or system. Symmetric ciphers such as Triple DES and AES are particularly vulnerable to this kind of attack. This application of quantum computing is a major interest of government agencies.
Since chemistry and nanotechnology rely on understanding quantum systems, and such systems are impossible to simulate in an efficient manner classically, many believe quantum simulation will be one of the most important applications of quantum computing. Quantum simulation could also be used to simulate the behavior of atoms and particles at unusual conditions such as the reactions inside a collider.
Quantum annealing or Adiabatic quantum computation relies on the adiabatic theorem to undertake calculations. A system is placed in the ground state for a simple Hamiltonian, which is slowly evolved to a more complicated Hamiltonian whose ground state represents the solution to the problem in question. The adiabatic theorem states that if the evolution is slow enough the system will stay in its ground state at all times through the process.
The Quantum algorithm for linear systems of equations, or “HHL Algorithm”, named after its discoverers Harrow, Hassidim, and Lloyd, is expected to provide speedup over classical counterparts.
The above is a brief about Quantum Computing. Watch this space for more updates on the latest trends in Technology.


Physicists see 27th dimension of photons
By Jesse Emspak
Published January 29, 2014
Scientists find a way to directly measure quantum states, such as momentum, of photons. (MPQ, Quantum Dynamics Division.)
Quantum computers and communications promise more powerful machines and unbreakable codes. But to make them work, it’s necessary to measure the quantum state of particles such as photons or atoms. Quantum states are numbers that describe particle characteristics such as momentum or energy.
But measuring quantum states is difficult and time-consuming, because the very act of doing so changes them, and because the mathematics can be complex. Now, an international team says they found a more efficient way to do it, which could make it simpler to build quantum-mechanical technologies.
In a study detailed in the Jan. 20 issue of the journal Nature Communications, researchers from the University of Rochester and the University of Glasgow took a direct measurement of a photon’s 27-dimensional quantum state. These dimensions are mathematical, not dimensions in space, and each one is a number that stores information.
To understand a 27-dimensional quantum state, think about a line described in 2 dimensions. A line would have a direction in the X and Y coordinates 3 inches left and 4 inches up, for instance. The quantum state has 27 such coordinates. [Quantum Physics: The Coolest Little Particles in Nature]
“We chose 27, kind of to make a point about 26 letters in the alphabet and throwing in one more,” said Mehul Malik, now a postdoctoral researcher at the University of Vienna. That means each quantum bit, or “qubit,” could store a letter instead of a simple 1 or 0.
Seeing a photon The group, led by Malik and Robert Boyd, a professor of optics and physics at the University of Rochester, was able to see a photon’s states directly. They measured the photon’s orbital angular momentum, which is how much the particles of light “twist” as they travel through space.
Ordinarily, finding the quantum state of a photon requires a two-step process. First, scientists have to measure some property of the photon, such as its polarization or momentum. The measurements are performed on many copies of the quantum state of a photon. But that process sometimes introduces errors. To get rid of the errors, the scientists have to look at what results they got that are “disallowed” states ones that don’t follow the laws of physics. But the only way to find them is to search through all the results and discard the ones that are impossible. That eats up a lot of computing time and effort. This process is called quantum tomography. [The 9 Biggest Unsolved Mysteries in Physics]
A light wave is a combination of an electric and magnetic field, each of which oscillates and makes a wave. Each wave moves in time with the other, and they are perpendicular to each other. A beam of light is made up of lots of these waves.
Light can have what is called orbital angular momentum. In a beam with no orbital angular momentum, the peaks of the waves the electric ones, for example are lined up. A plane connecting these peaks will be flat. If the beam has orbital angular momentum, a plane connecting these peaks will make a spiral, helical pattern, because the light waves are offset from one another slightly as you go around the beam. To measure the state of the photons, scientists must “unravel” this helical shape of the waves in the beam.
Measuring a photon’s quantum state The team first fired a laser through a piece of transparent polymer that refracted the light, “unraveling” the helix formed by the waves. The light then passed through special lenses and into a grating that makes many copies of the beam. After passing through the grating, the light is spread out to form a wider beam.
After the beam is widened, it hits a device called a spatial light modulator. The modulator carries out the first measurement. The beam then reflects back in the same direction it came from and passes through a beam splitter. At that point, part of thebeam moves toward a slit, which makes a second measurement. [Twisted Physics: 7 Mind-Blowing Experiments]
One of the two measurements is called “weak” and the other “strong.” By measuring two properties, the quantum state of the photons can be reconstructed without the lengthy error-correction calculations tomography requires.
In quantum computers, the quantum state of the particle is what stores the qubit. For instance, a qubit can be stored in the photon’s polarization or its orbital-angular momentum, or both. Atoms can also store qubits, in their momenta or spins.
Current quantum computers have only a few bits in them. Malik noted that the record is 14 qubits, using ions. Most of the time, ions or photons will only have acouple of bits they can store, as the states will be two-dimensional. Physicists use two-dimensional systems because that is what they can manipulate it would be very difficult to manipulate more than two dimensions, he said.
Direct measurement, as opposed to tomography, should make it easier to measure the states of particles (photons, in this case). That would mean it is simpler to add more dimensions three, four or even as in this experiment, 27 and store more information.
Mark Hillery, a professor of physics at Hunter College in New York, was skeptical that direct measurement would prove necessarily better than current techniques. “There is a controversy about weak measurements in particular, whether they really are useful or not,” Hillery wrote in an email to LiveScience. “To me, the main issue here is whether the technique they are using is better (more efficient) than quantum-state tomography for reconstructing the quantum state, and in the conclusion, they say they don’t really know.”
Jeff Savail, a master’s candidate researcher at Canada’s Simon Fraser University, worked on a similar direct measurement problem in Boyd’s lab, and his work was cited in Malik’s study. In an email he said one of the more exciting implications is the “measurement problem.” That is, in quantum mechanical systems the question of why some measurements spoil quantum states while others don’t is a deeper philosophical question than it is about the quantum technologies themselves.
“The direct measurement technique gives us a way to see right into the heart of the quantum state we’re dealing with,” he said. That doesn’t mean it’s not useful far from it. “There may also be applications in imaging, as knowing the wave function of the image, rather than the square, can be quite useful.”
Malik agreed that more experiments are needed, but he still thinks the advantages might be in the relative speed direct measurement offers. “Tomography reduces errors, but the post-processing [calculations] can take hours,” he said.


The smaller computer parts get, the more efficient they become – that was the rule of thumb when it comes to computers and electronics in general.
But these parts have reached a state where they can no longer get any smaller without losing the properties used to build machines like modern computers, becoming a barrier to technological advancement.
Computers, and technological advances in general, are reaching this physical limit as processors, transistors, and other computer parts approach the size of an atom.
Modern electronics have silicon-based transistors as small as 10nm, which is almost a hundred times smaller than the size of red blood cells in the human body.
Even smaller than that, transistors quickly and easily begin to play with the laws of classical mechanics, since at that subatomic level classical properties on which modern computers are built are not maintained. This is where quantum mechanics comes into play.
For the uninitiated, quantum mechanics is the study of subatomic particles such as electrons, neutrons, and protons. In contrast to the physical objects that surround us, particles on the subatomic scale behave differently.
While bits, or binary digits, are the building blocks of classical computing, quantum computing uses much more efficient subatomic qubits for computation. Bits in classical computing can be either 0 or 1, basically an “on” or “off” switch for the transistor to either pass or block electrons. Qubits, on the other hand, can be any combination of 0 and 1.
Imagine a glass of lemonade where the lemon juice is 1 and the water is 0. The glass of lemonade is a solution of lemon juice and water, and until the solution is distilled in a lab, there’s no way of telling what ratio you’re in.
Qubits are like that. 1 and 0 both exist in some ratio in a qubit, and as with lab testing, they collapse to a stable state of either 1 or 0 only when the qubit is observed or measured, giving us an unequivocal result. This uncertainty of state is called quantum superposition.
Aside from this uncertain state, qubits are also mathematically entangled with the qubits in their vicinity. This means that when measuring when a qubit collapses to a 1 or 0 state, the state of the neighboring qubit will be affected by the result. This property is known as quantum entanglement. Because of this entanglement, measuring one qubit can tell us what state the neighboring qubits are in.
Quantum computers are built based on these two basic principles of quantum mechanics: superposition and entanglement.
The Nobel Prize-winning American physicist Richard Feynman first realized while working on one of his projects that classical computers are not scalable to handle complicated, especially quantum, simulations. He added that the two principles of quantum mechanics could be used to build a much better and more efficient computing system.
In 1986, Feynman introduced the early version of the quantum circuit notation, based on which Peter Shor developed a quantum algorithm in 1994. Later, Isaac Chuang, Neil Gershenfeld, and Mark Kubinec developed the world’s first known working quantum computing tool using just two qubits in 1991. Although it was a very early rendition of a primitive computing device, it was quite a leap in the advancement of this nascent technology .
Quantum computers are computing devices that control the behavior of particles at the subatomic level. Because the components and building blocks of quantum computers are orders of magnitude smaller than those of classical computers, they are exponentially faster and use only a fraction of the power that conventional computers require.
However, contrary to how they are portrayed in the sci-fi genre, quantum computers are not an upgrade of the classic computers we have in our homes. That’s because they work very differently than the computers we have now. They are also exponentially better at complex calculations than the supercomputers that most technology companies like Google, IBM and Microsoft use for their R&D.
Comparing classical computers and supercomputers with quantum computers would be like comparing bicycles with motorcycles. Classic computer upgrades often refer to multiplying capacity or efficiency. A decade ago, 1GB of RAM was enough for a PC. But now the 2GB RAM is the bare minimum in modern computers, that’s two 1GB RAM bundled together.
Unlike the RAM in classic computers, no matter how many bikes are bundled together, they cannot become a motorcycle, as motorcycles are much more efficient and function differently than bicycles. The same applies to quantum computers, since they are fundamentally different from conventional computers.
That’s why physicists and researchers behind this technology insist that quantum computers are not an upgrade from supercomputers, but an entirely different superclass of computers that will change the course of computing algorithms for the future.
These computing devices are so advanced that they take a fraction of the time and energy to solve a problem that even modern supercomputers take hours to solve. A simple example would be how efficient they are at a database search.
For example, if there is a database with 1 trillion names and a search is performed, classical computers and supercomputers compare every single name in the database to the search, which means a trillion operations for just a simple search.
On the other hand, using the properties of qubits, a quantum computer can perform the same operation in significantly fewer steps. For the same search operation with 1 trillion names, quantum computers would only need to perform 1 million operations, which is a million times fewer operations than classical or supercomputers would require for the results.
What supercomputers can do, quantum computers can do with a fraction of the resources. However, progress in this technology has been slow. Although companies like IBM, Google and Microsoft have invested heavily in the development of quantum computing tools in recent years, we are nowhere near a full-fledged prototype for commercial or personal use.
News of prototypes from several Chinese and American researchers break out every few years. Still, we came closest to a quantum computer when Google AI partnered with NASA in October 2019. They claimed to have performed calculations at a quantum level that is seemingly infeasible on any classical or supercomputer. But even this claim is questioned by many.
Of course, the commercial and private use of quantum computing is a dream for the distant future, especially since exploiting the quantum properties of particles at the subatomic level, unlike the classical computing components we use, can only be possible in a controlled environment. However, in a decade or two, primitive quantum computing tools could feed into various research and simulations that will give us a closer look at atoms and molecular structures.
This level of intricate insight and powerful calculations will help the medical and nutritional industries better understand the elements. Any industry or branch that relies on research and simulation would benefit greatly from this hyper-efficient computing technology. These include space exploration, manufacturing, engineering, molecular analysis, cryptography, chemical engineering, etc.
Cybersecurity or encryption is another sector where quantum computing will break the norm and revolutionize it. Thanks to the quantum uncertainty of qubits, deciphering the encryption from a quantum computer would be nearly impossible.


Taking a Practical Step Forward in Optical Computing Using Slow Light: Photonic Crystals Offer a Slow Light Solution for Optical Computing
Previously published on Apr 13, 2011
Quantum computing is the Mount Everest of the information technology revolution. What approach succeeds will almost assuredly utilize optical components. With the limits of traditional electronics threatening to halt progress, alternatives, such as optical computing, will be needed in the not so distant future. One major hurdle for the development of such optical systems has been the need to convert between optical and electronic signals. Because time spent converting optical data into an electronic format takes longer than simply using the traditional medium, the concept is impractical in many respects. On the other hand, an almost paradoxical concept known as slow light offers a way around this barrier with a very practical solution.
It is a fundamental law of the universe that light can only exist at the speed of light. That is, photons must always move at approximately 300 million meters per second.
Looking closely at this law reveals a rather obvious loophole. Light waves passing through almost any given medium usually take longer to propagate through said medium than they would free space, because the light is bent along a lengthier path due to the internal properties of the medium. In other words, photons will continue to move at light speed, but it takes them longer to navigate through an object rather than simply moving within a vacuum at light speed, i.e. light goes slower. Consequently, given the proper medium, light could be slowed to a crawl, or even stopped.
It is how much a medium bends light that determines the "speed" of light and this property classically depends upon a material's index of refraction. A material with a high enough index of refraction, therefore, could be used to slow light. While the first demonstration of slow light in 1999, which yielded a speed around 17 meters per second, utilized a Bose-Einstein Condensate, which is a low-temperature state of matter where the atoms lose their individual characteristics and act almost as a single particle, one alternative approach is to utilize the many emerging manmade meta-materials that exhibit extreme properties, including super high indexes of refraction. On the other hand, researchers at the University of Sydney in New South Wales looked at advances in photonic crystals to suggest an even easier, more dynamic alternative.
Photonic crystals are a rapidly advancing technology first developed in the 1990's. By engineering regular structures in an optical material, light will respond to the pattern as though it is passing through a crystal. Giving researchers far greater control over light, photonic crystals can be used to slow light to variable speeds at continually shrinking costs with greater precision and less bulk. In fact, Professor Benjamin Eggleton's research group has already demonstrated an approach using a photonic crystal structure engineered by a University of St. Andrews team led by Professor Thomas F. Krauss for use over a broad bandwidth yields a sixteen fold increase in processing speeds over a traditional silicon chip, or 640 gigabits a second.
As such, it is obvious the next step forward is hybrid systems using photonic crystal chips. The key to processing and transmitting data stems from the ability to control how information flows. Light can get information to where it needs to go rather quickly, but the information must be stored until it can be used. Optical buffering as the "old fashion" approach relies on costly conversions between optical and electronic signals, so slowing light is a better option. If light is slowed or stopped until it is needed, a hybrid optical-electronic system would be extremely practical with results instantly surpassing the capacity of electronic devices. Consequently, we may soon see a major advancement in the telecommunications industry, followed by a renewed revolution in all computing technologies.
Thanks to initiatives for promoting civil investments in solar energy, LED lighting, national security and so on, technologies based on research from the fields of optics have known great progress in recent years. Just as the fruits of this research finally start to ripen, however, public support is drying up due to budget battles in Europe and the United States. Meanwhile, private funding can often be very selective to our civilization's detriment as entrepreneurs only want to invest in products that guarantee them a return, especially in the current environment where high return, low cost business deals can be exploited by the investment community. The US was already significantly behind in providing funds for research while even less funding is certain to retard progress just as we are the verge of major advances on a number of fronts.
With relatively low-cost experimental needs, the optical sciences offer solutions for everything from national and energy security to pharmaceutical and agricultural applications. Breakthroughs like slow light, meta-materials, photonic crystals, and quantum dots, which are essentially "traps" for photons and other particles, came about due to somewhat basic theories of some very complex subjects and scientists simply questioning. Not only do these discoveries and more have a myriad of potential applications, the costs associated with these technologies fall as we see progress while the benefits and profits begin to amass. Pursuing related research has already revealed some very meaningful discoveries and opportunities, but our society must be more aggressive in our pursuit of the basic research required to realize current and future gains.


|The diamond in the center measures 1mm by 1mm.|
This problem has now been resolved.
A paper just published in Nature claims that they have found a way to protect multiple qubits from decoherence over an extended period of time, and built a quantum computer into a diamond to prove it. Since I realize most of my readers don't keep up with this field as much as I do, I'll try my best to explain.
How it works.
Classical computers use bits (binary digits) to store information in memory. These are the binary digits (1s and 0s) that get stored in between calculation steps in any non-trivial program. If a 1 were to turn into 0 randomly in the middle of an operation, a program might still be able to recover if it has good error detection, but if huge numbers of bits were to switch from one to the other while a program was running, there's just no way it could continue to function as desired.
Quantum computers use qubits instead of bits. This is what makes quantum computing as a concept so very powerful. Rather than use 1s and 0s that are either one or the other, the qubits of quantum computing utilize a superposition of states where a single qubit might be partly a 1 and partly a 0. Yet this reliance upon qubits has been a fundamental problem in quantum computing so far. It is just too easy for qubits to decohere and lose whatever information you try to store in it.
It wasn't until 2008 that a group first figured out how to keep a qubit from losing its information for a grand total of 1.75 seconds. Not only is this amount of time too short to do anything with, but the process could not handle multiple qubits, making this type of quantum computer incapable of using more than one qubit at a time.
Today's news marks the first time anyone has figured out how to shield multiple qubits from decohering for an extended period of time. I won't get into details of how they accomplish this; you can read the paper yourself for that. (Suffice it to say that they used microwave pulses to delay decoherence continuously.) But the point is that they were able to construct a working quantum computer and run a simple program on it to verify the qubits are not decohering.
The fun part of the story.
|Quantum circuit representation of Grover's algorithm.|
The usual explanation of Grover's algorithm starts by imagining a phone book organized in alphabetical order. (We'll need a new mental example soon; I can't remember the last time I actually saw a phone book in person.) If we have a specific phone number we're looking for, our only real recourse is to search through the book one entry at a time. We might get lucky and find it as soon as we open phone book, but we also might be unlucky and not find it until the very last entry. (Technically the second to last, since we assume the number exists in the book, but please ignore this sentence for clarity.) In general, it turns out that, on average, we will find the number we seek on our N/2nd try, where N is the total number of entries in the phone book. For a book with four entries, we'll find it on our second try on average; if the book has a hundred entries, we'll find it on the fiftieth try on average. A phone book listing everyone in New York City would have ~8,000,000 entries; we'd find a particular entry on the four millionth try on average.
Grover's algorithm, on the other hand, will find it, on average, on our O(N1/2) try. For a book of four entries, we'll basically find it on the first try every time. With a hundred entries, we'll find it on the tenth try on average. With 8,000,000 entries, on average, we'll find it on the 2,829 try. That's not a typo; it really is less than three thousand tries for an eight million entry database. This speed increase is enormous. The applications for such sheer speed has drastic repercussions in the real world.
The team used Grover's algorithm on their quantum computer and found the correct entry on their first try 95% of the time. This puts the question of whether their computer works or not entirely to rest. Their quantum computer not only works, but works well enough to actually be capable of quantum calculation at a 5% error rate. Sure, that's not perfect, but it's already enough accuracy that, if one desired, error-correcting could be done. Just redo the calculation ten times; with a 95% accuracy rate, that is more than enough to determine the correct output. The insane speed increase quantum computers have over classical computers is enough to make it more than worthwhile to repeat the same calculation ten times in a row each time.
Of course, the quantum computer they built held only two qubits, and so can only store so much information at a time. Doing Grover's algorithm on an 8,000,000 entry database, for example, would require seven qubits. (In general, O(log(N)) qubits would be required for a database of N entries.) But there is nothing stopping someone from creating such a quantum computer in principle, so long as they have enough microwave pulses to keep them all from decohering. The future, it appears, is now.
So what does this all mean?
First of all, it means that somebody has a working two qubit quantum computer right now. Realistically, this is not enough to cause much of a ruckus. To put it in context, this is only enough storage to solve Grover's algorithm for databases of seven entries or less. (Although Grover's original paper points out that his algorithm requires only that the processing be done with qubits; the memory can be saved in a classical bits.) However, quantum computers do not scale linearly like classical computers do. As mentioned previously, a mere seven qubits would be enough memory to store steps for Grover's algorithm on a database of 8,000,000 entries. It takes very few qubits to handle very large problems.
Now that the conceptual hurdle has been passed, someone could, right this very moment, build a quantum computer with a dozen qubits. There is nothing preventing us from accomplishing such a feat now, save sheer expense. But if you can afford the dozen microwave lasers and have an appropriately flawed diamond to work with, it is certainly doable. What could one do with such a machine?
To put it bluntly, nearly everything.
|All bounded error, quantum, polynomial time (BQP)|
problems are efficiently solved by quantum computers.
This effectively breaks most commercial-grade encryption now in use on the internet. Some encryption does survive; notably lattice-based and McEliece cryptosystems.
Although the last section makes it seem like quantum computers have now arrived, there are still problems that need to be addresses. David DiVincenzo points out that a practical quantum computer must have:
- scalable physically to increase the number of qubits;
- qubits can be initialized to arbitrary values;
- quantum gates faster than decoherence time;
- universal gate set;
- easily read qubits.
This new discovery solves the first (albeit expensively) and third issues completely. The second issue is still problematic, but is something that can be programmed around. The fifth issue is a matter of convenience; expense and repeatability makes this solvable with money alone. This leaves only the fourth issue: a universal gate set. As this is not yet solved, we will not yet be able to program whatever we want on a quantum computer. But we can still run Grover's algorithm, and a few other programs to which we know the necessary gates, and I've already shown how this affects society directly.
A note on how the press is covering this.
As a skeptic, I was very disappointed in a quote by Daniel Lidar on their method of delaying decoherence. He told press "it’s a little like time travel", because the microwave pulses that made the electron qubit switch their direction of rotation did so in a way that makes the decoherence disappear by moving back in the direction it came from. But, quite frankly, this is bullshit. That has no more to do with time travel than moving left does when you want to take back a move to the right. However, now that the quote is out there in a story that headlines with the word "quantum", you can be sure lots of quacks will completely misunderstand, as they so often do.
A few credits.
Although credit for stuff like this gets cited in journals, blogs rarely take the time to actually link out to the individual scientists' blogs/social media in articles like this. So I thought I'd buck the trend by giving a shout out to the authors of the study, including Daniel Lidar, Zhihui Wang, Toeno van der Sar, Machiel Blok, Hannes Bernien, Tim Taminiau, David Toyli, David Awschalom, & Ronald Hanson. Well done.
Also, thanks to Wikipedia for its help in understanding basic principles and the University of South California for their press release summarizing the findings of the paper. And a tip of the hat to the Skeptic's Guide to the Universe for letting me know about this recent development in the first place. Your podcast is awesome. (c:


We always have interesting MSc/BSc/internship projects for enthusiastic students, show teachers cutting-edge experiments to teachers, etcetera, see opportunities!
Light consists of single photons, and the most classical type of light, coherent laser light, actually consists of a large mix of so-called photon number states - packets containing a certain number of photons. This fact makes it very hard to transform laser light into single photons, which are an important resource for photonic quantum information from quantum key distribution to quantum computing. High-quality sources of single photons were only recently demonstrated by several groups including us. In this paper in PRL, we demonstrate that we can turn it around: based on such a high-quality source of single photons, and quantum interference in an optical setup, we are able to engineer artificial states of light that are very similar to coherent laser light, literally, we are making “light from scratch”! There are subtle differences between textbook coherent states and our artificial coherent states, for instance, our artificial light contains some quantum entanglement between photons at different times, in fact it contains so-called photonic cluster states that are also very useful for quantum information, see QLUSTER. In the figure we compare the Wigner functions where quantum entanglement shows up as negative values (the white curves indicate a value of zero). Apart from that, we see that the overlap with perfect coherent states is pretty good!
We are happy to announce the start of the ERC-funded QLUSTER project, where we aim to use semiconductor quantum dots in micro-cavities to produce many quantum-entangled photons in form of cluster and graph states!
Read more on https://qluster.eu
All light that we see consists of photons, however, single photons itself show fascinating different properties that enable, for instance, 100% secure communications in quantum cryptography or superresolution in microscopy. But making single photons is not an easy task. In one approach, the “photon blockade” effect, a conventional laser beam is sent to an optical cavity with a single atom (or an artificial atom aka quantum dot). Quantum effects in this device make it possible that only one photon at a time exits the device, like a turnstile for single photons. This device, however, requires very special properties and is extremely hard to fabricate. Now, we have confirmed for the first time experimentally that this can be done much easier: by using the “unconventional photon blockade” effect. This was theoretically conceived by our co-authors Vincenzo Savona and Hugo Flayac from the EPFL Lausanne. In short, we exploit the polarization property of the photons, and by cleverly using quantum interference of different polarizations, we obtain the same as in the photon blockade: a nice stream of single photons. The “unconventional photon blockade” might be very useful for future single photon sources and gives insight in the exciting photon number dependent physics of such devices. Editors suggestion: A Double Take on Unconventional Photon Blockade", Article: Phys. Rev. Lett. 121, 043601 (2018), pre-print Arxiv:1803.10992.
Cyril Vaneph and colleagues at the University of Paris-Sud have at the same time demonstrated the effect in the microwave regime.
An ordered stream of single photons is fundamentally different from conventional light which contains bunches of a random number of photons. Sources of such single-photon light are essential for emerging quantum technologies such as quantum cryptography or computing, but widespread use of recently developed bright semiconductor quantum-dot based single-photon sources was hindered by the need for complex optical setups. Here we show a fiber-integrated source of high-quality single photons; integration with conventional optical fiber technology will enable broad use in quantum photonics but it also might enable a number of new fundamental studies in various fields from microscopy to quantum metrology by reducing the experimental complexity significantly.
With a single semiconductor quantum dot in a polarization degenerate microcavity, operating in the weak coupling regime, we can transform incident coherent light into a stream of strongly correlated photons with a second-order correlation function g2(0) up to 40! This work is published in Nat. Commun. 7, 12578 (2016).
See cqed for more details.
See 4photonoam for more details.
Polarization vortices are singular points in generic light fields, different but closely connected to phase vortices. Their singular nature makes them ideal as a positional marker of light beams, we investigated this by studying how they behave while being reflected at an interface. Apart from the scientific article here that is on the cover of issue 8, you can also find a labtalk article with a bit more background, and see here for more information.
The spatial structure of light can be either pure or in an incoherent superposition, a statistical mixture. A laserpointer reflected from a rough surface shows “speckle”, and if you move the surface very quickly these speckle appear washed out; this is light with reduced spatial coherence. We studied how beam shifts depend on this by theory and experiment, click here for more information.
“Spin-Orbit Interaction for Light and Matter Waves” is a workshop which we have organized at the MPIPKS in Dresden (germany) from 15.-19. April 2013. More information here. The workshop was a great success!


What is Advanced Materials Science and Technology
Playing text to speech
You’ve heard of materials science and engineering, but what are advanced materials science and technology? Advanced materials science is the study of the development, characterization, and production of next-generation materials. These are materials that have been designed to have improved properties or performance compared to existing materials. The field of advanced materials science covers a wide range of topics, from nanotechnology to quantum computing. In this blog post, we will explore what advanced materials science is and some of the different technologies involved in this cutting-edge field.
What are Advanced Materials?
Advanced materials are those that exhibit superior properties compared to conventional materials. They often have unique combinations of physical and chemical properties that make them suitable for a wide range of applications.
There are many different types of advanced materials, including:
• Ceramics: These are inorganic materials that have been used for centuries in a variety of applications, from pottery to engineering. Today, ceramics are used in everything from medical implants to solar panels.
• Composite materials: These are made by combining two or more dissimilar materials to create a material with improved properties. For example, fiber-reinforced composites are strong and lightweight, making them ideal for use in aircraft and sporting equipment.
• Nanomaterials: These are materials that measure just a few nanometers (billionths of a meter) in size. Due to their small size, nanomaterials often have unique physical and chemical properties that make them useful in a range of applications, from sensors to computer chips.
• Metals: Metals have been used by humans for thousands of years and continue to play an important role in society today. In recent years, advances in metallurgy have led to the development of new types of metals with improved properties, such as corrosion resistance and strength.
Properties of Advanced Materials
There are many different types of advanced materials, each with their own unique properties. Some common examples include:
-Graphene: A one-atom-thick layer of carbon that is incredibly strong and flexible.
-Nanocrystalline materials: Materials with extremely small grains that have enhanced strength and toughness.
-Metamaterials: Engineered materials with unusual properties, such as the ability to bend light in unprecedented ways.
-Shape memory alloys: Alloys that can be deformed at high temperatures and then returned to their original shape upon cooling.
Each of these materials has the potential to revolutionize various industries, from construction and transportation to electronics and computing.
Classification of Advanced Materials
- There are three primary categories of advanced materials: metals, polymers, and ceramics. Each of these categories can be further subdivided into subcategories.
- Metals: Metallic alloys are created by combining two or more metallic elements. They can be categorized based on their microstructure, which is the way the atoms are arranged within the metal. Common types of microstructures include grain boundary alloys, nanocrystalline alloys, and amorphous alloys.
- Polymers: Polymers are long chains of molecules that can be classified based on their chemical structure. Common types of polymers include thermoplastic polymers, thermosetting polymers, elastomers, and biopolymers.
- Ceramics: Ceramics are inorganic materials that are made up of non-metallic elements. They can be either crystalline or non-crystalline in nature. Common types of ceramics include oxide ceramics, nitride ceramics, carbide ceramics, and halide ceramics.
Some examples of Advanced Materials
- Advanced materials science and technology is a relatively new field that encompasses a wide range of material types, including nanomaterials, quantum materials, metallic glasses, and more. Researchers in this field are working to develop new materials with improved properties and performance characteristics.
- One example of an advanced material is carbon nanotubes. Carbon nanotubes are extremely strong and lightweight, making them ideal for use in a variety of applications, including energy storage, construction, and manufacturing. Another example is graphene, a one-atom-thick layer of carbon that is extremely strong and conductive. Graphene has potential applications in electronics, sensors, batteries, and more.
- Researchers are also working on developing new methods for 3D printing with advanced materials. This technology could be used to create customized parts and products with complex shapes and structures. Additionally, 3D printing with advanced materials could be used to create scaffolds for tissue regeneration or to fabricate medical devices.
How is Advanced Materials Science and Technology used?
- Advanced materials science and technology is used to develop new materials with improved properties. These improved properties can be due to the material’s composition, structure, or both. For example, advanced materials can be developed to be stronger and lighter than traditional materials, more heat resistant, more chemically resistant, or have other improved performance characteristics.
- Developing new advanced materials requires a deep understanding of the relationships between the material’s composition, structure, and properties. This understanding is typically gained through research at the atomic and molecular level. Once this understanding is attained, scientists and engineers can use this knowledge to design and synthesize new materials with specific desired properties.
- The development of advanced materials is an important area of research and development as these new materials can enable advances in many different fields. For example, stronger and lighter materials can lead to advances in transportation; more heat resistant materials can enable advances in energy production; and more chemically resistant materials can facilitate advances in environmental cleanup technologies.
Future of Advanced Materials Science and Technology
- Advanced materials science and technology is an interdisciplinary field that applies the properties of matter to create new materials with superior performance. It is a rapidly growing field with immense potential for transforming the way we live and work.
- The future of advanced materials science and technology is immensely bright. The field is constantly evolving, and new breakthroughs are being made all the time. We are on the cusp of major advances in many different areas, from developing stronger, lighter and more sustainable materials to creating new medical technologies and improving energy storage.
- There are endless possibilities for what we can achieve with advanced materials science and technology. In the coming years, we will continue to push the boundaries of what is possible, making everyday items smarter, stronger and more sustainable. We will also develop new technologies that have the potential to change the world, from medical treatments that can save lives to energy sources that are cleaner and more efficient.
Advanced materials science and technology is an exciting and growing field that promises to revolutionize the way we live and work. From stronger and lighter building materials to more efficient solar cells, advanced materials have the potential to transform our world. If you're interested in learning more about this field, be sure to check out our website for more information. Thanks for reading!


Quantum Machine Learning: An Overview
Quantum Machine Learning (Quantum ML) is the interdisciplinary area combining Quantum Physics and Machine Learning(ML). It is a symbiotic association- leveraging the power of Quantum Computing to produce quantum versions of ML algorithms, and applying classical ML algorithms to analyze quantum systems. Read this article for an introduction to Quantum ML.
At a recent conference in 2017, Microsoft CEO Satya Nadella used the analogy of a corn maze to explain the difference in approach between a classical computer and a quantum computer. In trying to find a path through the maze, a classical computer would start down a path, hit an obstruction, backtrack; start again, hit another obstruction, backtrack again until it ran out of options. Although an answer can be found, this approach could be very time-consuming.
In contrast, quantum computers “unlock amazing parallelism. They take every path in the corn maze simultaneously.” Thus, leading to an exponential reduction in the number of steps required to solve a problem.
The parallelism comes from the concept of ‘qubit’, 'superposition' and 'entanglement' derived from Quantum Physics.
I. Quantum Computing:
Quantum is the smallest possible unit of any physical entity, such as energy or mass. In 1900, Max Planck proposed that, at the atomic and subatomic level, energy of a body is contained in discrete packets called quanta’.
Wave-particle duality is the characteristic of quantic particles to behave as a wave sometimes and as a particle the other times, depending on the environment. Quantum theory is characterized by finding the probability of, and not the exact location of, a particle at a given point x in space.
Fig 1: The dual nature of light, which acts like both particles and waves. (Source)
A classical computer performs operations using classical ‘bits’, which are either 0 OR 1. However, a quantum computer uses quantum bits, also called ‘qubits’ to perform operations.
Qubits can be represented by:
- An electron orbiting a nucleus: where |1> and |0> are the excited state and ground state respectively
- A photon: where |1> and |0> are polarizations of the photon.
Qubits exist as both 0 AND 1 at the same time. This phenomenon is called ‘superposition’.
Although a particle can exist in multiple quantum states, once we measure that particle for its energy or position, its superposition is lost and it then exists in only one state.
Fig2 : The qubit is defined as a pair of complex vectors pointing to a spot on a unit sphere. Traditionally, a qubit pointing directly up (positive on the axis) is denoted as the column vector |0⟩ and the vector pointing down is known as |1⟩. (For example, in this case, the qubit is |0⟩).
‘Quantum entanglement’ is the phenomenon in which quantum particles interact with each other and are described with reference to each other, not independently, even if the particles are separated by a large distance.
At the time of measurement, if one entangled particle in a pair is decided to be in the spin state of ‘down’ (that is, the lowest energy state; when the electron is in alignment with its magnetic field), then this decision is communicated to the other correlated particle that now assumes the opposite spin state of ‘up’. Quantum entanglement allows qubits, including those faraway, to interact instantaneously with each other.
How does Quantum computing unlock immense parallelism?
Two interacting classical bits can take one of 4 forms: 00 or 01 or 10 or 11. Each of these 2 components of information- the first bit and the second bit, combine to represent only one binary configuration at a given time. Adding more bits to a regular computer would still represent a single binary configuration.
Fig3: One qubit in superposition before measurement, with its probabilities of ‘spin-up’ AND ‘spin-down'. (Source)
One qubit can exist in both states (0 AND 1) at once. Thus, two interacting qubits can store all 4 binary configurations simultaneously. In general, ‘n’ qubits can simultaneously represent ‘2^n’ classical binary configurations. Thus, a 300–qubit quantum computer can explore 2^300 possible solutions at the same time, unlike 1 solution at a time in a classical computer, causing immense parallelism. Adding more qubits to a quantum computer would exponentially increase the power of the computer.
A fully quantum computer has not yet been realized because adding more qubits and dealing with subatomic particles that require a low temperature of -452 F in order to be stable, is daunting and building a computer around that (a ‘quantum computer’), even more so. Thus, efforts are on to ‘simulate’ 40 qubit operations using Microsoft’s quantum simulator- LIQUi|> , extended by Microsoft Azure’s cloud computing resources.
Quantum Computing can solve specialized scientific problems such as molecular modelling, creation of high-temperature superconductors, drug modelling and testing, selection of molecules for the creation of organic batteries. It is not optimal for general-purpose tasks such as for watching videos or writing a Word document.
Now, how does Quantum Computing fit in with Machine Learning?
II. Quantum ML:
2a) Quantum versions of ML algorithms
- Finding eigenvalues and eigenvectors of large matrices:
One of the methods to perform the classical PCA algorithm is to take the eigenvalue decomposition of a data covariance matrix. However, this is not so efficient in case of high dimensional data.
Quantum PCA of an unknown low-rank density matrix, can reveal the quantum eigenvectors associated with the large eigenvalues, exponentially faster than a linearly-scaled classical algorithm.
- Finding nearest neighbours on a quantum computer:
The quantum algorithms presented here for computing nearest neighbours that are used in supervised and unsupervised learning, place an upper bound on the number of queries to the input data required to compute distance metrics such as the Euclidean distance and inner product. The best cases show exponential and super-exponential reductions in query complexity and the worst case still shows polynomial reduction in query complexity over the classical analogue.


Researchers have taken an important step toward the long-sought goal of a quantum computer, which in theory should be capable of vastly faster computations than conventional computers, for certain kinds of problems. The new work shows that collections of ultracold molecules can retain the information stored in them, for hundreds of times longer than researchers have previously achieved in these materials.
These two-atom molecules are made of sodium and potassium and were cooled to temperatures just a few ten-millionths of a degree above absolute zero (measured in hundreds of nanokelvins, or nK). The results are described in a report this week in Science, by Martin Zwierlein, an MIT professor of physics and a principal investigator in MIT’s Research Laboratory of Electronics; Jee Woo Park, a former MIT graduate student; Sebastian Will, a former research scientist at MIT and now an assistant professor at Columbia University, and two others, all at the MIT-Harvard Center for Ultracold Atoms.
Many different approaches are being studied as possible ways of creating qubits, the basic building blocks of long-theorized but not yet fully realized quantum computers. Researchers have tried using superconducting materials, ions held in ion traps, or individual neutral atoms, as well as molecules of varying complexity. The new approach uses a cluster of very simple molecules made of just two atoms.
Using this kind of two-atom molecules for quantum information processing “had been suggested some time ago,” says Park, “and this work demonstrates the first experimental step toward realizing this new platform, which is that quantum information can be stored in dipolar molecules for extended times.”
This vacuum chamber with apertures for several laser beams was used to cool molecules of sodium-potassium down to temperatures of a few hundred nanoKelvins, or billionths of a degree above absolute zero. Such molecules could be used as a new kind of qubit, a building block for eventual quantum computers. Courtesy of the researchers
“The most amazing thing is that [these] molecules are a system which may allow realizing both storage and processing of quantum information, using the very same physical system,” Will says. “That is actually a pretty rare feature that is not typical at all among the qubit systems that are mostly considered today.”
In the team’s initial proof-of-principle lab tests, a few thousand of the simple molecules were contained in a microscopic puff of gas, trapped at the intersection of two laser beams and cooled to ultracold temperatures of about 300 nanokelvins. “The more atoms you have in a molecule the harder it gets to cool them,” Zwierlein says, so they chose this simple two-atom structure.
The molecules have three key characteristics: rotation, vibration, and the spin direction of the nuclei of the two individual atoms. For these experiments, the researchers got the molecules under perfect control in terms of all three characteristics — that is, into the lowest state of vibration, rotation, and nuclear spin alignment.
“We have strong hopes that we can do one so-called gate — that’s an operation between two of these qubits, like addition, subtraction, or that sort of equivalent — in a fraction of a millisecond,” Zwierlein says. “If you look at the ratio, you could hope to do 10,000 to 100,000 gate operations in the time that we have the coherence in the sample. That has been stated as one of the requirements for a quantum computer, to have that sort of ratio of gate operations to coherence times.”
“The next great goal will be to ‘talk’ to individual molecules. Then we are really talking quantum information,” Will says. “If we can trap one molecule, we can trap two. And then we can think about implementing a ‘quantum gate operation’ — an elementary calculation — between two molecular qubits that sit next to each other,” he says.
Using an array of perhaps 1,000 such molecules, Zwierlein says, would make it possible to carry out calculations so complex that no existing computer could even begin to check the possibilities. Though he stresses that this is still an early step and that such computers could be a decade or more away, in principle such a device could quickly solve currently intractable problems such as factoring very large numbers — a process whose difficulty forms the basis of today’s best encryption systems for financial transactions.
Besides quantum computing, the new system also offers the potential for a new way of carrying out precision measurements and quantum chemistry, Zwierlein says.
Extending the coherence time of molecules
Quantum properties of atoms and molecules can be exploited for precision measurements or quantum information processing. The complex state structure of molecules can be exploited, but it is hard to preserve the coherence between pairs of those states in applications. Park et al. created fermionic molecules of NaK in the rovibrational ground state that maintained coherence between their nuclear spin states on a time scale of 1 second. This long coherence time makes dipolar ultracold molecules a valuable quantum resource.
Coherence, the stability of the relative phase between quantum states, is central to quantum mechanics and its applications. For ultracold dipolar molecules at sub-microkelvin temperatures, internal states with robust coherence are predicted to offer rich prospects for quantum many-body physics and quantum information processing. We report the observation of stable coherence between nuclear spin states of ultracold fermionic sodium-potassium (NaK) molecules in the singlet rovibrational ground state. Ramsey spectroscopy reveals coherence times on the scale of 1 second; this enables high-resolution spectroscopy of the molecular gas. Collisional shifts are shown to be absent down to the 100-millihertz level. This work opens the door to the use of molecules as a versatile quantum memory and for precision measurements on dipolar quantum matter.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.


The speed of light is the rate at which light travels. The speed of light in a vacuum is a constant value that is denoted by the letter c and is defined as exactly 299,792,458 meters per second. Visible light, other electromagnetic radiation, gravity waves, and other massless particles travel at c. Matter, which has mass, can approach the speed of light, but never reach it.
Value for the Speed of Light in Different Units
Here are values for the speed of light in various units:
- 299,792,458 meters per second (exact number)
- 299,792 kilometers per second (rounded)
- 3×108 m/s (rounded)
- 186,000 miles per second (rounded)
- 671,000,000 miles per hour (rounded)
- 1,080,000,000 kilometers per hour (rounded)
Is the Speed of Light Really Constant?
The speed of light in a vacuum is a constant. However, scientists are exploring whether the speed of light has changed over time.
Also, the rate at which light travels changes as it passes through a medium. The index of refraction describes this change. For example, the index of refraction of water is 1.333, which means light travels 1.333 times slower in water than in a vacuum. The index of refraction of a diamond is 2.417. A diamond slows the speed of light by more than half its speed in a vacuum.
How to Measure the Speed of Light
One way of measuring the speed of light uses great distances, such as distant points on the Earth or known distances between the Earth and astronomical objects. For example, you can measure the speed of light by measuring the time it takes for light to travel from a light source to a distant mirror and back again. The other way of measuring the speed of light is solving for c in equations. Now that the speed of light is defined, it is fixed rather than measured. Measuring the speed of light today indirectly measures the length of the meter, rather than c.
In 1676, Danish astronomer Ole Rømer discovered light travels at a speed by studying the movement of Jupiter’s moon Io. Prior to this, it seemed light propagated instantaneously. For example, you see a lightning strike immediately, but don’t hear thunder until after the event. So, Rømer’s finding showed light takes time to travel, but scientists did not know the speed of light or whether it was constant. In 1865, James Clerk Maxwell proposed that light was an electromagnetic wave that travelled at a speed c. Albert Einstein suggested c was a constant and that it did not change according to the frame of reference of the observer or any motion of a light source. In other words, Einstein suggested the speed of light is invariant. Since then, numerous experiments have verified the invariance of c.
Is It Possible to Go Faster Than Light?
The upper speed limit for massless particles is c. Objects that have mass cannot travel at the speed of light or exceed it. Among other reasons, traveling at c gives an object a length of zero and infinite mass. Accelerating a mass to the speed of light requires infinite energy. Furthermore, energy, signals, and individual photos cannot travel faster than c. At first glance, quantum entanglement appears to transmit information faster than c. When two particles are entangled, changing the state of one particle instantaneously determines the state of the other particle, regardless of the distance between them. But, information cannot be transmitted instantaneously (faster than c) because it isn’t possible to control the initial quantum state of the particle when it is observed.
However, faster-than-light speeds appear in physics. For example, the phase velocity of x-rays through glass often exceeds c. However, the information isn’t conveyed by the waves faster than the speed of light. Distant galaxies appear to move away from Earth faster than the speed of light (outside a distance called the Hubble sphere), but the motion isn’t due to the galaxies traveling through space. Instead, space itself it expanding. So again, no actual movement faster than c occurs.
While it isn’t possible to go faster than the speed of light, it doesn’t necessarily mean warp drive or other faster-than-light travel is impossible. The key to going faster than the speed of light is to change space-time. Ways this might happen include tunneling using wormholes or stretching space-time into a “warp bubble” around a spacecraft. But, so far these theories don’t have practical applications.
- Brillouin, L. (1960). Wave Propagation and Group Velocity. Academic Press.
- Ellis, G.F.R.; Uzan, J.-P. (2005). “‘c’ is the speed of light, isn’t it?”. American Journal of Physics. 73 (3): 240–27. doi:10.1119/1.1819929
- Helmcke, J.; Riehle, F. (2001). “Physics behind the definition of the meter”. In Quinn, T.J.; Leschiutta, S.; Tavella, P. (eds.). Recent advances in metrology and fundamental constants. IOS Press. p. 453. ISBN 978-1-58603-167-1.
- Newcomb, S. (1886). “The Velocity of Light”. Nature. 34 (863): 29–32. doi:10.1038/034029c0
- Uzan, J.-P. (2003). “The fundamental constants and their variation: observational status and theoretical motivations”. Reviews of Modern Physics. 75 (2): 403. doi:10.1103/RevModPhys.75.403


Emerging technologies are innovative technical developments that have the potential to change the way we live and work. Some examples of emerging technologies include artificial intelligence, the Internet of Things, virtual and augmented reality, blockchain, gene editing, robotics, and quantum computing. These technologies are still in the early stages of development, but have the potential to revolutionize industries and change the world in significant ways. It is important to consider both the potential benefits and risks of emerging technologies as they continue to evolve and become more widely adopted.
Emerging technologies are those technical innovations that represent progressive developments within a field for competitive advantage. These technologies are generally in the early stages of development and are not yet mature enough for commercial use, but have the potential to significantly change the way we live and work.
One emerging technology that has garnered a lot of attention in recent years is artificial intelligence (AI). AI refers to the ability of machines to perform tasks that would normally require human-level intelligence, such as learning, problem-solving, and decision-making. There are many potential applications for AI, including automating repetitive and time-consuming tasks, improving decision-making processes, and enabling the development of new products and services.
Another emerging technology is the Internet of Things (IoT), which refers to the interconnectedness of physical objects through the internet. This technology allows objects to be connected and controlled remotely, enabling them to send and receive data and perform actions based on that data. The IoT has the potential to revolutionize the way we live and work, with applications ranging from smart homes and cities to connected factories and agriculture.
Virtual and augmented reality
Virtual and augmented reality (VR and AR) are also emerging technologies that are gaining traction. VR involves the use of computer technology to create a simulated environment, while AR involves superimposing computer-generated images onto the real world. These technologies have a wide range of applications, including entertainment, education, and training. For example, VR and AR can be used to create immersive experiences, such as virtual field trips or simulated training scenarios.
Blockchain technology is another emerging technology that has generated a lot of buzz in recent years. A blockchain is a decentralized, distributed ledger that records transactions on multiple computers, making it virtually impossible to alter or hack. This technology has the potential to revolutionize many industries, including finance, healthcare, and supply chain management.
There are many other emerging technologies that have the potential to shape the future, including gene editing, robotics, and quantum computing. Gene editing involves making precise changes to the DNA of living organisms, with the potential to cure diseases, improve crop yields, and more. Robotics refers to the use of machines to perform tasks that would normally require human labor, and has applications in manufacturing, transportation, and more. Quantum computing involves the use of quantum-mechanical phenomena, such as superposition and entanglement, to perform calculations that would be impossible for classical computers. This technology has the potential to solve problems that would take classical computers years to solve in just minutes.
Artificial intelligence (AI) refers to the ability of machines to perform tasks that would normally require human-level intelligence, such as learning, problem-solving, and decision-making. There are many potential applications for AI, including automating repetitive and time-consuming tasks, improving decision-making processes, and enabling the development of new products and services.AI can be classified into two main categories: narrow or general. Narrow AI is designed to perform a specific task, such as facial recognition or language translation. It is often referred to as "weak AI." General AI, on the other hand, is designed to be able to perform any intellectual task that a human can. It is often referred to as "strong AI." While narrow AI is currently more prevalent, the ultimate goal of AI research is to develop general AI. There are several approaches to developing AI, including machine learning and deep learning. Machine learning involves training algorithms on a large dataset and allowing them to improve their performance over time, without being explicitly programmed. Deep learning is a subset of machine learning that involves the use of artificial neural networks to learn and make decisions.
AI has the potential to revolutionize many industries, including healthcare, information technology, transportation, finance, and manufacturing. It can also have a significant impact on society as a whole, both positive and negative. On the positive side, AI can be used to solve global challenges such as poverty, disease, and climate change. On the negative side, it could lead to job displacement and social and economic inequality. The development and use of AI raise ethical concerns, including issues around privacy, bias, and accountability. It is important for researchers and policymakers to consider these issues and work to address them as AI continues to evolve and become more prevalent in our lives.
In conclusion, companies like Scrrum Labs have emerging technologies that have the potential to significantly impact the way we live and work. These technologies are still in the early stages of development and have not yet reached maturity, but they have the potential to revolutionize industries and change the world in ways we can't yet imagine.


Updated on January 16, 2023 1:42 PM
Encryption standards play a vital role in securing blockchain ecosystems from malware and unusual authentication or attacks. Generally, Cryptographic algorithms are used to protect the data within a blockchain which only could be accessed via keys.
These keys can be publicly shared or even act as private keys. In this section of basics, we are going to learn about Symmetric-key cryptography which is an algorithm for Blockchain encryption. Let’s dive deep into this.
Symmetric-key Cryptography is a kind of encryption that uses a single key (a secret key) to encode and decode electronic data. The key must be exchanged between the organizations communicating using symmetric encryption so that it may be utilized in the decryption process. This encryption method is distinct from asymmetric encryption, which employs a pair of keys, one public and one private, to encrypt and decode messages.
For example, we all have used Google docs (if not then use it once). The content we put on our doc file is only visible to us until we disable the sharing restrictions from the top right bar. When we opt for the “Share with anyone” feature, we get a link which then could be shared with a reliable person whom you wanted to get access to your doc file to see the content.
Suppose the doc file is “Data or information” and the “link” is the key. This suffices the example of Symmetric-key cryptography.
Encoding data in this manner has been widely utilized in earlier decades to permit covert communication between governments and armies.
Symmetric-key cryptography is also known as shared-key cryptography, secret-key cryptography, single-key cryptography, one-key cryptography, and finally private-key cryptography. With this type of encryption, it is obvious that the key must be known by both the sender and the recipient. The distribution of the key is the source of the approach's intricacy.
Symmetric encryption technique ”scrambles” the data so that it cannot be understood by anybody who does not have the secret key to decode it. Once the intended receiver who holds the key obtains the message, the algorithm reverses its activity such that the message is restored to its original readable form. The secret key used by both the sender and the receiver might be a specific password/code or a random string of letters or numbers created using a secure random number generator (RNG).
Source: by David McNeal (TheCryptoWriter) | Medium
The above diagram could be concluded as:
The sender encrypts their information with an encryption key (often a string of letters and numbers).
The encrypted information, known as ciphertext, appears as jumbled letters and is unreadable by anybody along the road.
The decryption key is used by the receiver to convert the ciphertext back into readable text.
The previous example shows that the information was accessed using the same key.
The data can only be viewed and accessed by these two parties (sender and recipient). This is why it is also known as secret key cryptography, private key cryptography, symmetric cryptography, and symmetric key encryption.
The use of a single key for both encryption and decryption streamlines the encryption process. After all, you're using a single key to convert readable plaintext into unreadable gibberish (ciphertext) and vice versa. One benefit of employing symmetric encryption is that it enables data privacy and confidentiality without the additional complexity of many keys.
For some applications, symmetric key encryption works on its own. It's handy for encrypting databases and files, for example, when no data is being sent openly between parties.
Symmetric Key cryptography is classified into two types:
Let’s understand both separately.
Block algorithms serve to secure electronic data blocks. While using the authorized private key, predefined set lengths of bits are changed. This key is then applied to every block. When encrypting network stream data, the encryption system stores the data in its memory components while waiting for the whole blocks. The amount of time the system waits can create a security hole and compromise data security and integrity. The solution to the problem comprises a method in which the data block can be lowered and merged with the contents of the preceding encrypted data block until the rest of the blocks emerge. This is referred to as feedback. Only once the complete block has been received it is then marked as encrypted.
Stream algorithms are not stored in the memory of the encryption system but instead come in data stream algorithms. This approach is deemed slightly safer since a disk or system does not preserve data without encryption in the blocks.
Some examples of Symmetric-Key Cryptography:
AES (Advanced Encryption Standard)
DES (Data Encryption Standard)
IDEA (International Data Encryption Algorithm)
Blowfish (Drop-in replacement for DES or IDEA)
RC4 (Rivest Cipher 4)
RC5 (Rivest Cipher 5)
RC6 (Rivest Cipher 6)
AES, DES, IDEA, Blowfish, RC5 and RC6 are block ciphers. RC4 is a stream cipher.
Despite being an older kind of encryption, symmetric encryption is quicker and more effective than asymmetric encryption, which strains networks owing to performance concerns with data quantity and high CPU usage.
Symmetric cryptography is generally used for bulk encryption / encrypting huge volumes of data, such as for database encryption, due to its greater performance and faster speed (relative to asymmetric encryption).
The secret key could only be accessible to the database itself to encrypt or decrypt in the event of a database. Comparing existing standards for asymmetric algorithms to industry-standard symmetric encryption, one can see that the latter is less susceptible to developments in quantum computing (at the time of writing).
Here are some instances in which symmetric cryptography is applied:
Applications for making payments, like card transactions, require the protection of PII to stop identity theft and fraudulent charges.
validations that a message's sender is who they say they are.
Hashing or the random number generation.
In addition to encryption, symmetric ciphers are frequently employed to accomplish other cryptographic primitives.
A message cannot be guaranteed to remain intact while being encrypted. As a result, a message authentication code is frequently appended to a ciphertext to make sure that the recipient will be aware of modifications to the ciphertext. From an AEAD cipher, message authentication codes may be created (e.g. AES-GCM).
However, without involving extra parties, symmetric ciphers cannot be employed for non-repudiation (the assurance that someone cannot deny the validity of something) reasons.
Symmetric encryption uses a single key that must be shared with the individuals who need to receive the message.
While, Asymmetrical encryption involves a pair, consisting of a public key and a private key, to encrypt and decode messages while communicating
An old concept
Relatively new concept
In symmetric cryptography, data is encrypted or decrypted using a single shared key that both parties are aware of.
Asymmetric encryption is developed to address the issue of the symmetric encryption model's requirement for key exchange by replacing the key with a pair of public-private keys.
The execution time is much faster
Slower execution time.
The most recent technologies could sometimes be the ideal fit when it comes to encryption. In reality, as cryptography develops in a new way, new protocols are being built to keep up with would-be hackers and to safeguard the data to increase privacy. In the upcoming years, hackers will inevitably make things difficult for specialists, thus we can confidently anticipate new advancements from the cryptography community.


On the 4th November 1964, the physicist John S. Bell published a paper called On the Einstein-Podolsky-Rosen paradox. This was an important paper for both philosophy and physics with implications for our understanding of reality and freedom.
When quantum theory was developed in the early 20th century, the philosophical implications troubled some, including Einstein. The “Copenhagen interpretation” put realism in science under threat. Although the “macro” world (people, planets, plates and platypuses) were argued to be real existing things, electrons and other particles were held not to be. The world was therefore divided into the “classical” and the “quantum” worlds, or as John S. Bell later called them, the “speakable” and the “unspeakable”.
In 1935, Einstein published a paper with Nathan Rosen and Boris Podolsky (known collectively as EPR) arguing that quantum mechanics was not a complete theory, but required additional “hidden” variables to preserve realism and locality. “In the vernacular of Einstein: locality meant no instantaneous (“spooky”) action at a distance; realism meant the moon is there even when not being observed.” (wiki)
Bell also argued for realism, thus rejecting the Copenhagen Interpretation. He worked with realist theories such as de Broglie–Bohm theory, but the theory violated the EPR locality criterion. This fact was used to argue that it was on the wrong track, but Bell’s 1964 paper showed that “any serious version of quantum theory (regardless of whether or not it is based on microscopic realism) must violate locality. This means that if nature is governed by the predictions of quantum theory, the ‘locality principle’ is simply wrong, and our world is nonlocal” (American Scientist)
Experiments have since been carried out demonstrating that nature does indeed follow the predictions of quantum theory in the required way. The “conclusion that there are hidden variables implies that, in some spin-correlation experiments, the measured quantum mechanical probabilities should satisfy particular inequalities (Bell-type inequalities). The paradox consists in the fact that quantum probabilities do not satisfy these inequalities. And this paradoxical fact has been confirmed by several laboratory experiments since the 1970s” (IEP).
Thus Bell converted the EPR thought experiment into real experiments, albeit with results that Einstein would have disliked. It suggests that any quantum theory we arrive at will conflict with common sense. (It also has technical implications for technical advances such as quantum cryptography and quantum computing.)
Later, Bell suggested a hypothesis which would resolve the “spooky action” problem without requiring faster-than-light information transfer: super-determinism. Super-determinism states “[t]hat not only is inanimate nature deterministic, but we, the experimenters who imagine we can choose to do one experiment rather than another, are also determined. If so, the difficulty which this experimental result creates disappears” (from The Ghost in the Atom, P.C.W. Davies and J. Brown, ch.3, p.47, quoted here) – in other words, free will is an illusion.
Bell demonstrated that philosophy and physics can usefully interact. In the words of Tim Maudlin (“on the foundations of physics” in 3:am):
In my view, the greatest philosopher of physics in the first half of the 20th century was Einstein and in the second half was John Stewart Bell. So physicists who say that professional philosophers have not made the greatest contributions to foundations of physics are correct. But both Einstein and Bell had philosophical temperaments, and Einstein explicitly complained about physicists who had no grounding in philosophy. The community of people who work in foundations of physics is about evenly divided between members of philosophy departments, members of physics departments and members of math departments. […] A more salient division in contemporary foundations is between those, like myself, who judge that Bell was basically correct in almost everything he wrote and those who think that his theorem does not show much of interest and his complaints about the unprofessional vagueness that infects quantum theory are misplaced.
Bell’s essay “Against ‘measurement'” lists “system, apparatus, environment, microscopic, macroscopic, reversible, irreversible, observable, information, measurement” as terms ubiquitous in quantum theory that cannot be defined precisely. Without precision, concepts and theory cannot hope to be precise either.
John S. Bell died of a stroke in 1990. At the time of his death he was widely believed to be a front runner for the Nobel Prize in Physics. Surprisingly, his abilities in physics were almost lost. Born in Belfast in 1928, he failed to win a scholarship to grammar school and left school at 16. It was when he was working as a laboratory assistant in Queen’s University that his talent was spotted by Professors Karl Emelaus and Robert Sloane, who encouraged him to attend first-year lectures. Bell then enrolled, obtaining two first-class honours degrees at Queen’s followed by a PhD in the University of Birmingham. He then worked at the UK Atomic Energy Research Establishment, before moving to CERN.
Professor Mary Daly, President of the Royal Irish Academy said ‘The Academy wants John Bell to be the best known scientist in Northern Ireland and to be acknowledged as one of the most important scientists in the world’. A number of events are planned for John Bell Day – see the RIA for more information.
Michael Nauenberg, John Bell’s Major Contribution to Physics and Philosophy, RIA.
Help Wanted: Philosopher required to sort out Reality – Philosophy Now. A great straightforward overview, but available to subscribers only.
Tim Maudlin, PBS: Why Physics Needs Philosophy
Irish Times: Ireland’s rich history in science deserves acclaim. Opinion: time to give John Bell the recognition he deserves.
Scientia Salon: Quantum mechanics and scientific realism – on the difficulties of creating a realist theory of quantum mechanics.
— CERN (@CERN) November 4, 2014
— Queen's University (@QueensUBelfast) November 4, 2014
— Love Belfast (@love_belfast) November 4, 2014
John Bell: Belfast street to be named after physicist – a street next to Belfast Metropolitan College in the Titanic Quarter will be named Bell’s Theorem Way or Bell’s Theorem Crescent.


Humanity is in a back-and-forth relationship with nature. First, we thought we were at the center of everything, with the Sun and the entire cosmos rotating around our little planet. We eventually realized that wasn’t true. Over the centuries, we’ve found that though Earth and life might be rare, our Sun is pretty normal, our Solar System is relatively non-descript, and even our galaxy is one of the billions of spiral galaxies, a type that makes up 60% of the galaxies in the Universe.
But the Illustris TNG simulation shows that the Milky Way is special.
The large scale structure of the universe is dominated by vast empty regions known as cosmic voids. These voids appear as holes hundreds of millions of light years across in the distribution of galaxies. However, new research shows that many of them may surprisingly still be filled with dark matter.
In 1916, Einstein finished his Theory of General Relativity, which describes how gravitational forces alter the curvature of spacetime. Among other things, this theory predicted that the Universe is expanding, which was confirmed by the observations of Edwin Hubble in 1929. Since then, astronomers have looked farther into space (and hence, back in time) to measure how fast the Universe is expanding – aka. the Hubble Constant. These measurements have become increasingly accurate thanks to the discovery of the Cosmic Microwave Background (CMB) and observatories like the Hubble Space Telescope.
Astronomers have traditionally done this in two ways: directly measuring it locally (using variable stars and supernovae) and indirectly based on redshift measurements of the CMB and cosmological models. Unfortunately, these two methods have produced different values over the past decade. As a result, astronomers have been looking for a possible solution to this problem, known as the “Hubble Tension.” According to a new paper by a team of astrophysicists, the existence of “Early Dark Energy” may be the solution cosmologists have been looking for.
Constraints are critical in any scientific enterprise. If a hypothesis predicts that there should be an observable phenomenon, and there isn’t any trace of it, that’s a pretty clear indication that the hypothesis is wrong. And even false hypotheses still move science forward. So it is with astronomy and, in particular, explorations of the early universe. A paper authored by researchers at Cambridge and colleagues now puts a particularly useful constraint on the development of early galaxies, which has been a hot topic in astronomy as of late.
For the first time, scientists have created a quantum computing experiment for studying the dynamics of wormholes — that is, shortcuts through spacetime that could get around relativity’s cosmic speed limits.
“We found a quantum system that exhibits key properties of a gravitational wormhole, yet is sufficiently small to implement on today’s quantum hardware,” Caltech physicist Maria Spiropulu said in a news release. Spiropulu, the Nature paper’s senior author, is the principal investigator for a federally funded research program known as Quantum Communication Channels for Fundamental Physics.
Don’t pack your bags for Alpha Centauri just yet: This wormhole simulation is nothing more than a simulation, analogous to a computer-generated black hole or supernova. And physicists still don’t see any conditions under which a traversable wormhole could actually be created. Someone would have to create negative energy first.
According to the Standard Model of Particle Physics, the Universe is governed by four fundamental forces: electromagnetism, the weak nuclear force, the strong nuclear force, and gravity. Whereas the first three are described by Quantum Mechanics, gravity is described by Einstein’s Theory of General Relativity. Surprisingly, gravity is the one that presents the biggest challenges to physicists. While the theory accurately describes how gravity works for planets, stars, galaxies, and clusters, it does not apply perfectly at all scales.
While General Relativity has been validated repeatedly over the past century (starting with the Eddington Eclipse Experiment in 1919), gaps still appear when scientists try to apply it at the quantum scale and to the Universe as a whole. According to a new study led by Simon Fraser University, an international team of researchers tested General Relativity on the largest of scales and concluded that it might need a tweak or two. This method could help scientists to resolve some of the biggest mysteries facing astrophysicists and cosmologists today.
Johns Hopkins University (JHU) continues to pad its space community résumé with their interactive map, “The map of the observable Universe”, that takes viewers on a 13.7-billion-year-old tour of the cosmos from the present to the moments after the Big Bang. While JHU is responsible for creating the site, additional contributions were made by NASA, the European Space Agency, the National Science Foundation, and the Sloan Foundation.
Something huge lurks in the shadows of the Universe. Known as the Great Attractor, it is causing the Milky Way and all the surrounding galaxies to rush towards it. We would normally have a better understanding of this situation, except for the fact that the Great Attractor happens to lie in the direction behind the galactic bulge, which makes it difficult for us to observe. A team of astronomers have performed a new infrared survey of the region behind the bulge, and they have found yet another large galaxy cluster. Their work is helping to paint a more complete portrait of the environment of the Great Attractor.
In 2011, the Nobel Prize in physics was awarded to Perlmutter, Schmidt, and Reiss for their discovery that the universe is not just expanding, it is accelerating. The work supported the idea of a universe filled with dark energy and dark matter, and it was based on observations of distant supernovae. Particularly, Type Ia supernovae, which have consistent light curves we can use as standard candles to measure cosmic distances. Now a new study of more than 1,500 supernovae confirms dark energy and dark matter, but also raises questions about our cosmological models.