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How to Explain Post Quantum Cryptography to Anyone
Dr Michael Scott
Its actually not as complicated as it sounds. Let's get the maths over with first. Remember polynomials?
This would be an example of two first degree polynomials being multiplied together to create a second degree polynomial (or quadratic). In general two n-th degree polynomials when multiplied together create a 2n-th degree polynomial result. Polynomials can also be added
(3x+5)+(5x+6) = 8x+11
Don't tell me that's hard! For the polynomial 8x+11, the coefficients are 8 and 11.
Next consider polynomials where all of the coefficients are less than a fixed prime number q. If they ever get greater than q, they are reduced to their remainder when divided by q. So if q=7, then
because 8 leaves a remainder of 1 when divided by 7, and 11 leaves a remainder of 4 when divided by 7.
That's it for the maths. The next thing we will do is scale it up a little(!) Lets choose q=12289, and consider polynomials of degree 1024. Such a polynomial will look like
We've shortened it a bit, but you get the idea. Again it's easy to multiply two such polynomials,although obviously a computer is needed to do it. In fact due to the cunning choice for q and the degree as a power of 2 (1024=210), there is a particularly fast way to do the multiplication.
Now normally when we multiply two such polynomials, we get a 2048-th degree polynomial product. But here instead we chop this into two 1024-degree halves, and subtract the top half from the bottom half. That's our result, another 1024-degree polynomial.
So now we can quickly add, subtract and multiply 1024 degree polynomials to our hearts content in any order, generating 1024 degree polynomial results whose coefficients are all less than q.
We are now ready to do some crypto. First some notation. A polynomial as above with large coefficients we should denote as F(x), but we will simply call it F. We will also make use of polynomials with small coefficients, like
We will denote these with lower case letters, like f. Note that they are small only in terms of their coefficients, they are still of high degree. We shall call a polynomial with large coefficients a "large polynomial", and a polynomial with small coefficients a "small polynomial".
Now consider this calculation with such polynomials
Given A, s and e, its easy to calculate B, its just a multiplication followed by an addition. However given B and A, its very hard to calculate s and e. Think about it for a while - or just take my word for it! In fact for the size of polynomials we are talking about here its impossible even if you have a quantum computer! We call the small polynomial e an error polynomial, and the small polynomial s is often a secret. The large polynomial A is often a globally known value.
OK let's do some crypto. Alice and Bob who have no prior arrangement or shared secrets (although they both know a public large polynomial A), nevertheless want to communicate in private. In fact Bob wants to transmit an encrypted message to Alice that only Alice can read.
First Bob encodes his message as a large polynomial M.
Alice then calculates B=As+e where s and e are small secret and error polynomials of her own invention, and sends B to Bob. Bob calculates U=At+f and C=Bt+g+M and sends U and C back to Alice, where t, f and g are small polynomials of his own invention.
Finally Alice calculates C-Us. Substituting for C and U, and then for B, this becomes et+g+M-fs (there is some fortuitous cancellation). Check it for yourself.
At this stage the reader might well feel a little bewildered, and be wondering – so what? But this is the clever bit. Observe that in the expression et+g+M-fs, only M is a large polynomial. The other components are all small. So in effect M stands out from the "noise", and can thus be recovered by Alice. So Alice got the message, and anyone who eavesdropped their communication gets a problem they cannot possibly solve.
That's basically it. This is the NewHope post-quantum key exchange protocol as described by Alkim, Ducas, Poppelmann and Schwabe. The strength of the protocol depends on the difficulty of the so-called Ring Learning with Errors problem, which is a problem based on lattices, and for which there is no known effective quantum algorithm. There is of course a bit more to it (!) than given in this simple description, mainly concerning the ways in which the small polynomials are generated, and the statistical distribution of their small coefficients. This needs to be done carefully to avoid some attacks in some contexts. There is also the issue of effective encoding and decoding of the message M. But these are really just details.
At this stage I hope you are thinking – that was surprisingly easy. In fact post-quantum crypto, in my humble opinion, is often quite shallow mathematically. Its also blazingly fast. The downside is that the amount of data that must be exchanged is relatively large – those large polynomials are seriously big chunks of data.
But it works!


Researchers successfully sent a simulated elementary particle back in time
Don't start investing in flux capacitors just yet, though.
- The second law of thermodynamics states that order always moves to disorder, which we experience as an arrow of time.
- Scientists used a quantum computer to show that time travel is theoretically possible by reverting a simulated particle from an entropic to a more orderly state.
- While Einstein's general theory of relativity permits time travel, the means to achieve it remain improbable in nature.
In 1895 H.G. Wells published The Time Machine, a story about an inventor who builds a device that travels through a fourth, temporal dimension. Before Wells's novella, time travel existed in the realm of fantasy. It required a god, an enchanted sleep, or a bonk on the head to pull off. After Wells, time travel became popularized as a potentially scientific phenomenon.
Then Einstein's equations brought us into the quantum realm and there a more nuanced view of time. No less than mathematical logician Kurt Gödel worked out that Einstein's equations allowed for time travel into the past. The problem? None of the proposed methods of time travel were ever practical "on physical grounds."
So, "Why stick to physical grounds?" asked scientists from the Argonne National Laboratory, the Moscow Institute of Physics and Technology, and ETH Zurich before they successfully sent a simulated elementary particle back in time.
Fair warning: their results are tantalizing but will ultimately dishearten any time lords in training.
The great quantum escape
A quantum computer mixing chamber (Photo: IBM Research/Flickr)
Many of the laws of physics view the future and the past as a difference without a distinction. Not so with the second law of thermodynamics, which states that a closed system always moves from order to disorder (or entropy). Scramble an egg to make your omelet, for example, and you've added a whole lot of disorder into the closed system that was the initial egg.
This leads to an important consequence of the second law: the arrow of time. A process that generates entropy — such as your egg whisking — will be irreversible unless you input more energy. It's why an omelet won't reform back into an egg or why billiard balls don't spontaneously reform a triangle after the break. Like an arrow released, the entropy moves in a single direction, and we witness the effect as time.
We are trapped by the second law of thermodynamics, but the international team of scientists wanted to see if the second law could be violated in the quantum realm. Since such a test is impossible in nature, they used the next best thing: an IBM quantum computer.
Traditional computers, like the one you are reading this on, use a basic unit of information called a bit. Any bit can be represented as either a 1 or a 0. A quantum computer, however, uses a basic unit of information called a qubit. A qubit exists as both a 1 and a 0 simultaneously, allowing the system to compute and process information much faster.
In their experiment, the researchers substituted these qubits for subatomic particles and put them through a four-step process. First, they arranged the qubits in a known and ordered state and entangled them — meaning anything that happened to one affected the others. Then they launched an evolution program on the quantum computer, which used microwave radio pulses to break down that initial order into a more complex state.
Third step: a special algorithm modifies the quantum computer allow disorder to more to order. The qubits are again hit with a microwave pulse, but this time they rewind to their past, orderly selves. In other words, they are de-aged by about one millionth of a second.
According to study author Valerii M. Vinokur, of the Argonne National Laboratory, this is the equivalent of pushing against the ripples of a pond to return them to their source.
Since quantum mechanics is about probability (not certainty), success was no guarantee. However, in a two-qubit quantum computer, the algorithm managed a time jump an impressive 85 percent of the time. When it was upped to three qubits, the success rate dropped to about 50 percent, which the authors attributed to imperfections in current quantum computers.
The researchers published their results recently in Scientific Reports.
Bringing order from chaos
The results are fascinating and spur the imagination, but don't start investing in flux capacitors yet. This experiment also shows us that sending even a simulated particle back in time requires serious outside manipulation. To create such an external force to manipulate even one physical particle's quantum waves is well beyond our abilities.
"We demonstrate that time-reversing even ONE quantum particle is an unsurmountable task for nature alone," study author Vinokur wrote to the New York Times in an email [emphasis original]. "The system comprising two particles is even more irreversible, let alone the eggs — comprising billions of particles — we break to prepare an omelet."
A press release from the Department of Energy notes that for the "timeline required for [an external force] to spontaneously appear and properly manipulate the quantum waves" to appear in nature and unscramble an egg "would extend longer than that of the universe itself." In other words, this technology remains bound to quantum computation. Subatomic spas that literally turn back the clock aren't happening.
But the research isn't solely a high-tech thought experiment. While it will not help us develop real-world time machines, the algorithm does have the potential to improve cutting-edge quantum computation.
"Our algorithm could be updated and used to test programs written for quantum computers and eliminate noise and errors," study author Andrey Lebedev said in a release.
Is non-simulated time travel possible?
As Kurt Gödel proved, Einstein's equations don't forbid the concept of time travel, but they do set an improbably high hurdle to clear.
Writing for Big Think, Michio Kaku points out that these equations allow for all sorts of time travel shenanigans. Gödel found that if the universe rotated and someone traveled fast enough around it, they could arrive to a point before they left. Time travel could also be possible if you traveled around two colliding cosmic strings, traveled through a spinning black hole, or stretched space via negative matter.
While all of these are mathematically sound, Kaku points out that they can't be realized using known physical mechanisms. Similarly, the ability to nudge physical particles back in time remains beyond our reach. Time travel remains science fiction for all intents and purposes.
But time travel may one day become an everyday occurrence in our computers, making us all time lords (in a narrow sense).
- Time Travel Simulation Resolves “Grandfather Paradox” - Scientific ... ›
- Time Travel Is Possible: Scientists Have Already Built A Time ... ›
What can 3D printing do for medicine? The "sky is the limit," says Northwell Health researcher Dr. Todd Goldstein.
- Medical professionals are currently using 3D printers to create prosthetics and patient-specific organ models that doctors can use to prepare for surgery.
- Eventually, scientists hope to print patient-specific organs that can be transplanted safely into the human body.
- Northwell Health, New York State's largest health care provider, is pioneering 3D printing in medicine in three key ways.
Beyond Beef sizzles and marbleizes just like real beef, Beyond Meat says.
- Shares of Beyond Meat opened at around $200 on Tuesday morning, falling to nearly $170 by the afternoon.
- Wall Street analysts remain wary of the stock, which has been on a massive hot streak since its IPO in May.
- Beyond Meat faces competition from Impossible Foods and, as of this week, Tyson.
The most valuable college majors will prepare students for a world right out a science fiction novel.
- The future of work is going to require a range of skills learned that take into account cutting edge advancements in technology and science.
- The most valuable college majors in the future will prepare students for new economies and areas of commerce.
- Mathematics, engineering and science related educational majors will become an ubiqitous feature of the new job market.
A recent study used data from the Big Five personality to estimate psychopathy prevalence in the 48 contiguous states and Washington, D.C.
- The study estimated psychopathy prevalence by looking at the prevalence of certain traits in the Big Five model of personality.
- The District of Columbia had the highest prevalence of psychopathy, compared to other areas.
- The authors cautioned that their measurements were indirect, and that psychopathy in general is difficult to define precisely.
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How Will Quantum Computing Change The World?
Firstly, I would like to explain what the concept of ‘Quantum Computing’ is.
What is quantum computing?
Quantum computing takes advantage of the ability of subatomic particles to exist in more than one state at any time. Due to the way the tiniest of particles behave, operations can be done much more quickly and use less energy than classical computers.
In classical computing, a bit is a single piece of information that can exist in two states 1 or 0. Quantum computing uses quantum bits. These are quantum systems with two states. However, unlike a usual bit, they can store much more information than just 1 or 0, because they can exist in any superposition of these values.
Quantum computing is computing using quantum-mechanical phenomena, such as superposition and entanglement.
Now let’s break these two concepts down:
Now what exactly is superposition?
Superposition is the ability of a quantum system to be in multiple states at the same time until it is measured.
Because the concept is difficult to understand, this essential principle of quantum mechanics is often illustrated by an experiment carried out in 1801 by the English physicist, Thomas Young. Young’s double-slit experiment was intended to prove that light consists of waves. Today, the experiment is used to help people understand the way that electrons can act like waves and create interference patterns.
For this experiment, a beam of light is aimed at a barrier with two vertical slits. The light passes through the slits and the resulting pattern is recorded on a photographic plate. When one slit is covered, the pattern is what would be expected: a single line of light, aligned with whichever slit is open.
Intuitively, one would expect that if both slits are open, the pattern of light will reflect two lines of light aligned with the slits. In fact, what happens is that the photographic plate separates into multiple lines of lightness and darkness in varying degrees.
What is being illustrated by this result is that interference is taking place between the waves going through the slits, in what, seemingly, should be two non-crossing trajectories. Each photon not only goes through both slits; it simultaneously takes every possible trajectory on route to the photographic plate.
Quantum entanglement is a physical phenomenon which occurs when pairs or groups of particles are generated or interact in ways such that the quantum state of each particle cannot be described independently of the state of the other(s), even when the particles are separated by a large distance—instead, a quantum state must be described for the system as a whole.
Now that I have explained these two phenomena, I can now show how quantum computing will change the world.
Firstly, we need to clear a misconception about quantum computing, the misconception is that we have become so accustomed to advances in computing being reflected in slimmer, faster laptops and bigger memories that quantum computing is often envisaged in the same terms. It shouldn’t be.
Digital computers manipulate information encoded in binary form as sequences of ones and zeros the rest is software, whether that involves converting keystrokes or mouse movements into images, or taking numbers and feeding them into an equation to work out the answer.
Quantum computers are no different, except in one crucial respect. In a conventional computer, one bit of binary data can have one of just two values: one or zero. But in a quantum computer, these switches, called quantum bits have more options, because they are governed by the laws of quantum theory.
Thanks to superposition, qubits can, in effect, encode one and zero at the same time. As a result, quantum computers can represent many more possible states of binary ones and zeros. A classical bit can represent two states: zero and one. Add a bit to your computer’s processor and you can encode one more piece of binary information. Yet if a group of qubits are placed in a joint superposition, called an entangled state, each additional qubit doubles the encoding capacity. By the time you get to 300 qubits – as opposed to the billions of classical bits in the dense ranks of transistors in your laptop’s microprocessors – you have 2 options. That’s more than the number of atoms in the known universe.
Quantum computers have largely been advertised on the promise that they will be vastly faster at crunching through calculations than even the most powerful of today’s supercomputers. This speed-up – immensely attractive to scientists and analysts solving complex equations or handling massive data sets – was made explicit in 1994 when the American mathematician Peter Shor showed in theory that a computer juggling coherent qubits would be able to factor large numbers much more efficiently than classical computers. Reducing numbers to their simplest factors – decomposing 12 to “two times two times three”, for example – is an exercise in elementary arithmetic, yet it becomes extremely hard for large numbers because there’s no shortcut to trying out all the possible factors in turn. Factorising a 300-digit number would take current supercomputers hundreds of thousands of years, working flat out.
For this reason, a lot of data encryption – such as when your credit card details are sent to verify an online purchase – uses codes based on factors of large numbers, which no known computer can crack. Yet Shor showed that a quantum factorisation algorithm could find factors much more efficiently than a classical one can. As well as factorisation, quantum computation should be able to speed up database searches – and there’s no question how useful that would be, for example in combing through the masses of data generated in biomedical research on genomes.
One of the likely first big applications of quantum computing isn’t going to set the world of personal computing alight, but it could transform an important area of basic science. Computers operating with quantum rules were first proposed in 1982 by the American physicist Richard Feynman. He wasn’t concerned with speeding up computers, but with improving scientists’ ability to predict how atoms, molecules and materials behave using computer simulations. Atoms observe quantum rules, but classical computers can only approximate these in cumbersome ways: predicting the properties of a large drug molecule accurately, for example, requires a state-of-the-art supercomputer.
Quantum computers could hugely reduce the time and cost of these calculations. In September, researchers at IBM used the company’s prototype quantum computer to simulate a small molecule called beryllium dihydride. A classical computer could, it’s true, do that job without much trouble – but the quantum computer doing it had just six qubits. With 50 or so qubits, these devices would already be able to do things beyond the means of classical computers.
To conclude, I this essay I have outlined what a quantum computer is and how it can change the world. The last brief point I would like to address is that though a quantum computer is on the way there are many questions on how long it could take.
another big difficulty is dealing with errors. Given the difficulty of keeping qubits coherent and stable, these seem inevitable: qubits are sure to flip accidently now and again, such as a one changing to a zero or getting randomised. Dealing with errors in classical computers is straightforward: you just keep several copies of the same data, so that faulty bits show up as the odd one out. But this approach won’t work for quantum computing, because it’s a fundamental and deep property of quantum mechanics that making copies of unknown quantum states (such as the states of qubits over the course of a computation) is impossible. Developing methods for handling quantum errors has kept an army of researchers busy over the past two decades. It can be done, but a single error-resistant qubit will need to be made from many individual physical qubits, placing even more demands on the engineering.
You can only access the opportunities that the quantum computer holds, if all the qubits are mutually dependent: in a collective or “coherent” state, which, crudely speaking, means that if we do something to one of them (say, flip a one to a zero), all the others “feel” it. Generally, this requires all the qubits to be placed and maintained in an entangled state.
The difficulty of making a quantum computer mostly involves making and sustaining these consistent states of many qubits. Quantum effects such as superposition and entanglement are delicate and easily disrupted. The jangling atomic motions caused by heat can wash them away. So, to be consistently entangled, qubits must be cooled to extremely low temperatures – we’re typically talking less than a degree above absolute zero (-273° C) – and kept well isolated from the laboratory environment: that is, from the very equipment used to manipulate and measure them. That’s partly why the IBM quantum computer I saw is so bulky: much of it consists of cooling equipment and insulation from the lab environment.


Author: Sarah Kearns
Editors: David Mertz, Zuleirys Santana Rodriguez, and Scott Barolo
In a previous post, we discussed how proteins fold into unique shapes that allow them to perform their biological functions. Through many physical and chemical properties, like hydrogen bonding and hydrophobicity, proteins are able to fold correctly. However, proteins can fold improperly, and sometimes these malformed peptides aggregate, leading to diseases like Alzheimer’s.
How can we figure out when the folding process goes wrong? Can we use computers to figure out the folding/misfolding process and develop methods to prevent or undo the damage done by protein aggregates?
In the late 1960s, a scientist named Cyrus Levinthal noted that protein folding is different from regular chemical reactions. Chemical reactions proceed from a reactant to a product via a set pathway of structures and intermediates. Proteins do not do this because a protein doesn’t find just one intermediate shape as it folds — it can potentially find millions. Levinthal concluded that a new protein, moving through so many intermediate structures, must take an enormously long time to find its final native state.
To understand the vast number of conformational possibilities, let’s take a polypeptide of 101 amino acids. There will be a total of 100 bonds connecting amino acids, each bond having six possible conformations (see Figure 1). This means that a protein of 101 amino acids has 3100, or 5*1047, configurations—and some proteins are five or ten times longer!
Even if our 101-amino acid protein were able to sample 1013 conformations per second, it would still need 1027 years to try all possible shapes. However, in reality, it takes seconds, not eons, for a protein to find its native conformation. This leads to a big question: Can humans predict how proteins will fold? Even with the help of computers, which can test each possible shape in microseconds, testing them all would require 30 years of computation just for one protein.
Simplifying Structure Prediction
Protein structures, such as hydrogen and ionic bonding and hydrophobic interactions, are difficult to predict rationally just based on the amino acid sequence. Instead, a database of protein structures found by x-ray crystallography, called the Protein Data Bank, has been more helpful in determining the rules of protein folding. Still, determining protein structures accurately is difficult and time-consuming. Some computational shortcuts have made the process simpler, but the predicted folds still are not exact.
The biggest simplifications are made by assuming a lattice structure or using a coarse-grained representation. The former takes a globular protein that typically has variable bond lengths between each amino acid into a lattice (has uniform bond lengths) and places each residue into a 3D grid structure thus limiting the number of possibilities the possible placements of each amino acid. A coarse-grained model would simplify a protein structure by representing amino acids as a single point (see Figure 2).
So far, computational prediction of protein structures is limited to these simpler models because more realistic all-atom energy diagrams are too complex and computationally heavy. In our protein of 101 amino acids, there are close to 2000 atoms to move around in 3100 configurations. With the advent of quantum computing, such problems are becoming easier to solve, but for now, they still use coarse-grained representations.
How Your PC Can Help Mine Data
Some researchers have turned such computational problems into citizen science projects. Perhaps the most famous of these is FoldIt, developed by the Center for Game Science and the Department of Biochemistry at the University of Washington. Foldit is an online game where players compete to create accurate protein structures by moving around the backbone chain, amino acid residues, and domains. Players score points by packing the protein, hiding hydrophobic residues, and clearing any clashes between side chains to minimize the energy of the overall structure. The lowest-energy conformations from the game are then collected and analyzed to improve real-life folding algorithms.
A less hands-on folding program is Folding@home from Stanford University, which borrows unused processors on your personal computer to work on a folding algorithm. While users check their emails or listen to music, or even when the screensaver runs, their computers solve structures and compute minimization functions.
All this data has gone towards the goal of figuring out both how malformed proteins aggregate and how to design drugs that will prevent misfolding. FoldIt has already produced a retrovirus structure that is being used to determine inhibitors of HIV. One of the labs behind FoldIt has been focusing on proteins involved in cancer, AIDS, and other diseases. The Folding@home project has produced about 130 peer-reviewed papers describing its accomplishments in simulating, not only protein folding but also molecular dynamics, which help determine the ability for drugs to bind.
Having an idea of what the protein does and where it does it, without having to use expensive machines to do crystallography (to get the structure of a protein) or high-throughput screening (to find the substrates of a protein), saves both time and resources when developing a drug. More work has to be done before computational predictions perfectly line up with crystal structures. But when that day comes, we will be much closer to understanding how proteins work, and how to cure diseases of protein folding and function.
About the author
Read all posts by Sarah here.
Figure 1: Sarah Kearns
Figure 2: Sarah Kearns


Quantum computers are often seen in Sci-Fi movies like I, Robot and Eagle Eye as computers with capabilities beyond those of present day computers. The computer named Becky in Eagle Eye was especially memorable, attempting to kill government officials including the president by hacking into high security systems.
How were quantum computers able to express feelings and hack into everything in these movies? Today, I’d like to introduce you to the world of quantum computers.
Definition and Mechanism of Quantum Computers
Quantum computers are computers that process data using the principles of quantum mechanics such as entanglement and superposition. They are also called ‘future computers’ that surpass super computers by using quantum mechanics as their computing element.
It was Richard Feynman, a theoretical physicist in the US, who discovered the potential and necessity of quantum computers in 1982, and David Deutsch from Oxford University defined it in more specifics. Let’s take a look at the mechanism of quantum mechanics utilized in quantum computers.
Quantum computers make use of two basic phenomena: quantum superposition and entanglement. Quantum superposition is when electrons are held in an arbitrary state until protons are measured, and quantum entanglement describes an entire group of protons entering a fixed state when one of the entangled protons is observed.
These physical phenomena may be quite complicated to grasp. Let’s think about the cat experiment from Erwin Schrodinger, an Austrian physicist, famously known as Schrodinger’s cat.
Let’s say there’s a sealed box with potassium cyanide and a cat in it. Until we open the box and take a look inside, we wouldn’t know if the cat is dead or alive. This means that until the moment we open the box we cannot know whether the cat is dead or not, and that the situation is fixed simultaneously when the observation is made.
Based on these two phenomena, quantum computers use qubit, a unit at a quantum state, for calculations. Unlike in digital computers that use bits, in two states of 0 and 1, quantum computers use qubit with four states: 00, 01, 10, and 11. A qubit can be 0 or 1, and 0 and 1 can both exist as well.
Therefore, quantum computing means it’s processing in a state where the decision of whether something is at 0 or 1 can’t be made. The computing speed goes up in the form of , and if there are 512 qubits the speed is going to be as fast as .
D-Wave, the First Commercial Quantum Computer
Quantum computer D-Wave
In 2011, D-Wave System launched a 128 qubit quantum computer D-Wave 1 as the first commercial quantum computer, and 512 qubit D-Wave 2 in 2013. Its price was set at $10 million and Lockheed Martin, Google, and NASA were some of the first buyers of their new product.
Because D-Wave uses a superconductor made of niobium, it has to be used at a temperature of absolute zero (-273℃). The photo above makes it look like it’s a giant computer, but the actual computer inside is as small as a human fist. The outer part of the computer is made of a liquid helium cooling system which keeps it at the absolute zero degree and also lowers noise.
Unlike the quantum computer defined by David Deutsch, it works based on quantum annealing phenomenon to solve optimization (NP-complete) problems. Quantum annealing here means the process of electrons finding the most stable state called the ground state while the risen temperature of the object is dropping.
Pros and Cons of D-Wave
According to Google’s D-Wave benchmark in January of 2014, quantum computers show much higher speed in solving optimization problems compared to general PCs. Although there are reports that say its speed is sometimes slower than PCs, they seem to be faster than PCs, on average, to solve optimization problems involving data with regularity.
D-WAVE has three big cons.
First, despite being called the world’s first commercial quantum computer, it’s a shame that D-Wave is not considered a real quantum computer at the same time. This is because it’s designed to have an external computer read the processing results from the quantum CPU. Some may refer to it as just a “half-quantum” computer which consists of the regular workstation with a qubit CPU on the side.
Second, the CPU generates heat while operating, and the noise made while running the cooler to lower the temperature can create computing errors. The size of the computer is also quite large due to the big cooling unit on top to stabilize the temperature of absolute zero.
Finally, D-Wave is made based on the tunnel effect of quantum annealing unlike the formerly defined quantum computers. The tunnel effect here means the phenomenon where a particle stochastically tunnels through the energy barrier higher than its own potential energy. As a result, its computing speed is not overwhelmingly faster than in existing computers except for in particular calculating operations.
Prospects of Quantum Computers
Quantum computers still don’t have a lot of algorithms that solve general problems, so it only uses certain algorithms for specific operations such as prime factorization or Fourier transform.
Once the technology is found that can maintain algorithms and qubits to solve these problems and find a room temperature superconductor through which electric resistance gets close to zero at room temperature, quantum computers will become popular enough for us to see them in our daily lives.
There are new materials and products utilizing quantum mechanics these days, including TVs using quantum dots. When this kind of quantum communication is used in our lives, tapping and data interception will become impossible. I think the day when we’ll see these technologies go beyond our imagination and appear in our real lives is not so far away.
I look forward to seeing more quantum computers in the future.
Written by Deok Lee, University student reporter for LG CNS
 http://navercast.naver.com/contents.nhn?rid=122&contents_id=31579 [back to the article]
 D-Wave experiment report (https://plus.google.com/+QuantumAILab/posts/DymNo8DzAYi) [back to the article]
 http://www.dongascience.com/sctech/view/720 [back to the article]


The ability to produce arbitrarily superposed quantum states is a prerequisite for creating a workable quantum computer. Such highly complex states can now be generated on demand in superconducting electronic circuitry.
As an innocent reductionist in elementary school, I dreamed of creating everything in the world by assembling atoms one by one, just like building with Lego blocks. Decades later, the dream has, to some extent, come true with the 'bottom-up' approach of nanotechnology in which, for example, single atoms can be manipulated and assembled using the tip of a scanning probe microscope. But physicists are now playing with even fancier — and often more fragile — 'quantum Lego blocks'. Using a bottom-up approach, Hofheinz et al.1 (page 546 of this issue) report on-demand synthesis of arbitrary quantum states in a superconducting resonator circuit. Starting from a vacuum (zero-photon) state, the authors pile up photons one by one in the resonator and create complex quantum states in an entirely deterministic way.
Quantum mechanics was founded and, to a great extent, developed during the last century. Despite its weird and counterintuitive predictions, such as the uncertainty principle and the superposition and entanglement of states, it has stood up to a number of tests, and has proved itself to be a rigorous foundation across a broad spectrum of physics fields, from particle physics to solid-state physics. But only relatively recently have people recognized that the paradoxical nature of quantum mechanics is in itself useful in many applications, such as quantum cryptography and quantum computation. This recognition has boosted research on technologies of quantum-state engineering in various types of physical setting, and the twenty-first century will hopefully be memorable for the implementation of such technologies.
Among physical systems currently being investigated, superconducting (zero-resistance) macroscopic circuits stand in a unique position. Although the naive expectation is that quantum mechanics is normally associated with single microscopic systems such as atoms, nuclei and electrons, it has been shown that quantum-mechanical behaviour can be observed and controlled in human-designed, superconducting circuits that are micrometres or even millimetres in size2.
The simplest example of a superconducting quantum circuit is a linear resonator consisting of an inductor and a capacitor. If proper parameters are chosen, such a circuit can store a number of energy quanta (photons) at a microwave frequency. Another example is a quantum bit (or qubit), which is an effective two-state system. It can be implemented using a Josephson junction — a tunnel junction between two superconductors — as a nonlinear inductor; the two states are the ground and the first excited state of the nonlinear circuit. Coherent control of quantum states in such circuits and their combinations3 is the basis of superconducting quantum-state engineering.
To synthesize quantum states in a resonator, Hofheinz et al.1 use a circuit (see Fig. 1a on page 546) in which a resonator is coupled to a qubit. Because it is not possible to create arbitrary quantum states in resonator circuits using classical control signals alone, the qubit is used as a 'forklift' to load photons one by one into the resonator. Each cycle consists of two sequential steps. First, the qubit, initially detuned off-resonant with the resonator, is excited by a microwave pulse. Then, the qubit energy level is tuned into resonance with the resonator, enabling coherent transfer of energy quanta. A similar technique was proposed for an optical cavity with an atom inside4, and was demonstrated for the motional states of an ion in a trap5. Hofheinz and colleagues have also previously reported6 generation of states with a certain number of photons (N-photon or Fock states) — with up to 15 photons7 — based on the same scheme. In their new study1, they perfect the scheme to precisely control not only the amplitude but also the phase of each quantum loading. This allows them to synthesize, in a completely deterministic manner, quantum states that are the largest-ever arbitrary superpositions of multiple N-photon states.
Another distinctive aspect of Hofheinz and colleagues' experiment is the quantitative characterization and visualization (Fig. 1) of the generated quantum states, which they attained using Wigner tomography. This method fully characterizes, by means of the Wigner function, the resonator's quantum state: just as tomography is used in medical diagnoses such as magnetic-resonance and X-ray imaging, Wigner tomography allows the quantum state to be completely reconstructed from a large number of measurements. In this case, such measurements were taken by using the same qubit, now as a diagnostic probe, to unload energy quanta from the resonator. In the past year, an analogous technique was used to characterize the quantum state of a microwave field in a three-dimensional cavity, using atoms passing through the cavity as probes of the radiation field8.
Comparison1 of the observed and simulated Wigner functions (Fig. 1) clearly indicates that the target quantum states were synthesized with high fidelity. The ability to accurately create and control superposed quantum states is the first requisite for quantum computing. Moreover, coupling between qubits and resonators, such as that achieved in this study, has already shown its value in the implementation of quantum gates — the analogues of logic gates in conventional computers — between remote qubits9,10.
That said, the complexity and accuracy of the quantum states achieved by the authors is limited by decoherence — that is, the vulnerability of the quantum superposition. In superconducting circuits, quantum coherence tends to be lost more quickly than in atoms. This is not surprising if one considers the macroscopic nature of the circuits, which makes them interact more strongly with their surroundings.
Efforts to achieve longer coherence times are ongoing, and include improving circuit design and reducing the number of defects in the materials from which circuit components are made. Studying the decay of coherence in a variety of quantum states will be a valuable approach to understanding what mechanism triggers decoherence itself and the crossover from quantum to classical behaviour7,8. For now, Hofheinz and colleagues' experiment has set the stage for further developments in quantum-state engineering in superconducting electronic circuitry, and has brought physicists a step closer to realizing a workable quantum computer.


Have you ever tried to match wits with a computer? Perhaps you’ve tried playing it in a game of chess or raced to perform a calculation before your laptop could spit out the correct answer. You probably lost the chess game and the computer definitely beat you in the mathematics race. Given that, when you measure the ability of the human brain vs. a computer at face value, it seems like a computer would be faster and smarter, but there is actually far more to the story.
If you had posed this same question a few decades ago, there would be no question… the human brain could run circles around computers, but is that still true? Has technology begun to catch up to the most remarkable and awe-inspiring organ in the human body?
Old Ideas Aren’t Always the Best
Since the inception of the first computers, there has been a direct comparison between these “computational machines” and the human brain. One of the common phrases that has stuck around for decades, and which encourages the idea of a brain vs. computer argument, is “brains are analogue, computers are digital”. This makes it seem like computers are superior, but in truth, the human brain is far more advanced and efficient, and possesses more raw computational power than the most impressive supercomputers that have ever been built.
At the time of this writing, the fastest supercomputer in the world is the Tianhe-2 in Guangzhou, China, and has a maximum processing speed of 54.902 petaFLOPS. A petaFLOP is a quadrillion (one thousand trillion) floating point calculations per second. That’s a huge amount of calculations, and yet, that doesn’t even come close to the processing speed of the human brain.
In contrast, our miraculous brains operate on the next order higher. Although it is impossible to precisely calculate, it is postulated that the human brain operates at 1 exaFLOP, which is equivalent to a billion billion calculations per second.
In 2014, some clever researchers in Japan tried to match the processing power in one second from one percent of the brain. That doesn’t sound like very much, and yet it took the 4th fastest supercomputer in the world (the K Computer) 40 minutes to crunch the calculations for a single second of brain activity!
Brains Are Very Different From Computers
When we discuss computers, we are referring to meticulously designed machines that are based on logic, reproducibility, predictability, and math. The human brain, on the other hand, is a tangled, seemingly random mess of neurons that do not behave in a predictable manner.
Biology is a beautiful thing, and life itself is much smarter than computers. For example, the brain is both hardware and software, whereas there is an inherent different in computers. The same interconnected areas, linked by billions of neurons and perhaps trillions of glial cells, can perceive, interpret, store, analyze, and redistribute at the same time. Computers, by their very definition and fundamental design, have some parts for processing and others for memory; the brain doesn’t make that separation, which makes it hugely efficient.
The same calculations and processes that might take a computer a few millions steps can be achieved by a few hundred neuron transmissions, requiring far less energy and performing at a far greater efficiency. The amount of energy required to power computations by the world’s fastest supercomputer would be enough to power a building; the human brain achieves the same processing speeds from the same energy as is required to charge a dim lightbulb. Biological processes have had billions of years to evolve perfect, efficient organs that far supersede technology, and we are beginning to reach those artificial “limitations”.
One of the things that truly sets brains apart, aside from their clear advantage in raw computing power, is the flexibility that it displays. Essentially, the human brain can rewire itself, a feat more formally known as neuroplasticity. Neurons are able to disconnect and reconnect with others, and even change in their basic features, something that a carefully constructed computer cannot do.
We see this amazing transformative feat in a wide variety of brain functions, such as the formations of memory, knowledge acquisition, physical development, and even recovery from brain damage. When the brain identifies a more efficient or effective way to compute and function, it can morph and alter its physical and neuronal structure, hence the term “plasticity“. Until we achieve true Artificial Intelligence (in which computers should theoretically be able to re-wire themselves), neuroplasticity will always keep the human brain at least one step ahead of “static” supercomputers.
Looking Towards the Future
If there is one thing about human beings, it’s that we don’t like being told something is impossible. Therefore, now that we have a clear goal that is nearly in sight (a computer that operates at the exaFLOP level), we have begun to pay more attention (and spend more money) towards achieving it.
For example, the Human Brain Project has the ultimate goal of reaching exascale computing (computing at the same processing power and speed as the human brain; an artificial brain, so to speak). Launched in 2013, the Human Brain Project has already sourced billions of euros for this project, which could have hugely important ramifications in many different industries.
The fastest supercomputers created thus far (like the one seen above) haven’t even breached the 50 petFLOP mark, which is still 20 times slower than the human brain’s processing speed, not to mention…they’re massive!
Experts believe that exascale computing could be possible by 2020, but Intel, one of the largest technology companies in the world, boasted that they will have achieved that capability by 2018. By creating a legitimate artificial brain modeling, we will be able to explore real-time simulations of human brain activity – a major breakthrough.
Furthermore, major interests ranging from engineering and basic research to national security agencies and telecommunication giants are eager to see what this dreamt-of level of technological advancement will bring.
However, as we explained above, there are some serious issues with reaching this level of technical sophistication, namely energy, memory, and physical constraints. Even with new advancements in graphene transistors and the complex possibilities of quantum computing, a purely artificial brain on par with the real thing seems out of reach – for now.
The recent stall in any new supercomputers at the top of the “Fastest List” has made some people question the possibilities, but these new advancements may pay off in a major way, which would launch us into a new generation. If and when that happens, the answer to “who would win, the human brain or a supercomputer” might be different!


In 1965, Intel co-founder Gordon Moore published a remarkably prescient paper which observed that the number of transistors on an integrated circuit was doubling every two years and predicted that this pace would lead to computers becoming embedded in homes, cars and communication systems.
That simple idea, known today as Moore’s Law, has helped power the digital revolution. As computing performance has become exponentially cheaper and more robust, we have been able to do a lot more with it. Even a basic smartphone today is more powerful than the supercomputers of past generations.
Yet the law has been fraying for years and experts predict that it will soon reach its limits. However, I spoke to Bernie Meyerson, IBM’s Chief Innovation Officer, and he feels strongly that the end of Moore’s Law doesn’t mean the end of progress. Not by a long shot. What we’ll see though is a shift in emphasis from the microchip to the system as a whole.
Going Beyond Silicon
The end of Moore’s Law is not a new issue. In fact, Meyerson argues that it first began unraveling in 2003, when insulating components within transistors began failing due to quantum mechanical effects. Since then, chip manufacturers have been finding new materials that are more resistant to decay in their basic atomic properties and progress has continued.
However, sometime around 2020, these workarounds will no longer suffice as the silicon itself yields to quantum mechanical reality. Some researchers, including at IBM, are pursuing strategies like carbon nanotubes and silicon photonics that have the potential to increase chip speeds even without having to shrink chips to quantum scale.
Other approaches, such as quantum computing and neuromorphic chips, change the nature of computing itself and can be exponentially more efficient for certain tasks, such as pattern recognition in the case of neuromorphic chips and encryption in the case of quantum computers. Still, you wouldn’t want either of these running your word processor.
As Meyerson put it, “Quite frankly, for general purpose computing all that stuff isn’t very helpful and we’ll never develop it in time to make an impact beyond specialized applications over the next 5 or 10 years. For the practical future, we need to change our focus from chip performance to how systems perform as a whole by pursuing both hardware and software strategies.”
Integrating the Integrated Circuit
One way of increasing performance is by decreasing distance at the level of the system. Currently, chips are designed in two dimensions to perform specific functions, such as logic chips, memory chips and networking chips. Although none of them can do much by themselves, acting in concert they allow us to do extremely complex tasks on basic devices.
So one approach to increasing performance, called 3D stacking, would simply integrate those integrated circuits into a single three-dimensional chip. This is harder than it sounds, because entirely new chip designs have to be devised, but it would vastly reduce the time circuits need to wait for instructions from each other and increase speed significantly while decreasing power dramatically due to far shorter communication paths.
In truth, this is not a new strategy but rather one that was deployed in the 1960’s to overcome a challenge called the tyranny of numbers. Simply put, the physical requirements of wiring thousands of transistors together was putting practical limitations on what could be designed and built. That’s what led to the invention of integrated circuits in the first place.
Meyerson says, “when we moved from transistors to integrated circuits, we shrunk an entire rack measuring about 40 cubic feet down to a single board measuring 19 x 26 inches. 3D stacking will shrink that board down to less than a square inch and we can potentially get an increase in power performance of at least 10-100 fold.
Building Intelligently Agile Systems
In the 1980’s, chip manufacturers began building specialized types of chips, called ASICs, that were highly optimized for specific tasks, such as running complex financial models. These would significantly outperform conventional chips for those specific tasks, but ultimately, the process of hardwiring proved too expensive and unwieldy to be a viable strategy.
Yet Meyerson sees vastly more potential in a newer approach called FPGA, that can be re-purposed on the fly through software. He points to Intel’s recent purchase of Altera as a strong indication that things are moving in that direction. It is well known that in specific applications FPGA’s can produce gains of ten-fold or more in computing performance, but most importantly, that system level gain is not restricted to a single application.
The FPGA approach is a major improvement because rather than going through a roughly 18-month process to design and manufacture a specialized chip, the same thing can be done in a matter of weeks. However, Meyerson thinks the potential may actually be far greater than that if we can build intelligent software that can reprogram the chips autonomically.
“So for example,” Meyerson says,” while you’re writing a document, your laptop would be configured to do exactly that, but if you then needed to run a simulation of some financial data for that same report, your system would re-optimize itself for deep computations required. Such “intelligent” architectures and the enabling software are the next grand challenge in IT.”
“Take this idea a little further,” he continues “and you can see how new technologies like neuromorphic chips and quantum computing can deliver an enormous impact even as specialized systems in the cloud. Imagine being able to access the capabilities of a neuromorphic system for photo recognition and search while shopping, and then instantly switch to a quantum computer to facilitate the transaction with unbreakable encryption.”
The Future of Technology is all too human
Back in 1965, when Gordon Moore formulated his famous law, computers were enormous hunks that few people ever saw. After 20 years of continuous doubling, we got personal computers small enough to fit under our desks, but powerful enough to generate a graphical display and interact with us through a keyboard and a mouse. 20 more years gave us the mobile revolution.
The future of technology is always more human and Meyerson expects that, ”by 2020, we’ll still be improving system performance exponentially, but we’ll have to change our conception of information technology once again, this time from machines that store, analyze and retrieve information to systems that are active partners in a very natural human/machine collaboration.”
“The cognitive era will be ultimate bridge across the digital divide,” he notes, “spanning barriers of not only technology but that of language, education and skill level as well. IT will essentially become so advanced that it disappears along with previous institutional barriers. Even a teenager will have access to resources that only the most well-equipped research facilities have today and they will be able to access it in real time.”
But perhaps the most important consequence of Meyerson’s vision of cognitive computing is not how it will change how we work with computers, but with each other. Before the industrial era, people were valued for their ability to do physical work. In the knowledge economy, those with strong cognitive skills were considered “the best and the brightest.” Now, we will likely see a new shift in value.
In the future, when machines can do cognitive tasks more effectively than any human, we will likely find that competitive advantage will go to those who can collaborate effectively, with both people and machines. So the key to the future lies not so much in chips and algorithms as it does within ourselves.
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Greg Satell is a popular speaker and consultant. His first book, Mapping Innovation: A Playbook for Navigating a Disruptive Age, is coming out in 2017. Follow his blog at Digital Tonto or on Twitter @Digital Tonto.


A tiny cube floating and flipping in midair sounds like something straight out of "Harry Potter," but Harvard physicist Subir Sachdev doesn't need magic to levitate objects.
Sachdev performed a levitation demonstration using a magnet and a superconductor during a presentation at the Perimeter Institute on Oct. 1. Superconductors are incredible materials that can conduct electricity with zero resistance. But to generate the superconductivity, the material has to be extremely cold, and so Sachdev poured liquid nitrogen that's about minus 320 degrees Fahrenheit (minus 195 degrees Celsius) on the superconductor to trigger its superconductive state.
"One of the key properties of superconductors is that it hates magnetic fields," Sachdev said during his levitation demonstration. And so as the superconductor "repels" the magnet, the magnetic cube is lifted into the air. The magnet will fall after the superconductor begins to warm up again.
But superconductors aren't just for levitation demonstrations, Sachdev said. [The Cool Physics of 7 Classic Toys]
"The hope is that these materials will actually be useful for something," Sachdev said.
Superconducting materials have to be extremely cold to maintain their superconductive state, and physicists are searching for materials that could serve as high-temperature superconductors.
High-temperature superconductors could have a wide variety of applications, including in MRI machines, motors, generators, fusion reactors and low-loss power cables.
Quantum mechanics 101
Physicists are still not entirely sure what gives a superconductor its magiclike properties and why superconductivity doesn't work above a certain temperature, but Sachdev said he thinks he's pretty close to the answer.
But to understand how a superconductor works, "you need to know some quantum mechanics basics," Sachdev said after his levitation demonstration. The main idea of quantum mechanics is that an object like an electron or a photon behaves as both a particle and a wave, Sachdev said.
"That's one of the key mysterious properties of quantum mechanics," Sachdev said.
The other weird characteristic of quantum particles is that they can exist in multiple places at once, a phenomenon called superposition. But superposition is a fragile state. The moment that scientists try to measure the particles, the superposition state collapses and the particles come to exist in only one spot. Before the particles are disturbed, they exist in multiple places all at once, and "yeah, you just have to accept it," Sachdev joked during his presentation.
Quantum entanglement is superposition on a larger scale, something that Sachdev described during his talk. Particles become entangled when they interact with each other. Entanglement means that when an action is performed on one particle, it directly affects that particle's entangled partner no matter how far apart they are. [How Quantum Entanglement Works (Infographic)]
Sachdev said a good way to think about this is to imagine how two entangled electrons rotate. Electrons either rotate clockwise (an "up" spin) or counterclockwise (a "down" spin).
"Is the left electron up or down?" Sachdev asked the audience. "The answer is really both." And this is true for both electrons.
The electrons will stay in this superposition state until someone measures one of the two particles. If one electron has an up spin upon being measured, its entangled partner instantaneously acquires a down spin. This is true no matter how far apart the electrons are, even if one electron stayed on Earth and the other was beamed to the moon.
Sachdev said he thinks a special kind of this quantum entanglement is responsible for the magiclike properties of superconductors.
A crystalline compound called YBCO (yttrium barium copper oxide) is the first material that scientists discovered that can act as superconductor at temperatures above the boiling point of liquid nitrogen (minus 320 degrees Fahrenheit). Sachdev said the copper atoms in this substance are the most important part of the compound. The electrons around the copper atoms pair off, and "every pair of electrons is everywhere [in the material] at the same time," Sachdev said while showing a diagram of the paired electrons. This clump of entangled particles in superposition leads to superconductivity.
The quantum entanglement in a superconductor is a little more complex, Sachdev said. It appears the electron pairs swap partners, creating what he calls "long-range entanglement."
Learning more about long-range entanglement, Sachdev explained, will lead to better high-temperature superconductors. The basic technology already exists, but other obstacles prevent high-temperature superconductors from being used on a large scale. For example, using superconductors as power lines would require a huge startup cost, Sachdev said.
"Just think about replacing all the power cables under New York," Sachdev said.
- The Mysterious Physics of 7 Everyday Things
- Wacky Physics: The Coolest Little Particles in Nature
- The 9 Biggest Unsolved Mysteries in Physics
Copyright 2014 LiveScience, a TechMediaNetwork company. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.


Tiny robots could help turn prospective parents' dreams into reality.
It depends on how a robotic in-vitro fertilization technique developed by the Advanced Micro and Nanosystems Laboratory (AMNL) at the University of Toronto pans out.
Two years ago, this technique was used to "produce the world's first robotically created human fertilization," says Yu Sun, who established the lab in 2004 in the U of T Mechanical and Industrial Engineering department.
Large-scale trials have yet to be conducted to determine whether the system is feasible as a standard medical tool.
Microtechnology and nanotechology involve the manipulation of extremely small robots or bits of matter. To give a sense of the units of measure involved, a micrometre is one millionth of a metre, while a nanometre is a billionth.
The AMNL's in vitro project focused on improving Intracytoplasmic Sperm Injection, a process used to create test tube babies. Developed in the early 1990s, the procedure allows an embryologist to gather a single sperm in a needle and inject it into an oocyte (egg cell). Given that a sperm head is about five micrometres wide, doing this procedure by hand requires a tremendous amount of precision, dexterity and accuracy.
To make this process more efficient and precise, the U of T lab developed a robotic injection system.
Their system analyzes the sperm "and picks out the best one. The robot then picks up the selected sperm … recognizes the egg cell and punctures the cell membrane to get the sperm in there," Dr. Sun explains. "The injected cell is incubated until it develops in vitro in a petri dish." If it looks like it is growing well, the physician transfers it into the uterus of the patient.
What the lab calls the RICSI system (Robotic Intracytoplasmic Sperm Injection) was first used in human trials in 2012. Though the egg cells were successfully fertilized and transferred to the patients, they ended up having miscarriages, Dr. Sun says.
Researchers want to improve the technology behind RICSI and attract funding for large-scale patient trials at some point in the next 18 months.
Solar cells to synapses
The Grutter Research Group at McGill University in Montreal is also active on the tiny technology front. The group, led by Peter Grutter, chairman of McGill's Department of Physics, invents, designs, builds and modifies atomic force microscopes and similar equipment that can be used to manipulate and study matter on a micro or nano scale.
These studies have a wide range of applications, such as looking at how organic systems convert light to electricity (useful in solar power cells), or understanding the properties of atomic scale contacts (potentially useful in smaller electronic devices in the future, or in the future production of new materials, such as in advanced car engines), Dr. Grutter says.
The group also studies quantum-level processes (with potential applications in quantum computing) and seeks to understand how lithium ions diffuse in batteries (important in getting lithium batteries to charge faster).
In another field, the group looks at how connections, or synapses, in neurons are formed and can be artificially manipulated (which could be relevant for treating neuro-degenerative diseases).
McGill also runs Nanotools Microfab, a facility where academics and researchers of all stripes can experiment with micro- and nanotechnology (the Toronto Nanofabrication Centre at the U of T serves a similar purpose) and build devices related to their research.
"Essentially, it's a 21st century tool shop allowing you to machine structures as small as a few nanometres in size," Dr. Grutter says.
The University of Waterloo Nanorobotics Group in Waterloo, Ont., is experimenting with levitating tiny robots. They are using a process called "quantum locking," where a superconductor tends to stay in place when exposed to a magnetic field. For example, if you tilt the superconductor at a 45-degree angle while it hovers in the air, it will remain at a 45-degree angle. This allows the group's microrobot, MAYA, to turn or levitate.
"We have several projects that could have commercial applications," says Ryan Kearns, project director at the Nanorobotics Group, which is affiliated with the Mechanical and Mechatronics Engineering department.
For instance, if the MAYA project is successful, it could have applications in the micro-fabrication industry, where the tiny robots would allow the precise manipulation of microscopic parts, he says. This would also open the door to building more complicated micro-machines.
No sci-fi film
Still, researchers warn against unrealistic expectations; we're a long way off from Fantastic Voyage, the 1960s science-fiction film in which a miniaturized submarine and crew are injected in a human body to root out a troublesome blood clot.
"I am very optimistic about the use of microrobots in medicine. However, they will have specific and limited applications, such as targeted drug delivery, cauterizing small veins, opening blocked arteries, etc. Any 'inject and forget' nano-robot is still many, many years from being possible," Mr. Kearns says.


The debris from a dwarf galaxy surrounded in dark matter is now moving through Earth at an insane speed of 500 km/second. Scientists are equating it to a hurricane due to its velocity, yet are saying it will have no impact on our planet. What excites them and why this is such big news is because they believe this will be a prime opportunity to finally observe the hypothetical substance.
Back in 2017, astronomers detected a stretched-out line of stars all moving in the same direction along an elliptical path through our region of the Milky Way. Known as the S1 stream, scientists believe this collection of stars to be the remnants of a dwarf galaxy that was shredded by our Milky Way billions of years ago. Through they have detected roughly 30 other streams, this one is of particular interest because its path crosses that of our Sun.
What is Dark Matter/Energy?
Dark matter/energy is a theory and has never technically been directly observed by anyone. They know more about what it isn’t rather than what it is.
It is a term given to the unseen space between solar systems and galaxies that seems to hold everything in place – the unseen glue that makes up an estimated 85% of the universe.
It is called dark matter/energy because it does not seem to interact with any observable electromagnetic radiation, such as light – that is to say, any form of electromagnetic radiation that we know of. Whatever it is, it is not on our known spectrum. Our only way of looking at it is to observe its effects.
They can see light bend from the gravitational force of invisible objects. They are observing stars moving faster than they should be. They know there is an invisible matter or form of energy contributing to the mass and rotation rate of galaxies and solar systems.
Simply put, dark matter is the term given to the invisible energy that exists within space holding it all together. If it were to be seen, it would look like this:
If you think about it, dark matter seems to perfectly resemble the neural webbing of a human brain:
Related Article: Physicists Find Evidence That The Universe Is A Giant Brain
Dark Matter Seems to Be the Mind of the Universe
Scientists are saying nothing will happen to the planet when it passes through because they believe dark matter passes through everything: visible matter, itself, every observed particle, etc. Yet, studies have shown that dark matter may, in fact, interact with light particles to form visible glowing halos.
Neutrinos and dark matter have been observed displaying weak interactions. Around each planet, star, solar system, and galaxy scientists are now detecting dark matter halos.
Dark matter is also believed to exist within the human brain.
When neurons of the brain fire up, radioactive potassium isotopes emit neutrinos. If neutrinos have been observed to interact with dark matter in space, then it is possible these neutrinos within our minds would do the same. Just as halos have been observed around solar systems and galaxies, so to have electromagnetic fields of energy known as auras been observed around humans.
Anything that is conscious is emitting a detectable electromagnetic field – a halo if you will.
Dark matter is an energy we have yet to figure out how to observe because it does not exist within our known electromagnetic spectrum, yet it appears to be fundamental in maintaining the structure of the universe. It is, therefore, possible that dark matter may indeed be the energy of consciousness.
What is Actually Hurtling Past Us?
Since dark matter cannot be observed, how do they know a mass quantity of it is rocketing through our solar system?
All that can be seen are the stream of stars moving in the same direction along an elliptical path – another solar system if you will. So in simple terms, by announcing a hurricane of dark matter is moving through our solar system, they are saying a collection of stars or planets is now passing through.
Could scientists have announced the arrival of the infamous Planet X and its solar system of planets?
Those who have been following this theory believe Planet X to be the architect of worlds: a dwarf star with seven planets in tow. The Sumerian tablets describe this planet as being the home of a race known as the Annunaki who genetically recreated humans long ago. The Nag Hammadi scriptures describe them as Archons – cyborgs that are being led by an ancient form of artificial intelligence.
If dark energy is indeed the energy of consciousness, then perhaps what is passing through our planet is the energy or consciousness of that solar system; our ancient history – and it is absolutely affecting our planet.
This could explain why we are seeing:
- The tilt of our Sun being off by 6 degrees as if something is pulling on it
- Not only is Earth heating up, but so are all of the other planets within our solar system. The brightness of other planets is increasing
- Earth’s magnetic field is beginning to weaken
- Earth’s frequency of 7.83 Hz, known as Schumann’s Resonance is suddenly spiking between 12 – 16 Hz
- Major world changes are now taking place, such as Earth’s pole shifting, increase in earthquakes and floods
- An uptick in asteroids and meteors entering our atmosphere
- Increase in UFO appearances, such as the recent Ireland sightings
- Telescopes shutting down, such as Hubble and Chandra
Most importantly, a mass awakening is occurring. The illusions pulled over our eyes from the corrupt institutions that rule this planet are dissipating and we are beginning to see the world as it truly is.
Related Article: 5 Signs We Are Going Through A Global Mass Awakening
Now here is where things get spooky. The leading dark matter/energy detector is CERN. CERN and Google have just partnered up:
“Together, we will be working to explore possibilities for joint research and development projects in cloud computing, machine learning, and quantum (artificial intelligence) computing.”
For those who are unfamiliar with CERN, the large hadron particle collider located in Geneva, Switzerland, known for smashing particles together, as well as creating and storing anti-matter, here is what you might not know:
- A large portion of CERN is located in the territory of Saint Genis Pouilly. In Roman times, it was called Apolliacum. The town and temple were dedicated to Apollyon – the destroyer (Shiva/Horus). A 2m tall statue of Shiva sits near the main building.
- A video of a human sacrifice taking place in front of the giant Shiva statue surfaced in 2016.
- The main dipoles generate magnetic fields that are 100,000 more powerful than the Earth’s magnetic field.
- Otto Rossler, a German professor at the University of Tubingen, filed a lawsuit against CERN with the European Court of Human Rights, on the grounds that the facility could trigger a mini black hole that could get out of control and annihilate the planet. The Court tossed out Rossler’s request.
- Sergio Bertolucci, former Director for Research and Scientific Computing of the facility, grabbed headlines when he told a British tabloid the supercollider could open otherworldly doors to another dimension for “a very tiny lapse of time,” mere fractions of a second. However, that may be just enough time “to peer into this open door, either by getting something out of it or sending something into it.”
This is what happens above CERN when they are busy colliding particles:
Now the leading company in artificial intelligence and quantum computing is partnering up with a facility that either has or is attempting to access other dimensions – just as they announce the arrival of a dark matter hurricane/solar system of stars/planets that ancient texts describe as cyborgs under the control of artificial intelligence.
Have Google and CERN partnered to welcome the arrival of the Old Gods?
Get involved in a truly independent media platform. Freedom.social is designed for truth seekers and activists of all types, where you get paid in 1776 tokens to participate and take action.
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Devin Powell, Science News, via Tech News, Discovery News (March 28, 2011)
" * By manipulating atoms inside diamonds, scientists have developed a new way to store information.
" * The technique could lead to quantum computers capable of solving problems beyond the reach of today's technology.
"Could be that diamonds are a geek's best friend.
"Scientists have developed a new way to manipulate atoms inside diamond crystals so that they store information long enough to function as quantum memory, which encodes information not as the 0s and 1s crunched by conventional computers but in states that are both 0 and 1 at the same time. Physicists use such quantum data to send information securely, and hope to eventually build quantum computers capable of solving problems beyond the reach of today's technology.
"For those developing this quantum memory, the perfect diamonds don't come from Tiffany & Co. -- or Harry Winston, for that matter. Impurities are the key to the technology.
" 'Oddly enough, perfection may not be the way to go,' said David Awschalom of the University of California, Santa Barbara. 'We want to build in defects.'...
The article implies that the defects - anomalies in the diamond crystal's lattice - would probably involve nitrogen, a frequently-found impurity in diamonds.
The non-carbon atoms are important because - "...Several years ago, scientists learned how to change the spin of such electrons using microwave energy and put them to work as quantum bits, or qubits...."
The new technique links the spin of an electron to a nitrogen atom's nucleus. The transfer involves magnetic fields, and it's fast: "...about 100 nanoseconds, comparable to how long it takes to store information on a stick of RAM."
Back to the article, again: "...The technique has 'a fidelity of 85 to 95 percent,' Awschalom said March 22 in Dallas at a meeting for the American Physical Society.
"In contrast to some other quantum systems under development, which require temperatures close to absolute zero, this diamond memory works at room temperature. The spins inside the diamond can be both changed and measured by shining laser light into the diamond. This could make diamond an attractive material for scientists developing nanophotonic systems designed to move and store information in packets of light.
"Unlike a diamond itself, this quantum memory isn't forever. But it lasts for a very long time by quantum standards. The nuclear spin remains coherent for more than a millisecond, with the potential to improve to seconds....
"...Sebastian Loth, a physicist at IBM's Almaden Research Center in San Jose, Calif. [said], 'If you have a lifetime of milliseconds, that lets you do millions of operations.'
"In addition to stability, diamond may also overcome another hurdle that has faced quantum computing -- it can be scaled up to larger sizes. In a paper published last year in Nano Letters, Awschalom developed a technique for creating customizable patterns of nitrogen atoms inside a diamond, using lasers to implant thousands of atoms in a grid...."
A thousand atoms in a grid is impressive: but the scaling doesn't, apparently, stop there. Transmitting quantum information is possible, by connecting/entangling qubits. Problem is, entanglement seems to work up to a distance of kilometers: which is huge on the atomic scale, but pretty much useless for a network that extends much beyond one city.
That may not be such a serious limitation, though. The article ends with this: "...Quantum repeaters could potentially use small chips of diamond to catch, store and retransmit this information to extend the range, enabling quantum networks to work over much longer distances."
The principle sounds pretty much like the way we transmit radio and television signals today - and that's almost another topic.
For an article with phrases like "fidelity of 85 to 95 percent" that says why some folks are interested in which way electrons spin - it's pretty interesting. In the Lemming's opinion. Your experience may vary.
The Still Sell Vacuum TubesThe Lemming checked: and sure enough, some outfits are still selling vacuum tubes.
That's not very surprising. Old technologies are sometimes useful for particular situations - or someone may just like doing things the old-fashioned way. One of the Lemming's extended family was a flint knapper - and that's another topic.
It's been an exciting half-century. The Lemming remembers when it was obvious that computers would be huge things, occupying entire buildings and consuming vast amounts of power.
Then the transistor stopped being a laboratory curiosity, and started being part of little boxes attached to the ears of adolescents.
This hasn't, the Lemming suspects, been a particularly comfortable era for folks who'd just as soon that their great-grandfather's way of life be indistinguishable from their own - and for whom "innovation" is the reckless practice of trying a new sort of food. Or wearing a shirt of a different color.
And the Lemming's getting off-topic again.
- "Good News, Neural Devices Connect Brain, Computers: Bad News, Same Thing"
(July 11, 2009)
- "Nanotechnolgy and Electronics: Atom-Sized Transistors ('Nanotronics'??)"
(February 20, 2009)
- "Programmable Metallization Cell (PMC): One Terabyte of Data in a Little Package"
(June 20, 2008)
- "More About the Marvelous Memristor"
(May 1, 2008)
- "Oral Tradition; Writing; Movable Type; Internet - Exciting Times!"
(July 31, 2007)


If you are flipping coins, and you want to turn up five heads at the same time, how would you go about it?
You could take five coins and keep flipping them until the odds finally work in your favour. Or you could flip a lot of coins at once, and only count the ones that turn up heads.
That second idea is the basic approach to scattershot boson sampling, considered a potential precursor to quantum computing, and Perimeter Institute postdoctoral researcher Daniel Brod is part of a team that just showed it can work, in a paper published in the new web-based journal Science Advances on Friday.
A boson sampler is essentially a simplified quantum computation device that uses bosons (in this case, photons) to carry out a specific task.
No one yet knows if boson sampling has any practical application, but it is considered a good test case for quantum computation because photon behaviour inside the sampler is expected to be hard to simulate classically.
In boson sampling, photons are sent into an interferometer made up of an array of beam splitters. Interferometers can be as big as a room, but boson sampling experiments often use chips as small as microscope slides, with a network of optical fibres etched into the glass.
The photons follow along the fibres and, when two fibres come sufficiently close, there is a chance that the photon will “jump” from one to the other. These close-set fibres effectively acts as a beam splitter.
Each time a photon passes through a beam splitter, it moves along two directions in quantum superposition. A measurement at the output ports reveals the balance between constructive and destructive interference that the photon experienced along the way.
There is a significant challenge to boson sampling, though: we don’t yet have a simple way to generate identical photons on demand.
Experiments rely on a technique called parametric down-conversion (PDC). PDC sources shine a laser through a non-linear crystal, and some of the laser’s photons are converted into new pairs of photons that split off in opposite directions.
For boson sampling, one photon from the new pair shoots into the sampling device; the other flows into a separate collector that alerts the scientists to the photon’s existence (this is called “heralding”).
The problem is, you cannot control when these new, paired photons will appear. The result is somewhat similar to the coin-flipping example from above: three PDC sources will eventually generate three identical photons, but you can’t control when that will happen.
If you want to create 30 photons at once (enough to run an experiment that is too hard for a classical computer to simulate), you could be waiting such a long time – weeks, months, or longer – that it renders the experiment void.
This limitation was a major obstacle for boson sampling. Then, a new idea was floated in 2013: why not take a scattershot approach? By connecting several PDC sources to the interferometer, but only collecting data when they produced the photons you needed, scientists could, in essence, flip more coins.
The idea of taking a scattershot approach was explored on the blog of MIT professor Scott Aaronson (who proposed the original boson sampling mode in 2010), with the idea credited to Steven Kolthammer in Oxford, and came on the heels of a similar idea proposed by researchers at the University of Queensland.
Using this scattershot approach, a collaboration including Brod and theorists and experimentalists from Perimeter, Brazil, Rome, and Milan has made another significant advance.
In experiments led by Fabio Sciarrino at the Quantum Optics group at the Sapienza University of Rome, the team performed boson sampling in a chip with 13 input ports and 13 output ports, connected by pathways in the chip.
The experiment called for three photons, so the team connected six PDC sources to the sampler’s input ports. Two photons would come from the first PDC source. The third photon could come from any of the other five sources. (Watch an animation of the experiment here.)
Whenever three photons were generated at the input ports, the team collected the corresponding output data showing from where the photons emerged. (Since the photons are identical, it is impossible to know which incoming photon ends up where.)
This is what makes the computation fundamentally hard, Brod says: “If we could know which path a photon followed, or if we could distinguish them (by their frequency or polarization, for example), the whole thing would be easy to simulate classically.”
While this did not increase the number of photons being used in a boson sampling experiment, the scattershot approach collected data 4.5 times faster.
“The first time we did experiments with three photons, I think it took over a week to collect the data. That is very slow,” Brod says. “An almost fivefold improvement is good, but this approach promises exponentially larger improvements as the experiments scale up.”
The improvement also addresses one of the big problems facing boson sampling: the reliance on PDC photon sources. The team showed that, even if you can’t create photons on demand, with enough sources working in a scattershot manner, you can get the desired number of photons to run your experiment.
There are still many other challenges ahead, including ways of finding out if the device is working like it’s supposed to. After all, if no classical computer can reproduce the results, “how do you know that your data really corresponds to anything interesting?” Brod says.
“This is one of the most important open questions from the theoretical side. We are going to need more theoretical advances before we can say something concrete about the computational tasks these systems are performing.”
Back in 2011, when boson sampling came to the fore, Brod was a curious PhD student in Brazil who became engrossed in a lecture Scott Aaronson gave at Perimeter that was webcast on PIRSA.
Now, Brod is a member of one of four experimentalist teams around the world actively pushing the idea forward. There’s also a lot of theoretical work yet to do, but that is fine by him.
“I find this whole boson sampling idea very elegant – the way that it seems, at least, to connect some concepts of computer science to fundamental physics, the very fundamental properties of identical particles. I think that is a very elegant connection that I would definitely like to understand better.”
– Tenille Bonoguore
 Bosons are fundamental particles that can occupy the same quantum state, and can be elementary, like photons, or composite, like mesons.
 Superposition is the counter-intuitive quantum phenomenon where a particle can display the wave-like behaviour of being “spread out” through various points in space, whilst retaining the particle-like property that it can only be measured at specific locations. In this case, it’s as if the photon, after passing through a beam splitter, propagates along the two outward directions at once. The caveat is that, when it reaches a detector, it is found in only one of those two directions with corresponding probabilities. This is the so-called collapse of the wave function.
Watch an earlier boson sampling experiment carried out by the Sapienza University of Rome team as part of this collaboration.


Quantum Key Distribution
Quantum key distribution is a technique used in the context of quantum cryptography in order to generate a perfectly random key which is shared by a sender and a recipient while making sure that nobody else has a chance to learn about the key, e.g. by intercepting the communication channel used during the process. Basic principles of quantum mechanics are exploited to ensure that. Only if quantum mechanics were to turn out to be a flawed theory (for which there is no reasonable evidence after decades of intense research), it might be possible to break the security of such a communication system.
The best known and popular scheme of quantum key distribution is based on the Bennet–Brassard protocol (in short: BB84), which was invented in 1984 . It relies on the no-cloning theorem [3, 4] for non-orthogonal quantum states. For example, it can be implemented using polarization states of single photons. Briefly, the Bennet–Brassard protocol works as follows:
- The sender (usually called Alice) sends out a sequence of single photons. For each photon, it randomly chooses one of two possible base states, with one of them having the possible polarization directions up/down and left/right, and the other one polarization directions which are tilted by 45°. In each case, the actual polarization direction is also randomly chosen.
- The receiver (called Bob) detects the polarizations of the incoming photons, also randomly choosing the base states. This means that on average half of the photons will be measured with the “wrong” base states, i.e. with states not corresponding to those of the sender.
- Later, Alice and Bob use a public (possibly interceptable) communication channel to talk about the states used for each photon (but not on the chosen polarization directions). In this way, they can find out which of the photons were by chance treated with the same base states on both sides.
- They then discard all photons with a “wrong” basis, and the others represent a sequence of bits which should be identical for Alice and Bob and should be known only to them, provided that the transmission has not been manipulated by anybody. Whether or not this happened they can test by comparing some number of the obtained bits via the public information channel. If these bits agree, they know that the other ones are also correct and can finally be used for the actual data transmission.
A possible eavesdropper (called Eve) would have to detect the photons' polarization directions without knowing the corresponding base states. In those cases where Eve's guess concerning the base states is wrong, Eve obtains random results. If Eve sends out photons with these polarization directions, Bob's results will also be random in cases where Bob's guess was right. This will therefore be detected during the last stage (the bit verification). Quantum mechanics would not allow Eve to do a polarization measurement without projecting the photon state onto the chosen base states, i.e., without altering the photon states.
Note that Alice and Bob actually need to carry out secure authentication in order to prevent an interceptor from manipulating their public communications. This also requires some secret key, which at a first glance would seem to lead to a catch-22 situation: you need a secret key in order to generate another secret key. However, authentication requires only a short key, whereas the quantum key distribution scheme can generate a much longer one and is therefore still useful.
Some remaining problems are:
- Ideally, a perfect single photon source should be used for the sender, but this is difficult to realize. Using strongly attenuated laser pulses which have only the order of one photon per pulse generates some risk that pulses which by chance have more than one photon can be used by Eve to gain some information. However, there are some schemes of privacy amplification to destroy this possible knowledge of Eve at the cost of reducing the number of obtained bits for the key.
- Losses in the transmission channel (e.g. an optical fiber) reduce the degree of the required quantum correlations and also create chances for an eavesdropper. However, there are also refinements of the technique (quantum error correction) to deal with this issue, provided that the losses are low enough (at most a few percent of the photons).
- The bit rate with which a key is generated is normally fairly low, particularly for large transmission distances. This accordingly limits the bit rate of secure communications.
A modified cryptography scheme was suggested in 1991 by Ekert . Here, entangled states are used instead of the randomly chosen measurement basis. In many respects, this protocol is similar to the BB84 protocol.
Some quantum key distribution systems have been demonstrated which promise unconditional security for transmission distances up to a few tens of kilometers, although at least one system has been proven not to be perfectly secure; successful eavesdropping has been demonstrated . It should be possible, however, to eliminate such security loopholes with more careful implementations. Further system refinements should also allow for transmission distances over 100 km. Research is also directed at developing more practical single-photon and correlated photon pair sources, based on, e.g., spontaneous parametric downconversion in χ(2) crystals or spontaneous four-wave mixing in optical fibers.
There are already some commercial quantum key distribution systems which can be used by banks, for example.
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