In quantum teleportation, the properties of quantum entanglement are used to send a spin state (qubit) between observers without physically moving the involved particle. The particles themselves are not really teleported, but the state of one particle is destroyed on one side and extracted on the other side, so the information that the state encodes is communicated. The process is not instantaneous, because information must be communicated classically between observers as part of the process. The usefulness of quantum teleportation lies in its ability to send quantum information arbitrarily far distances without exposing quantum states to thermal decoherence from the environment or other adverse effects.
Although quantum teleportation can in principle be used to actually teleport macroscopic objects (in the sense that two objects in exactly the same quantum state are identical), the number of entangled states necessary to accomplish this is well outside anything physically achievable, since maintaining such a massive number of entangled states without decohering is a difficult problem. Quantum teleportation, is, however, vital to the operation of quantum computers, in which manipulation of quantum information is of paramount importance. Quantum teleportation may eventually assist in the development of a "quantum internet" that would function by transporting information between local quantum computers using quantum teleportation .
Below is a sketch of an algorithm for teleporting quantum information. Suppose Alice has state C, which she wants to send to Bob. To achieve this, Alice and Bob should follow the sequence of steps:
1) Generate an entangled pair of electrons with spin states A and B, in a particular Bell state:
Separate the entangled electrons, sending A to Alice and B to Bob.
2) Alice measures the "Bell state" (described below) of A and C, entangling A and C.
3) Alice sends the result of her measurement to Bob via some classical method of communication.
4) Bob measures the spin of state B along an axis determined by Alice's measurement
Since step 3 involves communicating via some classical method, the information in the entangled state must respect causality. Relativity is not violated because the information cannot be communicated faster than the classical communication in step 3 can be performed, which is sub-lightspeed.
The idea of quantum teleportation, which can be seen in the mathematics below, is that Alice's measurement disentangles A and B and entangles A and C. Depending on what particular entangled state Alice sees, Bob will know exactly how B was disentangled, and can manipulate B to take the state that C had originally. Thus the state C was "teleported" from Alice to Bob, who now has a state that looks identical to how C originally looked. It is important to note that state C is not preserved in the processes: the no-cloning and no-deletion theorems of quantum mechanics prevent quantum information from being perfectly replicated or destroyed. Bob receives a state that looks like C did originally, but Alice no longer has the original state C in the end, since it is now in an entangled state with A.
Which of the following is true of quantum teleportation?
1) Quantum information is transferred between states
2) The teleported particle is physically transferred between locations
3) A quantum state is cloned between observers
4) Quantum information is permanently removed from the system
As a review, recall the Pauli matrices:
The spin operators along each axis are defined as times each of for the axes respectively.
These Pauli matrices are used to construct Bell states, an orthonormal basis of entangled states for the tensor product space of spin- particles:
Measurements that project tensor products of spin states onto the Bell basis are called Bell measurements.
Now, follow the algorithm sketched in the previous section. Suppose Alice starts with state C, which she wants to send Bob. State C can be written in the most general form:
with and normalized complex constants.
1) Generate an entangled pair of electrons A and B in the Bell state:
The state of the full system of three particles is therefore . This is a product state between entangled pair AB and non-entangled C.
2) Alice measures the Bell state of AC, entangling A and C while disentangling B. The process of measuring the Bell state projects a non-entangled state into an entangled state, since all four Bell states are entangled.
Expanding Alice's full original state, she starts with:
Multiplying out the states and changing to the Bell basis of A and C, this state can be rewritten:
When Alice measures the Bell state of A and C, she will find one of , each with probability . Whichever she measures, the state of particle B will be after measurement.
3) To send Bob the state of particle C, therefore, Alice does not need to send Bob the possibly infinite amount of information contained in the coefficients and which may be real numbers out to arbitrary precision. She needs only to send the integer of the Bell state of A and C, which is a maximum of two bits of information. Alice can send this information to Bob in whatever classical way she likes.
4) Bob receives the integer from Alice that labels the Bell state that she measured. After Alice's measurement, the overall state of the system is:
Bob therefore applies to the disentangled state on his end, by measuring the spin along axis . Since for all , Bob is left with the overall state:
Bob has therefore changed the spin state of particle B to:
which is identical to the original state of particle C that Alice wanted to send. The information in state C has been "teleported" to Bob's state: the final spin state of B looks like C's original state. Note, however, that the particles involved never change between observers: Alice always has A and C, and Bob always has B.
- Pirandola, S., & Braunstein, S. Physics: Unite to build a quantum Internet. Retrieved from http://www.nature.com/news/physics-unite-to-build-a-quantum-internet-1.19716
- Debenben, . quantum teleportation diagram. Retrieved from https://commons.wikimedia.org/w/index.php?curid=34503176


The theory behind quantum computing was first laid out in the 1980s. Yet, it was not until recently that practice caught up with theory, enabling the construction of the first quantum computers. An unchallenged pioneer in this technology is the Canadian company D-Wave Systems. Its clients include the CIA and the National Security Agency (NSA), many research institutes, NASA, and businesses including Google and Lockheed Martin. The European Union plans to allocate a billion euros to quantum research. Tech companies are developing their own technologies anticipating diverse applications for the awesome computational power that can be derived from quanta, the fundamental building blocks of matter.
The evening of Moore’s Law
Why is so much being spent on quantum computing? Why is it such a huge breakthrough?
Today’s processors are made up of billions of transistors a few nanometers in size, packed into a very small space. According to Moore’s Law, the number of transistors that fit into a microprocessor doubles roughly every two years.Unfortunately, or inevitably, increases in the processing power of chips have been plateauing. We are approaching the technological limits of how many transistors can be jammed into such a small space. The borderline that cannot be crossed is a transistor the size of a single atom with a single electron used to toggle between the states of 0 and 1.
The simplest way to demonstrate the advantages of the quantum computer is to compare it with the classical machine. The familiar device we know from our daily work relies for all its operations on basic information units called bits. These, however, can only represent two states: 0 or 1.
In quantum computing, it’s possible to use intermediate, non-binary states that liberate us from the bondage of 0 and 1, two opposing values. The qubit(or quantum bit), which is what the information units used by quantum devices are called, can assume the values of 0 and 1 simultaneously. In fact, qubits can assume an infinite number of states between0 and 1, achieving what is referred to as the superposition. Only when the value of a qubit is observed does it ever assume either of the two basic states: 0 or 1.
This may seem like a minor difference, but a qubit remaining in superposition can perform multiple tasks at the same time. We are helped here by the operation of two fundamental laws of quantum physics. Physically, a qubit can be represented by any quantum set to two different basic states: two energy levels in an atom, or two levels of photon polarization, vertical or horizontal. Therefore, while a bit in a classical computer holds one of two values (0-1 or 1-0), and two bits hold one of four values, and so on, two qubits hold not two but four values at any given time while 16 qubits may hold as many as 65536 values simultaneously, or 16 squared. The number of possibilities doubles for every qubit added, allowing a quantum machine to process far more data than can a binary computer in an incredibly short time.
Imagine a volume of data so big it would take millions of years to process by means of a classical computer. This would not be a problem for a quantum machine. It can process data hundreds of thousands and, ultimately, millions of times faster than machines made up of even the most sophisticated silicon components. The difference in capacity between quantum and conventional computers can theoretically amount to an astounding 1:18 000 000 000 000 000 000 times!
Such a computer could sift through and recognize objects in a giant collection of photographs. It would be perfect for big number processing, encryption and code breaking.
Or, blockchain breaking.
The kiss of death for cryptocurrencies
According to some researchers, once quantum computers rise and spread, they could be used to crack the cryptographic protections responsible for the operating model and security of blockchain technology – the technology on which cryptocurrencies are based.
Collectively, on January 3, 2018, cryptocurrencies were worth an estimated USD 700 billion. This certainly makes them worth fighting for. What makes blockchain technology vulnerable to the threat of quantum computers?Blockchain architecture is protected by two types of security keys: private and public. To make a cryptocurrency transaction, the buyer shares a public key with its seller, while the latter uses a private key to acknowledge receipt. Should anyone other than the seller or buyer acquire the private key, they would gain control of the transaction. The private key can either be stolen or broken by the brute force of enormous computational power. The emergence and spread of quantum computers will render the blockchain technology’s algorithms useless. A holder of a quantum computer will be able to calculate the private key using the public key.This will give the code holder unfettered access to all world’s wallets holding all the world’s cryptocurrencies.
However, even though it can crack a private key in minutes, the cost of a quantum computer will make that a very expensive operation.
But $700 billion is a powerful incentive.
Not all is lost
The easiest way to secure keys in the face of quantum computing would be to have the cryptocurrency community adopt a more sophisticated set of cryptographic standards. The technology to do so is out there. However, any modifications require the consent of the entire cryptocurrency community, with separate consents for each cryptocurrency. Considering that a recent attempt to get all users to agree to an increase in the volume of bitcoin (BTC) blocks – from 2MB to 4MB – has failed miserably, reaching a consensus for upping security standards may prove equally elusive. The blockchain protocol requires 80% of currency users to approve any change. Since doubling the bandwidth, and significantly accelerating transactions would benefit everyone, that would appear to be a no-brainer. And yet, as it turned out, not everyone saw it that way.
On the other hand, by the time quantum computers become widely available, the cryptocurrency community may well recognize the threat and begin to see eye to eye on updating cryptographic standards. That would keep blockchain and the cryptocurrency technology secure from quantum computers well into the future.
Devilishly fast but not unlimited
A quantum computer requires a control system (the equivalent of an operating system), algorithms to make quantum calculations and proper calculation software. The development of quantum algorithms is very difficult as they need to rely on the principles of quantum mechanics. The algorithms followed by quantum computers rely on the rules of probability. What this means is that by running the same algorithm on a quantum computer twice, one may get completely different results as the process itself is randomized. To put it simply, to arrive at reliable calculation with a quantum computer, one must factor in the laws of probability – a complex process indeed!
Quantum computers are suited for very specialized and specific calculations as well as algorithms that help harness all their powers. In other words, quantum computers will not appear on every desk or in every home. However, regardless of how much time is needed to generate a given result by means of an algorithm, we can imagine, even today, a situation in which a quantum machine, and only a quantum machine, could solve a problem that mankind desperately needs to solve.
Quantum computer IBM 4


Companies like IBM or Google have already unveiled the first quantum computers in history. This technological innovation represents an advance comparable to that of the arrival of the first computers in the mid-20th century.
As its name indicates, quantum computing may seem like another advancement in traditional computing, but that’s not the truth; this is a technology radically different from the one used in our computers. It will take time for quantum computing to reach the home environment precisely because of this.
However, that does not prevent quantum computing from having a large number of applications that we will discover. We’ll explain what quantum computing is, and what uses will be given to it in the near future.
What is quantum computing?
To understand how quantum computing works, it is helpful to remember how classical computing works. A traditional computer uses a binary system, based on the bit as the fundamental unit of information. That means that all the elements of the computer translate the electrical impulse into 1, if the voltage is high, or 0 if it is low or null.
This system makes it possible to represent numbers and perform different logical operations with them. However, it has a fundamental limitation: the numbers do not change by themselves, but each of them must be deliberately changed by a mathematical operation, which consumes energy and time.
Quantum computing introduces a very important quantitative leap: the minimum unit is the qubit, which can have a value of 1, 0 or both at the same time in different percentages (for example, 1 to 60% and 0 to 40%). This allows a great variety of intermediate states, which are achieved through processes such as superposition or entanglement. These processes make it possible to perform calculations beyond the capabilities of a classical computer.
The main advantage of quantum computers is the optimization of data processing. In fact, quantum computing will not replace classical computers, but will be combined with them in a hybrid structure: the traditional computing device can send data and instructions to the quantum computer, which processes the data at high speed and returns it.
Applications of quantum programming are virtually endless. Disciplines such as chemistry, medicine, logistics, economics and agriculture will benefit from the processing and calculation of complex data at high speed. Another field in which it will become vitally important will be artificial intelligence and online security: the power of a quantum computer will allow technological devices to analyze data and react to it much faster.
Origin of quantum computing
Although the first practical applications of quantum computing are very recent, they are all based on quantum physics, a theory that developed over the past century. Albert Einstein and Max Planck observed that light does not propagate in a continuous wave, but in several different sets or quanta. Subsequent quantum mechanical investigations found that these units overlap, resulting in several physical states overlapping simultaneously.
Although superposition made it possible to conceive a quantum computer in the mid-20th century, another problem arose: Quantum physics showed that there were intermediate states, but classical computing would always read them as bits. Using the example above, a traditional computer would process a qubit from 1 to 60% and 0 to 40% and interpret it as 1.
What allowed the development of quantum computing was entanglement. This process allowed the discovery of Shor’s algorithm and quantum temper, which sped up the calculation of minimum values and prime factors. This makes the computer capable of encrypting intermediate states and processing data at high speed.
Differences between usual computing and quantum computing
We have already explored some differences between classical and quantum computing: the basic unit they use, the language derived from them, and the speed of processing. These factors lead to radical differences in application: quantum computing is capable of executing algorithms that a classical computer would take thousands of years to perform, unless it had unlimited memory.
Quantum computers differ fundamentally in their operation, as well as in their construction: IBM’s quantum computer is a device kept in glass and covered in cables, and it does not have conventional devices such as screens or keyboards. There are two reasons for this: First, they are currently able only to process information, so they do not require an interface. Secondly, they work under very strict conditions: they require a temperature of -273 ° C and have superconducting components.
Progress and Challenges in Quantum Computing
Considering the difficulty of building and maintaining a quantum computer, it is clear that the widespread application of this new technology will take a few more years. However, there have already been some significant advances in quantum computing: the first quantum computer was introduced in 1998, and Shor’s algorithm was run for the first time only three years later.
At the beginning of this century, the D-Wave company was at the forefront of progress in quantum computing: in 2007 it managed to execute quantum tempering with 16 qubits, and that same year it introduced a 2000 qubit computer. IBM has already introduced devices capable of running other algorithms, so we can expect new milestones to be achieved in the coming years.
However, a number of challenges facing quantum computing today need to be addressed first. Quantum computers have a very limited calculation time after which the information will lose its precision. This is because qubits are very unreliable and easily miscalculated. Furthermore, the hybrid technology between classical and quantum computing requires the development of quantum algorithms, before which it will be difficult for technological advances to be applied to common devices.
In summary, it appears that quantum computing will only be available to companies that perform computationally expensive calculations. For example, companies such as Google and Microsoft will use them to develop machine learning or replicate biochemical processes, and security agencies will use them to decipher encrypted codes and increase security. Ordinary users will need to wait before seeing any results in their homes, but the exponential growth of quantum computing is very promising.


By manipulating Quantum Structures in the Sun’s Atmosphere entanglement of electricity can be achieved.
What is electricity
Electricity is the set of physical phenomena associated with the presence and motion of matter that has a property of electric charge. Electricity is related to magnetism, both being part of the phenomenon of electromagnetism, as described by Maxwell’s equations. Various common phenomena are related to electricity, including lightning, static electricity, electric heating, electric discharges and many others.
Electricity is carried by moving electrons. In the gases that make up air, these electrons are normally strongly attached to the molecules that they form. However, during a lighting strike, they’re ripped away and can move about, allowing electricity to flow. This leaves behind positively charged molecules. The mix of electrons and positive molecules is called a plasma. When the negative electrons recombine with the positively charged gas to re-form stable molecules, visible light is given off. That’s what you see.
Visible light is one way energy uses to get around. Light waves are the result of vibrations of electric and magnetic fields, and are thus a form of electromagnetic (EM) radiation. Visible light is just one of many types of EM radiation, and occupies a very small range of the overall electromagnetic spectrum. We can, however, directly sense light with our own eyes, thus elevating the role of this narrow window in the EM spectrum because of its significance to us.
The sun’s magnetic field
Similar to our own planet, the sun is like a huge bar magnet with a north and a south pole producing a magnetic field. But the sun’s magnetic field is about twice as strong as the Earth’s and much, much larger, extending well beyond the farthest planet in the solar system.
The Magnetic Field
Magnetic fields are produced by moving electric charges. Everything is made up of atoms, and each atom has a nucleus made of neutrons and protons with electrons that orbit around the nucleus. Since the orbiting electrons ≠are tiny moving charges, a small magnetic field is created around each atom. These magnetic fields have a specific orientation or direction; this orientation is called the atom’s magnetic moment. Basically, all of the atoms in an object act like several tiny magnets. In most materials, all of these moments face random directions and they all cancel each other out, and there is a net magnetization of 0, which means the object will not be a magnet. However, when all or most of these moments align in the same direction, the entire object has a net magnetization and creates a magnetic field around itself.
Hidden Portals in Earth’s Magnetic Field
“We call them X-points or electron diffusion regions,” explains plasma physicist Jack Scudder of the University of Iowa. “They’re places where the magnetic field of Earth connects to the magnetic field of the Sun, creating an uninterrupted path leading from our own planet to the sun’s atmosphere 93 million miles away.”
Observations by NASA’s THEMIS spacecraft and Europe’s Cluster probes suggest that these magnetic portals open and close dozens of times each day. They’re typically located a few tens of thousands of kilometers from Earth where the geomagnetic field meets the onrushing solar wind. Most portals are small and short-lived; others are yawning, vast, and sustained. Tons of energetic particles can flow through the openings, heating Earth’s upper atmosphere, sparking geomagnetic storms, and igniting bright polar auroras.
THE MAGNETIC FIELD PORTALS POSITIONS AND LOCATIONS REMAIN TOP SECRET AND CLASSIFIED
Macroscopic Quantum Energy
By manipulating the Quantum state of electrons in the solar wind that interacts with the Earth’s Magnetic Field creating portals to the sun’s atmosphere you could create Macroscopic Quantum Structures that use Quantum Entanglement to entangle Electrons in the atmosphere of the Sun and connect those entangled packets of energy to Earth instantly.
The energy would be teleported via quantum entanglement. Currently Electricity flows on lines and wires and distributed to the consumer. Meters are installed to measure the amount of energy units being used. If one of the lines connecting this flow of energy is disruption and it supplies you personally with power you lose power until the line is reconnected.
The Macroscopic Quantum Structures will be the hubs where this energy is being generated. The Electricity generated by the Sun’s atmosphere can be converted remotely and entangled. Once entangled the energy is transported instantaneously to a transfer station on Earth or in Space. The transfer station then distributes the entangled Electricity to larger networks globally to be distributed to the consumer directly with the proper device to convert this entangled Electricity directly into flowing Electricity. No wires needed.
With Macroscopic Quantum Energy and Macroscopic Quantum Communication it sends and receives packets of information outside of the radio spectrum. It instead focuses on the frequency of interest (Electrons and Photons) which are easily manipulated with Quantum Technology. Using what Einstein referred to as “Spooky Action at a Distance ” Quantum Entanglement of all of our communications and energy needs can be securely sent and received globally at little to no costs.
Quantum Energy would benefit all of humanity. It would revolutionize the power industry and give every citizen on this planet access to free forms of energy naturally formed in our universe in addition to communication technology. You would no longer need Power Plants and instead would generate all electricity on Earth from space with energy from our star the Sun.
The Sun releases energy at a mass–energy conversion rate of 4.26 million metric tons per second, which produces the equivalent of 38,460 septillion watts (3.846×1026 W) per second.
In 2019, the world primary energy consumption was 13.9 billion toe (ton of oil equivalent). With a world population of about 7.7 billion, we now have a world average consumption of primary energy of 58 kWh per day per person.
WITH QUANTUM ENERGY WE COULD HARNESS ENORMOUS AMOUNTS OF ENERGY in the amount of 38,460,000,000,000,000,000,000,000,000 watts per second. (38,460 septillion watts (3.846×1026 W) per second.)
This energy is always around us and currently due to our lack of technological advancement it has remained unacessable to humans until now. Rather than using Earth’s natural resources to produce energy on Earth that pollutes the atmosphere of Earth with toxins we can NOW theoretically use the natural resources of space and harness the powerful energy of our star using Advanced Macroscopic Quantum Structures.Expand the research


Think back a second. When was it that you got your first smartphone? What about the first time that you streamed a show online?
Those things were available to us around 12-15 years ago, depending on how tech-savvy you were at that time. Now, though, smartphones and fast computers are ubiquitous. Not only that, but they’re affordable.
The cutting-edge technology just keeps slicing deeper and deeper to the point that we’re used to advanced progress. We expect to be amazed, then we get bored of our amazement and look for the next thing.
That said, is computer processor speed just going to keep getting better?
We’re going to look at this question today, giving you some insights into the world of technology and where it’s headed. Let’s get started.
How Do Computer Processors Work?
To start this discussion, we have to know a few things about how computer processors work. A few basic insights into CPUs allow us to have a better grasp of what the future might hold.
A central processing unit (CPU) is considered the brain of the computer. It’s where all of the complex tasks take place, and it manages everything you do while you use a device. The CPU reaches into the random access memory and hard drive storage to get information in a matter of milliseconds.
It also interacts with your graphics processing unit to generate all of the beautiful images and 3D renderings you engage with on-screen.
The processor consists of 10s of millions of transistors made of semiconductor materials. Simply put, a semiconductor allows or blocks electrical signals to flow, depending on the situation.
The Importance of Transistors
As a semiconductor, a transistor manages electrical signals in either a positive or negative fashion. When it’s positive to the current, it allows it to continue or directs it in the right way. When negative, that signal is stopped.
It’s like a little traffic cop that stops and starts traffic to keep things flowing smoothly. This little device is the absolute building block for all computers and pieces of modern technology.
It might not seem like that’s very complex or that it could power something as influential as the iPhone. That said, these devices are all just the result of small electrical signals getting directed to produce specific, mechanical actions.
When you press a single key on your keyboard, there’s a simple and elegant process that takes place. The button sends a signal to the CPU, which then sends a signal to the screen, and the letter pops up in an instant. That process is reflective of almost any process you do on the computer.
It’s simple, but the complexity compounds each time you press another button. In the case of the transistor, that little traffic cop gets multiplied by orders of magnitude and placed in a microchip.
The microchip is an essential worker for the CPU. A chip the size of your fingernail holds billions (yes, billions) of transistors.
Moore’s Law and The Future of Technology
At some point, the only devices available had ten or twenty transistors in them. That was some time back in the sixties or seventies when computer technology took hold.
The more transistors you include in a device, though, the better it is. When they’re placed on a microchip, they’re said to be included in an “integrated circuit.” When you increase the ability of an integrated circuit to house transistors, you improve the quality of the device in question.
One of the founders of Intel computers, Gordon Moore, proposed an idea. He said that, so long as the price stays consistent, the integrated circuit will be able to house double the number of components every 18 to 24 months.
As a result, the performance of technology will be twice as good as it was a year and a half prior. His law held up for the first twenty years of the computer.
Since then, it has had years when advancement fell behind his estimate and years when it surpassed his estimate. That said, the slope of Moore’s law and the slope of microprocessor ability are eerily close to one another.
If nothing else, we can look to Moore’s law to estimate roughly how good technology will be in the near and distant future, barring any big changes to the situation.
It will keep doubling and improving ad infinitum in that case, though. Can we be sure that that will happen?
How Can Things Improve?
The thing about Moore’s law is that it was created when one couldn’t foresee the technology we have now. Technology breeds paradigm shifts, and that’s what we can expect in the next decades if Moore’s law is correct until then.
We’ll hypothetically reach a point when we no longer need transistors and microchips at all. People are already producing transistors that are the size of a handful of atoms pushed together.
That’s approaching the size of the fundamental building blocks of the universe as far as we know. What lies beyond that advancement is difficult to say, but things are accelerated by the fact that computers are actually doing the thinking for us in some instances.
There are more neurons in the human mind than microchips in the smartest computer, but that doesn’t mean that computers aren’t better at thinking logically and recalling information than we are. Artificial intelligence thinks critically in real-time, and it might be able to produce better computers than we can.
Is Quantum Computing Just Science Fiction?
Quantum computers are already in existence, although they’re not as powerful as classical computers with microchips yet. Yet is the keyword, though.
The science hasn’t gotten narrowed down into perfection as of yet, but the idea is that artificial intelligence will keep chipping away at the stone until David emerges.
Quantum computing plays on the random nature of quantum states like entanglement, superposition, and more. Without getting too deep into the terminology, it might help to understand, basically, what those things are.
Quantum mechanics state that particles and waves exist to different degrees at different times and their existence is relative to the observer at a particular time. Entanglement is an instance when the particle and wave occupy the same space in such a way that the observer can’t say that either one doesn’t exist.
Superimposition suggests that both particle and wave are atop one another in an instance that produces a third, equally viable state. Those things are heady enough as it is, but introduce computing into the mix and you’ve got a real brain-melter.
The result is that computers will work trillions of times faster than ours do. The implications of that are hard to imagine, especially for our consumer technology.
What To Expect From Computer Processor Speed
Whether or not Moore’s law is correct, we can be sure that things will improve. Provided that there’s no extreme climate disaster or global collapse, technology will improve.
Phones, computers, and other devices are essential to the lifestyles of billions of people on earth. There’s a lot of money waiting for the individuals or companies that think up new ways to improve our lives through technology.
There are also a lot of issues on planet earth that something like quantum computing could fix. Supply chain management, hunger, poverty, and numerous other essential problems might get solved by a more intelligent computer.
So, there are more than enough carrots dangling in front of humanity to push the technology cart forward. Whether that will keep happening in a way that doubles every couple of years, only time will tell.
That said, quantum computing advancements will be a paradigm shift for the entire idea of technology. The speed of our computers today was almost unimaginable 30 years ago. Things are incredibly fast and easy to use now.
You can get the scoop on modern computers and start enjoying them if you’re not already.
Where Will It End?
If things scale up at an exponential rate as they have, it’s impossible to imagine what the state of technology could be. Just like people 100 years ago would faint if they saw a smartphone, we might do the same if we saw what was possible 20 years from now.
The difference for us is that things change at an exponential rate. What would have taken 100 years might take only ten now. Ten years from now, it’ll only take one year to do what took us ten, and so on and so forth.
If things keep multiplying upon themselves like that, the only question is “where does it all end?” Will the singularity come and take us over? Will we merge with technology in some way?
Science fiction has to take the reins from that point on.
Want to Learn More About Computer Chips?
Hopefully, our look at computer processor speed was interesting to you. There’s a lot more to learn and keep track of as things move forward, though.
We’re here to keep you filled in. Explore our site for more ideas on technology, central processing unit insights, processor cores, and much more.


Artificial intelligence will track down gravity lenses2022.09.15 12:26 - Marek Pawłowski
Images of distant galaxies, distorted by powerful gravitational lenses, are visually the most out-of-the-box phenomena photographed by telescopes. Their automatic detection is difficult for many reasons. During the international workshop in Warsaw, dedicated to machine learning, scientists from the National Center for Nuclear Research demonstrated theoretical models and software that deal with this task with high efficiency and reliability.
In the coming years, astronomers expect the influx of a huge number of photos, mainly from large-scale sky surveys. Abundant observational material gives hope for groundbreaking discoveries, but requires the development of automated image analysis tools capable of reliably classifying astronomical objects captured in photographs. The National Center for Nuclear Research (NCBJ) has already made a contribution in this field: scientists from Świerk have created and tested a set of models built on neural networks and trained to detect strong gravitational lenses. The achievement Was presented, among others, at the WMLQ 2022 (International Workshop on Machine Learning and Quantum Computing Applications in Medicine and Physics) co-organized by NCBJ, dedicated to improving machine learning methods and their applications in physics and medicine.
„Strong gravitational lensing is so difficult to see that until five years ago, we only knew a few hundred cases in the entire cosmos,” says PhD student Hareesh Thuruthipilly (NCBJ), first author of a paper in the scientific journal Astronomy & Astrophysics. „Photographs from the sky surveys that are just beginning should increase this number to hundreds of thousands within a decade. However, there is a condition: tools that will optimize the work of astronomers must be developed. We demonstrate that our theoretical models and software are already capable of reliably detecting candidates for strong gravity lenses."
Gravitational lenses are a consequence of general relativity, in which mass is one of the physical quantities capable of curving space-time. The paths of photon motion, rectilinear in flat space-time, bend in space-time around a large mass, which gives the observer the impression that they came from slightly different directions than the original one.
An ordinary focusing lens deflects a light ray the farther it is from its optical axis. A gravitational lens works differently: the deflection of the light beam is greater the closer it is to the center of the lens. This feature causes the images of lensed objects to blur into more or less bent streaks. In the optimal setting, when, from the viewer’s point of view, the lens focuses light rays passing through it on all sides, the image of the lensed object will be stretched into a circle called the Einstein’s ring. The influence of low-mass objects on the shape of space-time is negligible. However, when a galaxy with a mass of many billions of solar masses becomes the lensing object, spectacular views can be expected. However, in practice, detection is so difficult, that the first gravitationally lensed object Was not spotted until 1979.
„Only one massive galaxy in ten thousand creates images of lensing,” shows the scale of the challenges Thuruthipilly, MSc. „The shapes of these pictures are unusual, and the long distance makes the photos small and not the best brightness. Moreover, in the overwhelming number of cases, the orientation of the lensing galaxy and the object behind it is suboptimal, and only bits of streak can be seen. As if the problems weren’t enough, sometimes not one galaxy is involved in lensing, but several galaxies, which results in additional image distortions."
In order to determine the optimal methods for the detection of gravity lenses, the NCBJ team prepared five models built on relatively simple neural (convolutional) networks and 21 models operating on more complex networks (with a self-attention mechanism). Each model Was trained separately on 18,000 images of simulated gravity lenses. Ultimately, the effectiveness of the network Was checked on computer-generated one hundred thousand photos from the Bologna Lens Challenge database, to make it difficult to supplement with actual photos from the Kilo Degree Survey (KiDS).
„Self-attentive neural networks did much better” – says PhD Adam Zadrożny from the NCBJ Astrophysics Department. „With just three million parameters, they achieved results comparable to those of convolutional networks with 23 million parameters. Identification of the candidates Was correct in more than nine out of ten cases. The results of our work therefore suggest that when it comes to detecting powerful gravitational lenses, the future belongs to self-attentive models."
Detecting a large number of strong gravitational lenses is important in determining the applicability of general relativity and in studying the evolution of the universe. Currently, a small number of such lenses does not allow them to be used to estimate the values of the most important parameters calibrating modern cosmological models. However, if automated detection methods qualitatively expand the pool of known lenses, a new source of information will open up for cosmologists.
The WMLQ 2022 (International Workshop on Machine Learning and Quantum Computing Applications in Medicine and Physics) workshops are dedicated to issues related to machine learning and quantum computing and the possibilities of their use in physics and medicine. The workshops are held in Warsaw from 13 to 16 September at the seat of the Polish Chamber of Commerce. The event is organized by the National Center for Nuclear Research in cooperation with scientists from the Jagiellonian University and the University of Vienna. More information: https: //events. ncbj. gov. pl/event/141/
„Finding strong gravitational lenses through self-attention” H. Thuruthipilly, A. Zadrożny, A. Pollo, M. Biesiada Astronomy & Astrophysics 664, A4 (2022) DOI:https: //doi. org/10.1051/0004-6361/202142463
Photo 1. Examples of strong gravitational lenses photographed by the Hubble telescope. In the lower left year there is a clear Einstein ring. (Sources: Hubble / NASA / ESA)


Quantum computers can perform certain kinds of optimization problems much faster than classical computers. One example is finding the ground state of a quantum system, which can be used to optimize the performance of a quantum device.
A quantum computer is a computer that uses quantum mechanics to store and process information. The basic principle behind quantum computing is that a quantum bit (qubit) can represent a zero and a one at the same time, and that quantum computers can exploit this fact to solve certain problems much faster than classical computers.
In recent years, there has been a lot of interest in using quantum computers for optimization problems. Optimization problems are problems where we are trying to find the best possible solution from a set of possible solutions. For example, we might want to find the shortest route between two cities, or the cheapest way to make a given product.
There are many different algorithms that have been developed for quantum computers that can be used to solve optimization problems. For now, let's focus on one particular algorithm, called the quantum approximate optimization algorithm (QAOA).
The QAOA is a quantum algorithm that can be used to find the minimum of a function. It works by first preparing a special state called a superposition, which is a combination of all the possible solutions to the problem. The algorithm then uses a series of quantum operations to find the solution that has the lowest energy.
The QAOA has been used to solve a number of different optimization problems, including the traveling salesman problem and the knapsack problem. In this example, we will use the QAOA to solve a simple optimization problem called the maximum cut problem.
The maximum cut problem is an optimization problem where we are given a graph, and our goal is to find the largest possible set of edges that can be cut from the graph without disconnecting it. For example, consider the following graph:
If we wanted to cut as many edges as possible from this graph, we could cut the edges highlighted in red, which would disconnect the graph into two pieces. In this case, the maximum number of edges that can be cut is four.
The maximum cut problem is a difficult problem to solve, but it can be solved using the QAOA.
QAOA is a heuristic algorithm, meaning that it is not guaranteed to find the optimal solution, but it can often find very good solutions.
QAOA works by encoding the problem into a quantum state, and then using a series of unitary operations to evolve the state. The final state is then measured, and the result is the solution to the problem.
There are a few different ways to encode the problem into a quantum state. One common way is to use a Hamiltonian that encodes the constraints of the problem. For example, if the problem is to find the shortest path between two points, the Hamiltonian would encode the constraint that the path must be a certain length.
Once the Hamiltonian is encoded, the QAOA algorithm proceeds in two steps. In the first step, called the "preparation step", a unitary operation is applied to the state that creates a superposition of all the possible solutions. In the second step, called the "evolution step", a series of unitary operations are applied that depend on the Hamiltonian. These operations cause the state to evolve in such a way that the solutions that are "closer" to the optimum are more likely to be measured.
Finally, the state is measured, and the result is the solution to the problem.
Maxcut is a problem in graph theory that seeks to find the maximum number of edges that can be cut from a given graph. It is a NP-hard problem, meaning that it is believed to be computationally intractable. However, recent advances in quantum computing have led to the development of algorithms that can solve Maxcut on a quantum computer in polynomial time.
The algorithm that we will use to solve Maxcut on a quantum computer is called the Quantum Approximate Optimization Algorithm (QAOA). QAOA is a variationally algorithm that uses a quantum computer to find the approximate solution to an optimization problem. In order to use QAOA to solve Maxcut, we first need to encode the graph into a quantum state. This can be done using the well-known Ising model.
The Ising model is a model of a system of spins that interact with each other via the Ising interaction. In our case, the spins will represent the vertices of the graph, and the Ising interaction will represent the edges of the graph. We can then use QAOA to find the maximum number of edges that can be cut from the graph, by finding the configuration of spins that minimizes the Ising interaction.
It should be noted that QAOA is not a perfect algorithm, and it will not always find the optimal solution to the Maxcut problem. However, it is a very powerful algorithm that can find very good solutions to Maxcut in polynomial time.
In general, the optimization problem can be expressed as follows:
min x f ( x )
g i ( x ) = 0 , i = 1 , 2 , … , m
h j ( x ) ≤ 0 , j = 1 , 2 , … , p
where x is the decision vector to be optimized, f ( x ) is the objective function, and g i ( x ) and h j ( x ) are the constraint functions.
The quantum algorithm for solving optimization problems is to encode the objective function and the constraint functions into a quantum state, and then use a quantum circuit to search for the optimal solution through quantum interference. The quantum state encoding objective function and constraint functions is usually expressed as follows:
| ψ ⟩ = a | x ⟩ + ∑ i b i | g i ( x ) ⟩ + ∑ j c j | h j ( x ) ⟩
where the superposition | x 〉 of all potential solutions is usually the leading term to be optimized, and | g i ( x ) 〉 and | h j ( x ) 〉 are the superposition of all solutions that violate the constraint conditions. The coefficient a is set to 1 to ensure that all solutions are encoded in the quantum state, and the coefficients b i and c j can be set to 0 or 1. In the quantum optimization algorithm, the quantum state is evolved by a quantum circuit, which is composed of a unitary operator U and a measurement operator M. The operation of the quantum circuit is as follows:
U | ψ ⟩ → | ψ ′ ⟩ = U | ψ ⟩
M | ψ ′ ⟩ → | ψ ′ ′ ⟩ = M | ψ ⟩
U is a unitary operator composed of many basic gates, and M is a general measurement operator. The general unitary operator U can be expressed as follows:
U = e − i α X Δ t e − i β Z Δ t e − i γ Y Δ t
where X, Y, and Z are the Pauli operators, and X and Y correspond to the constraint functions g i and h j . The quantum state | ψ ′ ⟩ after evolution by the quantum circuit U is given by:
| ψ ′ ⟩ = a | x ⟩ + b × e − i Δ t ( β | 0 ⟩ + γ | 1 ⟩ ) + c × e − i Δ t ( β | 1 ⟩ − γ | 0 ⟩ )
From Equation (8), we can see that, for the quantum state | ψ ′ ⟩ , the leading term | x 〉 is only evolved by the unitary operator U, and all other terms are evolved by the unitary operator U multiplied by a phase factor. The different phases of the terms cause interference between the terms, and the terms that are less beneficial to the optimization are partially canceled out. Therefore, after the quantum state | ψ ′ ⟩ is evolved by the quantum circuit, the probability of measuring the quantum state | ψ ′ ⟩ is proportional to the objective function f ( x ) , which is expressed as:
P ( x ) = | ‖ ψ ′ ⟩ | 2 ∝ f ( x )
The above equation shows that the probability of measuring the quantum state | ψ ′ ⟩ is proportional to the objective function f ( x ) . Therefore, the objective function can be optimized by repeatedly measuring the quantum state | ψ ′ ⟩
Optimizing the layout of a quantum circuit
Minimizing the number of quantum gates in a quantum circuit
Minimizing the number of quantum operations in a quantum algorithm
Reducing the error rate of a quantum computer
Improving the fidelity of a quantum state
Optimizing the control of a quantum system
Quantum computers can be used to optimize the schedule of a production line. This can lead to a significant reduction in manufacturing costs.
Quantum computers can be used to optimize the management of supply chains. This can lead to a significant reduction in inventory levels and an improvement in customer satisfaction.
Quantum computers can be used to optimize quality control procedures. This can lead to a significant reduction in product defects.
If you're looking for a way to solve optimization problems on a quantum computer, look no further! Our quantum computer can help you solve optimization problems quickly and efficiently. Contact us today to learn more about how our quantum computer can help you solve optimization problems.


There are many different models that we can use to describe how particles interact with each other in the quantum world. We can also refer to these models as systems. A system is a set of parts that form a complex whole and has order to it.One of these systems is a two-level or two-state system. This system is sometimes abbreviated as a TLS.
A simple way of picturing this type of system is a coin. A coin is a single object with two sides to it. In the quantum world, the two sides of the coin would have two possible quantum states. A quantum state is a state of a quantized system that is described by a set of quantum numbers. A quantum number is a number that expresses the value of some property of a particle which occurs in the quanta
There are several examples of these systems in the quantum world:
Spin. Spin is one of the four basic quantum numbers. It is the intrinsic angular momentum It defines the spin given to a particle. For the two level system, spin can exists as counter clockwise and clockwise. It can have a value of either +1/2 or -1/2. There is a special name given to these type of particles. They are called fermions. Fermions obey the Pauli Exclusion Principle. This means that no two particles in the same energy level have the same properties or states. Think about the coin, it has a head on one side and a building(or eagle) on the other side.. There are no two same images per coin. This is the same with spin as a two-level system. One particle has a -1/2 spin while the other particle has a +1/2 spin. Protons, neutron, electron, neutrinos, and quarks are all fermions.
The transition of an atom from an excited state to a ground state . This is not necessarily a quantum system.Because photons are involved, this can be classified as a quantum system and be called an “atom-light” interaction. Using the coin, you have the excited state on one side and the ground state on the other side. The excited state is where the atom jumps to when energy is added. The ground state is the lowest energy level of the atom.
There are two processes that happen between the ground state and the excited state. These processes are absorption and emission. Absorption happens when the atom absorbs a photon . this causes the atom to become excited. Emission happens when the atom falls to ground state and releases a photon. There are actually two types of emission. There is stimulated emission and spontaneous emission. An example of spontaneous emission would be radioactive decay. An example of stimulated emission is a laser.
The difference between the two types of emission is that stimulated emission requires an induced electromagnetic field. This means that an electromagnetic field has to be introduced to the system to cause emission . Spontaneous emission, on the other hand, occurs naturally. With our coin, we can imagine that the coin has been forced to spin or is infinitely flipping, this action demonstrates how absorption and emission are constantly occurring.
The ammonia molecule. The nitrogen of ammonia has two molecular states. These states are “up “and “down”. Once again, on one side of the coin, you have “up” and on the other, you have “down”.These two states are non-degenerate. When something is non-degenerate, it does not have the same quantum energy level. In this situation, when excitation of the molecule happens, vibration is caused by the absorption and re-emission of photons.
This is similar to tossing a slinky back and forth in your hand.This quantum phenomena allows the ammonia molecule to have its pyramidal shape and allows ammonia to be used a source for a special type of a laser called a “maser”.MASER stands for Microwave Amplification of Stimulated Emission of Radiation.
The qubit. The qubit is used in quantum computing. Like the bit that is used in regular computing, the qubit is the unit of quantum information used in quantum computing. Unlike the bit, the qubit can have a 0 and 1 at the same time. A common example of the two states used in the qubit is polarization. On one side of the coin, there is vertical polarization and on the other, horizontal polarization. You have the value of 0 and perhaps horizontal polarization. While, on the other side, you have the value of 1 and vertical polarization.
The qubit reveals an interesting property about our quantum coin. This property is called superposition. This basically means that two states are existing at the same time. This is also called entanglement. Entanglement is when collective properties are shared. In this case, the collective or common property is polarization; vertical and horizontal.
The doublet. Doublets are spectral lines of an ionized gas that have been split into two lines under the influence of a magnetic field. The doublet would have +1/2 on one side of the coin and -1/2 on the other side of the coin. The doublet reveals another unique feature about our quantum coin. This feature is called rotational symmetry. This means that , regardless of how you rotate the coin, the value is still ½.
The concept of the two-level or two-state quantum system is being studied more as researchers seek to refine the idea of quantum computing. Though there are systems other than the qubit. The other systems have helped researchers understand how to manipulate and develop the qubit.


Quantum computers can lead to breakthroughs in a wide variety of subject areas because they offer a computational strength we’ve never seen before. However, not all problems are favorable for a quantum computer. In order to identify which problems make good candidates, it’s important to have an understanding of how a quantum computer solves problems.
While quantum computers can offer an exponential boost in computational power, they can’t be programmed in the same way as a classical computer. The instruction set and algorithms change, and the resulting output is different as well. On a classical computer, the solution is found by checking possibilities one at a time. Depending upon the problem, this can take too long. A quantum computer can explore all possibilities at the same time, but there are a few challenges. Getting the right answer out of the computer isn’t easy, and because the answers are probabilistic, you may need to do extra work to uncover the desired answer.
For example, assume you wanted to page-rank the internet. To do so, the process would require loading every single page as input data. On a classical machine you would create a computation that gives you the page rank of each page, but this takes time and a significant amount of hardware. With a quantum computer, computation is exponentially faster than on classical hardware. But the caveat is that with quantum, your result will typically be the page rank of one page. And then you’d have to load the whole web again to get another, and do it again to get another, and continue until you eventually have the page rank for the entire internet. Because you have to load everything each time, the exponential speedup is lost. This example would not be favorable for quantum computing.
To solve any problem, you’ll have input, computation, and output.
- Input – The data required to run the computation
- Computation – The instructions given to the computer to process the data
- Output – The useful result received from the computation
Instead of returning the entire quantum state, a quantum computer returns one state as the result of a computation. This unique characteristic is why we write the algorithm in such a way that produces the desired answer with the highest probability. For this reason, problems that require a limited number of values are more applicable.
The amount of input data is also a consideration. As input data increases, either the number of qubits or the amount of work to ‘prepare’ the data grows quickly. Problems with highly compressed input data are more much more favorable.
What types of problems are ideal challenges for a quantum computer? Quantum computers are best-suited for solving problems with a limited volume of output, and—ideally—those with a limited amount of input. These restrictions might lead you to assume that the scope of what quantum computers can do is narrow, but the exact opposite is true. Quantum computers provide a level of computational power that allows us to tackle some of the biggest challenges we face. The nuance is in framing problems in a way that makes them solvable. Here are some great examples of how a quantum computer can be used to address some of today’s biggest challenges.
Modelling molecules is a perfect application for quantum computing. In Richard Feynman’s own words, “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical, and by golly it’s a wonderful problem, because it doesn’t look so easy.”
While we have an accurate understanding of organic molecules—those with S and P orbitals—molecules whose orbitals interact with each other are currently beyond our ability to model accurately. Many of the answers we need to address significant issues, such as world hunger and global warming, come by way of understanding these more difficult molecules. Current technology doesn’t allow us to analyze some of the more complex molecules, however, this is an excellent problem for a quantum computer because input and output are small. There’s a unique approach in quantum computing where, instead of loading the input data, you’re able to encode it into the quantum circuit itself. Modelling molecules are an example of this; the initial positions of the electrons would be the input—also referred to as ‘preparation’—and the final positions of the electron would be the output.
Modelling materials is essentially in the same problem class as modelling molecules, which means quantum computers are also helpful in identifying new possibilities in material science. The ability to develop high-temperature superconductors is a great example. We currently lose around 15% of the power in the energy grid every year due to the resistance in the wires transporting the electricity. Finding a material that can transmit energy without heating up the wires requires modelling properties of materials, a process very similar to modelling molecules. Again, this precise focus has a minimal amount of input and a highly focused output—both great candidates for quantum computing. In addition, materials have a regular structure with (mostly) local interactions making them generally easier to model than chemicals on a quantum computer.
Many cryptosystems are built using math problems more difficult than a classical computer is able to solve. However, a quantum computer has the computational ability to find solutions to the cryptographic algorithms in use today. Cryptographic problems that use factoring are excellent examples of problems that can be solved with a quantum computer because both the input and output are each a single number. Note that the numbers used in the key are huge, so a significant amount of qubits are needed to calculate the result. A quantum computer’s ability to solve cryptographic algorithms is an issue we take extremely seriously at Microsoft, and we are already working on quantum-safe cryptography protocols to replace those which will be vulnerable to quantum attacks.
Machine learning and optimization
In general, quantum computers aren’t challenged by the amount of computation needed. Instead, the challenge is getting a limited number of answers and restricting the size of the inputs. Because of this, machine learning problems often don’t make for a perfect fit because of the large amount of input data. However, optimization problems are a type of machine learning problem that can be a good fit for a quantum computer.
Imagine you have a large factory and the goal is to maximize output. To do so, each individual process would need to be optimized on its own, as well as compared against the whole. Here the possible configurations of all the processes that need to be considered are exponentially larger than the size of the input data. With a search space exponentially bigger than the input data, optimization problems are feasible for a quantum computer.
Additionally, due to the unique requirements of quantum programming, one of the unexpected benefits of developing quantum algorithms is identifying new methods to solve problems. In many cases, these new methods can be brought back to classical computing, yielding significant improvements. Implementing these new techniques in the cloud is what we refer to as quantum-inspired algorithms.
Quantum computing brings about a paradigm shift in multiple ways: Not only will quantum computing provide access to new levels of computational ability, but it will also inspire new ways of thinking. For a quantum computer to solve some of our biggest challenges, we have to understand how to frame the problem. As we look at problems in new ways, this shift can, in turn, bring new ideas to how we approach classical computation as well. With more and more individuals considering problems from different angles, more and more ideas and solutions will result. Luckily, you don’t have to wait until quantum computers are readily available to begin considering problems in new ways—you can start today by learning quantum development.
As you dive into the world of quantum development, you’ll practice your ability to think about problems in new ways, get familiar with programming a quantum computer, and even simulate your work so that you’ll be ready once quantum computers are made available.
Get started today with the Microsoft Quantum Development Kit.


From the National Institute of Standards and Technology to your home. Learn about cutting edge random number generators in this hands-on lab.
What you’ll learn:
How to entangle two magnets and recreate a quantum entangled system of random number generation.
Random numbers are difficult to create, but are necessary to safegaurd our personal information, online passwords, and electronic messages.
While we can’t create our own truly random number generator in our living room, we can learn about the science of quantum entanglement and generate our own random numbers in a project that closely mimics that of NIST. Kids will learn what entanglement means, how binary is used in quantum entanglement, and how binary numbers are converted into digits that we recognize.
Add depth to this project by reading our interview with the researcher Dr. Peter Bierhorst!
A hands-on demonstration of quantum entanglement
When it comes to the world of quantum mechanics it is difficult to find a basis of understanding in the real world. Why? Because the quantum world is not only very tiny, it is also very weird – for example, is a photon of light a particle? Is it a wave? Is it both…at the same time?!? What happens if a light particle is whizzing around at mind-boggling speeds near the speed of light?
The New World of Mr. Tompkins by George Gamow is one of my all-time favorites for mind experiments when it comes to the quantum world and a great read if your learner wants to dive deeper.
In this project, we will take a look at how two entangled photons can create the ultimate random number generator.
1. Make your entangled photons.
In this project, our ‘photons’ will be round magnets that snap together. Each magnetic pair creates one set of entangled ‘photons’, one spin up, and one spin down. This is a great analogy because magnets have two states, North, and South.
Take two magnets that are snapped together, break them apart, and write ‘up’ on the internal face of one magnet, and ‘down’ on the other. When the magnets are together we should not be able to see which magnet has which state – just as with entangled photons we cannot see which photon is spin up or spin down until we look at the state of one.
What happens in the lab?
In the lab entangled photons are created by pulsing a laser beam at a single, high energy photon inside a crystal. This photon splits into two lower energy photons that are ‘entangled’. For example, a blue photon could be split into two red photons inside the crystal. These red photons are now bound sisters with entangled energies, momenta, and polarization. In these experiments, we use the polarization to generate our signal of 0 or 1. This is because a photon can have two polarizations, up or down.
What happens in the lab?
In the lab, a stream of entangled photons is created as a laser beam is continuously shined upon the producing crystal.
2. Run your experiment
We can’t put a bunch of our entangled photons in a bag, shake it up, and draw them out. Can you think why?
Our entangled photons are really magnets. If you put a whole bunch of magnets in a bag together they will just snap together and make a long brick!
To run our experiment we will put a set of entangled magnets into a small cup, shake it and remove it. The two magnets are identical on the outside – that is, you can’t tell which is spin up and which is spin down by looking at the set together (with the writing on the inside).
Open the magnets face down so you can’t see which magnet (or photon) is which. Place one on your paper template, and the other goes to a nearby student.
3. Keep running your experiment.
We need 4 bits of data to make the numbers 0 through 15. That means we need 4 magnets to be shaken and drawn before we can convert the spins to binary and the binary to a number.
Shake an entangled set of magnets, draw them out, and place one magnet face down on each paper. Repeat this until you have filled the four spaces created for the magnets.
What happens in the lab?
The two entangled photons are separated from each other using a beam splitter. A beam splitter is an optical device that can split a beam into two pieces (these can be equally size beams or not, depending on the beam splitter you choose).
4. Flip over your magnets.
Up until now, no one would have been able to tell you if your magnets are spin up and spin down. Your partner will have the opposite set of magnets. Think about it – if you have an entangled magnet that is spin up, what entangled magnet does your partner have?
Flip over your entangled magnets and record in the circles if your magnets were spin up (with an up arrow), or spin down (with a down arrow).
Below each magnet, there is a line. Write either a 1, if your magnet in the position was spin up, or a 0, if your magnet was spin down.
5. Convert to ASCII
You now have a series of 4 bits (or 4 zeroes and ones). Use the chart on the left-hand side of the page to discover what number that binary code is represented by. Write this number in the box on the right.
Counting in binary
Binary is defined as having two states, here a 0 or a 1. If we want to count in binary we need to do so in something called base-2. Here the number 2 is really represented by the number 10. What?!? How is that possible? In base 2 each digit holder represents a 2^x. So the first digit is 2^0 which is one, and the second digit is 2^1, which is 2!
6. Re-entangle your photons
We will need to run our experiment more to be able to create a string of numbers. Use the set of magnets shared between you and your partner to entangle the four sets of magnets, with the writing (spin up or spin down) on the inside, invisible to observers.
7. Run the experiment again.
Go through the process of shaking the magnets, then separating them face down, one on your paper, one on your partner’s paper. Once you have another set of four magnets face down you can flip them over, record the results and convert the binary to ASCII getting yet another number.
Repeat the process until you have four sets of numbers converted from binary.
8. Find your random number
To find your random number string you will transcribe the numbers you found with your entangled photons. For example, if your first experiment gave you the number 5, the second 14, the third 2, and the fourth 4 your random number would be: 51424.


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Hitachi Cambridge Labs Tackles the Challenge of Building a Large-Scale Quantum Computer
Quantum computers promise to have a major positive impact on society. How else will we have the capability to process the exabytes of data and turn it into useful information to develop new cures for cancer, improve security, and boost artificial intelligence. However, building the hardware that will enable that paradigm change is one of the greatest technological challenges facing humanity.
In 2019, which seems like a lifetime ago due to the Pandemic, Google announced that their quantum computer was the first to perform a calculation that would be practically impossible for a classical supercomputer. This is known as Quantum Supremacy. Its quantum computer, known as “Sycamore”, carried out a specific calculation that is beyond the practical capabilities of regular, ‘classical’ machines. They estimate that the same calculation would take even the best classical supercomputer 10,000 years to complete. However, the problem that was solved was not a practical application and was more proof of concept.
Quantum computers work in a fundamentally different way from classical computers where a classical bit is either a 1 or a 0. In a Quantum computer, a quantum bit, or qubit, can exist in multiple states at once. This capability allows for the construction of an exponentially large computing space with only a linear number of resources, Qubits, making it exponentially more powerful than conventional computing for a specific set of tasks. When qubits are inextricably linked, physicists can, in theory, exploit the interference between their wave-like quantum states to perform calculations that might otherwise take millions of years.
The one of the drawbacks with current Quantum computers is noise because it can make qubits change state at times and in ways that programmers did not intend, leading to computational errors. Most interactions with the surrounding environment, such as charge instabilities and thermal fluctuations, are sources of qubit noise. All of them can compromise information. The algorithms used by quantum computers must spend resources, qubits, to correct for this noise. Current systems are still relatively simple and as such their performance is far from what supercomputers can achieve. The first wave of development, known as noisy intermediate-scale quantum (
) technology, is being led by two key technologies: ion traps and superconductors. Ion traps use single charged atoms trapped in electromagnetic fields as qubits. Superconductors are electrical resonators that can oscillate in two different manners simultaneously. Ion traps are being explored by companies like IonQ, Inc., Alpine Quantum Technologies, GmbH., and Honeywell International Inc., whereas superconductors are being worked on by International Business Machines Corporation (IBM), Google LLC, Alibaba Group Holding Limited, Intel Corporation, and Rigetti & Co, Inc. Systems using NISQ technology have been successfully demonstrated with up to a few tens of qubits working simultaneously. The power of a quantum computer is rated by the number of qubits that it manages. Google’s Sycamore had 64 qubits.
While Google was able to achieve Quantum Supremacy with Sycamore, the problem that was solved has little practical application. In order to run the quantum algorithms that can make a real impact in society would require orders of magnitude of qubits. Predictions estimate that 10
qubits are needed to run a simulation of a simple material, and 10
qubits for an arbitrarily complex one. Scaling up to such a large number of qubits is the greatest challenge to overcome in order to fulfill the promise of quantum computing. Ion trap and superconducting qubits offer limited prospects for scalability beyond the NISQ era with current qubit densities of just 1 and 100 qubits/cm
respectively. This translates into machines the size of a whole room or even a football stadium.
The Hitachi Cambridge Laboratory (HCL) is developing a new technology that has the potential to solve the scaling problem, making it a leading hardware candidate for building the first general-purpose quantum computer. (Hitachi established the Hitachi Cambridge Laboratory (HCL) in collaboration with the Cavendish Laboratory of the University of Cambridge in 1985)
HCL is using silicon transistors, the omnipresent device in all microprocessors, to make scalable qubits. One of the advantages of silicon is that it offers a relatively
where spins can retain their quantum nature. This means that less resources will be required for error correction.
The biggest attraction of silicon-based quantum processors is the ability to leverage the same technology that the microchip industry has handled for the past 60 years. This means manufacturers can expect to benefit from previous multibillion-dollar infrastructure investments, keeping production costs low. By using silicon as a basis for a quantum computer means that all the clever engineering and processing that went into developing modern classical microelectronics – from dense device packaging to integrated interconnect routing – can be adapted and used to build quantum devices. By using the same technology that is used in conventional computing, HCL aims to deliver a cost-effective chip-size solution with an unparalleled qubit density of 10
, one that could be manufactured in large quantities in silicon foundries. The proposed solution will open up quantum computing to many new companies by transferring the successful fabless business model from the microelectronics industry to the field of quantum nano electronics.
At the Hitachi Cambridge Laboratory, the Quantum Information Team is tackling this challenge using complementary metal-oxide semiconductor technology, the same transistor technology used in all conventional information processing devices, such as mobile phones, computers, and cars. By using the spin of single electrons trapped in these transistors at very low temperatures, the Quantum Information Team aims to deliver a scalable solution while also reducing the cost of development.
For more information on this approach read the following article from Phys.org:
Quantum computers could arrive sooner if we build them with traditional silicon technology
Moore’s Law Is Replaced by Neven's Law for Quantum Computing
Social Innovation Drives Computing Innovations for Powering Good
Should You Be Concerned With Quantum Computing in 2020?
A Look Into The Future - Ten Years Out
Preparing for Post Quantum Encryption
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Within days of each other back in 1998, two teams published the results of the first real-world quantum computations. But the first quantum computers weren’t computers at all. They were biochemistry equipment, relying on the same science as MRI machines.
You might think of quantum computing as a hyped-up race between computer companies to build a powerful processing device that will make more lifelike AI, revolutionize medicine, and crack the encryption that protects our data. And indeed, the prototype quantum computers of the late 1990s indirectly led to the quantum computers built by Google and IBM. But that’s not how it all began—it started with physicists tinkering with mathematics and biochemistry equipment for curiosity’s sake.
“It was not motivated in any way by making better computers,” Neil Gershenfeld, the director of MIT’s Center for Bits and Atoms and a member of one of the two teams that first experimentally realized quantum algorithms, told me. “It was understanding whether the universe computes, and how the universe computes.”
Computers are just systems that begin with an abstracted input and apply a series of instructions to it in order to receive an output. Today’s computers translate inputs, instructions, and outputs into switches, called bits, that equal either zero or one and whose values control other switches. Scientists have long used computers to simulate the laws of physics, hoping to better understand how the universe works—for example, you can simulate how far a ball will go based on where it starts and how fast it is thrown.
But using bits to simulate physics didn’t make much sense to famed physicist Richard Feynman, since the laws of physics at the smallest scale are rooted in a set of rules called quantum mechanics. “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical,” Feynman famously said at a 1981 conference.
A small band of scientists theorized about using these rules to create better simulations during the decade following. Instead of switches, their quantum simulation’s bits are the dual particle-waves of quantum mechanics. Each individual quantum bit would still be restricted to two choices, but as waves, they can take on either of these states simultaneously with varying strengths, interacting with one another like ocean waves—either amplifying the strength of certain combinations of choices or canceling combinations out. But once you measure these quantum bits, each one immediately snaps into a single state. Those strengths, or amplitudes, translate into the probability of ending up with each outcome.
Through the early 1990s, “people thought that quantum computing was essentially mad, and many had [supposedly] proved that it could never work,” Jonathan Jones, a physics professor at the University of Oxford who was one of the first to run quantum algorithms on a real quantum computer, told me. Mainly, people thought it was just a curiosity created by theoretical physicists who wondered whether they could understand the universe itself in the language of computers. It also seemed that the finickiness of quantum mechanics—the fact that any slight jostle could quickly snap fragile qubits into single-state particles—would make them impossible to realize.
Two milestones busted those ideas. Physicist Peter Shor unveiled an algorithm in 1994 that showed that a computer based on qubits could factor large numbers near-exponentially faster than the best bit-based algorithms. If scientists could invent a quantum computer advanced enough to run the algorithm, then it could crack the popular modern-day encryption systems based on the fact that it’s easy for classical computers to multiply two large prime numbers together but very, very hard to factor the result back into primes. The second turning point came in the mid-90s when physicists started developing error correction—the idea of spreading a single qubit’s worth of information across a series of correlated qubits to lessen the errors.
But even after that, the field was small, and the physicists we spoke to discussed conferences at which most of the world’s quantum computing scientists could fit in a room together. Quantum computing forerunners like Charlie Bennett, Isaac Chuang, Seth Lloyd, and David DiVincenzo were coming up with lots of new ideas that percolated quickly through the community. Almost simultaneously, several independent groups realized that the medical and biochemistry industry had long been using a quantum computer in research—Nuclear Magnetic Resonance, or NMR spectrometers.
NMR, the technology behind MRI, most commonly consists of a molecule of interest dissolved in a liquid solvent, placed in a strong magnetic field. The nuclei of the atoms in these molecules have an innate quantum mechanical property called “spin,” which is essentially the smallest unit of magnetic information, and can be in either of two states, “up” or “down.” These spins align with the direction of the field.
In medicine and biochemistry, scientists will hit the molecules with additional smaller oscillating magnetic fields, called radio-frequency pulses, causing the atoms to release characteristic signals that offer physical information about the molecule. Magnetic resonance imaging or MRI machines instead use this signal to create a picture. But the physicists realized that they could treat certain molecules in this magnetic field as quantum computers, where the nuclei served as qubits, the spin states were qubit values, and the radio-frequency pulses were both the instructions and controllers. These are the operations of quantum computers, also called logic gates as they are in classical computers.
“In a sense, NMR had actually been ahead of other fields for decades,” said Jones, a biochemist who teamed up with physicist Michele Mosca to perform one of the first quantum calculations. “They had done logic gates back in the 70s. They just didn’t know what they were doing and didn’t call it logic gates.”
Physicists including Chuang, Gershenfeld and David Cory released papers detailing how to realize these devices in 1997. A year later, two teams, one with Jones and Mosca and another with Chuang and Mark Kubinic, actually performed the quantum algorithms. The former consisted of cytosine molecules where two hydrogen atoms had been replaced with deuterium atoms—hydrogen with a neutron. The latter used chloroform molecules. They prepared the qubits into initial states, performed a computation by applying a specially crafted radio-frequency pulse, and measured the final states.
We don’t often hear about NMR quantum computers today, because even then, physicists knew that the technique had its limits, something all of the physicists I spoke with mentioned. More qubits would mean more specially crafted molecules. The techniques relied on special workarounds such that each additional qubit would make it harder to pick the signal out of the background noise. “No one thought it would ever be used for more than a demonstration,” Jones said. They just weren’t scalable beyond a few qubits.
Still, they were important experiments that physicists still talk about today. NMR machines remain crucial to biochemistry and still have a place in quantum technology. But this early work has left an important, indirect impact on the field. The science behind those radio-frequency pulses has lived on in the quantum computers that Google, IBM, and other companies have built in order to control their qubits. Quantum computers running Shor’s algorithm are still decades away even today, but companies have begun unveiling real devices with dozens of qubits that can perform rudimentary and clearly quantum calculations.
Charlie Bennet, IBM fellow and quantum computing veteran, explained that these experiments weren’t enormous discoveries on their own, and indeed the NMR community had been advancing its own science and radio-frequency pulses before quantum computing came along. The physicists I spoke with explained that nobody “won” and there was no “race” back in the late 1990s. Instead, it was a transition point along a road of incremental advances, a point in time in which groups of scientists all came to realize that humans had the technology to control quantum states and use them for computations.
“Science is always like that. The whole evidence is more important than almost any one paper,” said Bennett. “There are important discoveries—but these rarely occur in single papers.”


Walk into a quantum lab where scientists trap ions, and you'll find benchtops full of mirrors and lenses, all focusing lasers to hit an ion “trapped” in place above a chip. By using lasers to control ions, scientists have learned to harness ions as quantum bits, or qubits, the basic unit of data in a quantum computer. But this laser setup is holding research back — making it difficult to experiment with more than a few ions and to take these systems out of the lab for real use.
Now, MIT Lincoln Laboratory researchers have developed a compact way to deliver laser light to trapped ions. In a recent paper published in Nature, the researchers describe a fiber-optic block that plugs into the ion-trap chip, coupling light to optical waveguides fabricated in the chip itself. Through these waveguides, multiple wavelengths of light can be routed through the chip and released to hit the ions above it.
“It's clear to many people in the field that the conventional approach, using free-space optics such as mirrors and lenses, will only go so far,” says Jeremy Sage, an author on the paper and senior staff in Lincoln Laboratory's Quantum Information and Integrated Nanosystems Group. “If the light instead is brought onto the chip, it can be directed around to the many locations where it needs to be. The integrated delivery of many wavelengths may lead to a very scalable and portable platform. We're showing for the first time that it can be done.”
Computing with trapped ions requires precisely controlling each ion independently. Free-space optics have worked well when controlling a few ions in a short one-dimensional chain. But hitting a single ion among a larger or two-dimensional cluster, without hitting its neighbors, is extremely difficult. When imagining a practical quantum computer requiring thousands of ions, this task of laser control seems impractical.
That looming problem led researchers to find another way. In 2016, Lincoln Laboratory and MIT researchers demonstrated a new chip with built-in optics. They focused a red laser onto the chip, where waveguides on the chip routed the light to a grating coupler, a kind of rumble strip to stop the light and direct it up to the ion.
Red light is crucial for doing a fundamental operation called a quantum gate, which the team performed in that first demonstration. But up to six different-colored lasers are needed to do everything required for quantum computation: prepare the ion, cool it down, read out its energy state, and perform quantum gates. With this latest chip, the team has extended their proof of principle to the rest of these required wavelengths, from violet to the near-infrared.
“With these wavelengths, we were able to perform the fundamental set of operations that you need to be able to control trapped ions,” says John Chiaverini, also an author on the paper. The one operation they didn't perform, a two-qubit gate, was demonstrated by a team at ETH Zürich by using a chip similar to the 2016 work, and is described in a paper in the same Nature issue. “This work, paired together with ours, shows that you have all the things you need to start building larger trapped-ion arrays,” Chiaverini adds.
To make the leap from one to multiple wavelengths, the team engineered a method to bond a fiber-optic block directly to the side of the chip. The block consists of four optical fibers, each one specific to a certain range of wavelengths. These fibers line up with a corresponding waveguide patterned directly onto the chip.
“Getting the fiber block array aligned to the waveguides on the chip and applying the epoxy felt like performing surgery. It was a very delicate process. We had about half a micron of tolerance and it needed to survive cooldown to 4 kelvins,” says Robert Niffenegger, who led the experiments and is first author on the paper.
On top of the waveguides sits a layer of glass. On top of the glass are metal electrodes, which produce electric fields that hold the ion in place; holes are cut out of the metal over the grating couplers where the light is released. The entire device was fabricated in the Microelectronics Laboratory at Lincoln Laboratory.
Designing waveguides that could deliver the light to the ions with low loss, avoiding absorption or scattering, was a challenge, as loss tends to increase with bluer wavelengths. “It was a process of developing materials, patterning the waveguides, testing them, measuring performance, and trying again. We also had to make sure the materials of the waveguides worked not only with the necessary wavelengths of light, but also that they didn't interfere with the metal electrodes that trap the ion,” Sage says.
Scalable and portable
The team is now looking forward to what they can do with this fully light-integrated chip. For one, “make more,” Niffenegger says. “Tiling these chips into an array could bring together many more ions, each able to be controlled precisely, opening the door to more powerful quantum computers.”
Daniel Slichter, a physicist at the National Institute of Standards and Technology who was not involved in this research, says, “This readily scalable technology will enable complex systems with many laser beams for parallel operations, all automatically aligned and robust to vibrations and environmental conditions, and will in my view be crucial for realizing trapped ion quantum processors with thousands of qubits.”
An advantage of this laser-integrated chip is that it's inherently resistant to vibrations. With external lasers, any vibration to the laser would cause it to miss the ion, as would any vibrations to the chip. Now that the laser beams and chip are coupled together, the effects of vibrations are effectively nullified.
This stability is important for the ions to sustain “coherence,” or to operate as qubits long enough to compute with them. It's also important if trapped-ion sensors are to become portable. Atomic clocks, for example, that are based on trapped ions could keep time much more precisely than today's standard, and could be used to improve the accuracy of GPS, which relies on the synchronization of atomic clocks carried on satellites.
“We view this work as an example of bridging science and engineering, that delivers a true advantage to both academia and industry,” Sage says. Bridging this gap is the goal of the MIT Center for Quantum Engineering, where Sage is a principal investigator. “We need quantum technology to be robust, deliverable, and user-friendly, for people to use who aren't PhDs in quantum physics,” Sage says.
Simultaneously, the team hopes that this device can help push academic research. “We want other research institutes to use this platform so that they can focus on other challenges — like programming and running algorithms with trapped ions on this platform, for example. We see it opening the door to further exploration of quantum physics,” Chiaverini says.


New research from MIT shows that graphene can effectively filter electrons according to the direction of their spin, something that cannot be done by any conventional electronic system.
Graphene has become an all-purpose wonder material, spurring armies of researchers to explore new possibilities for this two-dimensional lattice of pure carbon. But new research at MIT has found additional potential for the material by uncovering unexpected features that show up under some extreme conditions — features that could render graphene suitable for exotic uses such as quantum computing.
The research is published this week in the journal Nature, in a paper by professors Pablo Jarillo-Herrero and Ray Ashoori, postdocs Andrea Young and Ben Hunt, graduate student Javier Sanchez-Yamaguchi, and three others. Under an extremely powerful magnetic field and at extremely low temperature, the researchers found, graphene can effectively filter electrons according to the direction of their spin, something that cannot be done by any conventional electronic system.
Under typical conditions, sheets of graphene behave as normal conductors: Apply a voltage, and current flows throughout the two-dimensional flake. If you turn on a magnetic field perpendicular to the graphene flake, however, the behavior changes: Current flows only along the edge, while the bulk remains insulating. Moreover, this current flows only in one direction — clockwise or counterclockwise, depending on the orientation of the magnetic field — in a phenomenon known as the quantum Hall effect.
In the new work, the researchers found that if they applied a second powerful magnetic field — this time in the same plane as the graphene flake — the material’s behavior changes yet again: Electrons can move around the conducting edge in either direction, with electrons that have one kind of spin moving clockwise while those with the opposite spin move counterclockwise.
“We created an unusual kind of conductor along the edge,” says Young, a Pappalardo Postdoctoral Fellow in MIT’s physics department and the paper’s lead author, “virtually a one-dimensional wire.” The segregation of electrons according to spin is “a normal feature of topological insulators,” he says, “but graphene is not normally a topological insulator. We’re getting the same effect in a very different material system.”
What’s more, by varying the magnetic field, “we can turn these edge states on and off,” Young says. That switching capability means that, in principle, “we can make circuits and transistors out of these,” he says, which has not been realized before in conventional topological insulators.
There is another benefit of this spin selectivity, Young says: It prevents a phenomenon called “backscattering,” which could disrupt the motion of the electrons. As a result, imperfections that would ordinarily ruin the electronic properties of the material have little effect. “Even if the edges are ‘dirty,’ electrons are transmitted along this edge nearly perfectly,” he says.
Jarillo-Herrero, the Mitsui Career Development Associate Professor of Physics at MIT, says the behavior seen in these graphene flakes was predicted, but never seen before. This work, he says, is the first time such spin-selective behavior has been demonstrated in a single sheet of graphene, and also the first time anyone has demonstrated the ability “to transition between these two regimes.”
That could ultimately lead to a novel way of making a kind of quantum computer, Jarillo-Herrero says, something that researchers have tried to do, without success, for decades. But because of the extreme conditions required, Young says, “this would be a very specialized machine” used only for high-priority computational tasks, such as in national laboratories.
Ashoori, a professor of physics, points out that the newly discovered edge states have a number of surprising properties. For example, although gold is an exceptionally good electrical conductor, when dabs of gold are added to the edge of the graphene flakes, they cause the electrical resistance to increase. The gold dabs allow the electrons to backscatter into the oppositely traveling state by mixing the electron spins; the more gold is added, the more the resistance goes up.
This research represents “a new direction” in topological insulators, Young says. “We don’t really know what it might lead to, but it opens our thinking about the kind of electrical devices we can make.”
The experiments required the use of a magnetic field with a strength of 35 tesla — “about 10 times more than in an MRI machine,” Jarillo-Herrero says — and a temperature of just 0.3 degrees Celsius above absolute zero. However, the team is already pursuing ways of observing a similar effect at magnetic fields of just one tesla — similar to a strong kitchen magnet — and at higher temperatures.
Philip Kim, a professor of physics at Columbia University who was not involved in this work, says, “The authors here have beautifully demonstrated excellent quantization of the conductance,” as predicted by theory. He adds, “This is very nice work that may connect topological insulator physics to the physics of graphene with interactions. This work is a good example of how the two most popular topics in condensed matter physics are connected to each other.”
Reference: “Tunable symmetry breaking and helical edge transport in a graphene quantum spin Hall state” by A. F. Young, J. D. Sanchez-Yamagishi, B. Hunt, S. H. Choi, K. Watanabe, T. Taniguchi, R. C. Ashoori and P. Jarillo-Herrero, 22 December 2013, Nature.
The team also included MIT junior Sang Hyun Choi and Kenji Watanabe and Takashi Taniguchi of the National Institute for Materials Science in Tsukuba, Japan. The work was supported by grants from the U.S. Department of Energy, the Gordon and Betty Moore Foundation, and the National Science Foundation, and used facilities at the National High Magnetic Field Laboratory in Florida.