Eindhoven – August 30, 2021
While the word “quantum” has only started trending in the technology space during the last decade, many past technologies already relied on our understanding of the quantum world, from lasers to MRI imaging, electronic transistors, and nuclear power. The reason quantum has become so popular lately is that researchers have become increasingly better at manipulating individual quantum particles (light photons, electrons, atoms) in ways that weren’t possible before. These advances allow us to harness more explicitly the unique and weird properties of the quantum world. They could launch yet another quantum technology revolution in areas like sensing, computation, and communication.
What’s a Quantum Computer?
The power of quantum computers comes chiefly from the superposition principle. A classical bit can only be in a 0 or 1 state, while a quantum bit (qubit) can exist in several 0 and 1 state combinations. When one measures and observes the qubit, it will collapse into just one of these combinations. Each combination has a specific probability of occurring when the qubit collapses.
While two classical bits can only exist in one out of four combinations, two quantum bits can exist in all these combinations simultaneously before being observed. Therefore, these qubits can hold more information than a classical bit, and the amount of information they can hold grows exponentially with each additional qubit. Twenty qubits can already hold a million values simultaneously (220), and 300 qubits can store as many particles as there are in the universe (2300).
However, to harness this potential processing power, we must understand that probabilities in quantum mechanics do not work like conventional probabilities. The probability we learned about in school allowed only for numbers between 0 and 1. On the other hand, probabilities in quantum mechanics behave as waves with amplitudes that can be positive or negative. And just like waves, quantum probabilities can interfere, reinforcing each other or cancelling each other out.
Quantum computers solve computational problems by harnessing such interference. The quantum algorithm choreographs a pattern of interference where the combinations leading to a wrong answer cancel each other out. In contrast, the combinations leading to the correct answer reinforce each other. This process gives the computer a massive speed boost. We only know how to create such interference patterns for particular computational problems, so for most problems, a quantum computer will only be as fast as a conventional computer. However, one problem where quantum computers are much faster than classical ones is finding the prime factors of very large numbers.
How Quantum Computers Threaten Conventional Cryptography
Today’s digital society depends heavily on securely transmitting and storing data. One of the oldest and most widely used methods to encrypt data is called RSA (Rivest-Shamir-Adleman – the surnames of the algorithm’s designers). RSA protocols encrypt messages with a key that results from the multiplication of two very large numbers. Only someone who knows the values of these two numbers can decode the message.
RSA security relies on a mathematical principle: multiplying two large numbers is computationally easy, but the opposite process—figuring out what large numbers were multiplied—is extremely hard, if not practically impossible, for a conventional computer. However, in 1994 mathematician Peter Shor proved that an ideal quantum computer could find the prime factors of large numbers exponentially more quickly than a conventional computer and thus break RSA encryption within hours or days.
While practical quantum computers are likely decades away from implementing Shor’s algorithm with enough performance and scale to break RSA or similar encryption methods, the potential implications are terrifying for our digital society and our data safety.
In combination with private key systems like AES, RSA encrypts most of the traffic on the Internet. Breaking RSA means that emails, online purchases, medical records, company data, and military information, among many others, would all be more susceptible to attacks from malicious third parties. Quantum computers could also crack the digital signatures that ensure the integrity of updates to apps, browsers, operating systems, and other software, opening a path for malware.
This security threat has led to heavy investments in new quantum-resistant encryption. Besides, existing private key systems used in the enterprise telecom sector like AES-256 are already quantum resistant. However, even if these methods are secure now, there is no guarantee that they will remain secure in the future. Someone might discover a way to crack them, just as it happened with RSA.
Quantum Key Distribution and its Impact on the Telecom World
Given these risks, arguably the most secure way to protect data and communications is by fighting quantum with quantum:protect your data from quantum computer hacking by using security protocols that harness the power of quantum physics laws. That’s what quantum key distribution (QKD) does: QKD uses qubits to generate a secret cryptographic key protected by the phenomenon of quantum state collapse. If an attacker tries to eavesdrop and learn information about the key, they will distort the qubits irreversibly. The sender and receiver will see this distortion as errors in their qubit measurements and know that their key has been compromised.
Quantum-safe encryption will take part in people’s day-to-day lives through upgrades to laptops, phones, browsers, and other consumer products. However, most of the burden for quantum-safe communication will be handled by businesses, governments, and cloud service providers that must design and install these systems. It’s a hugely complex change that’s on par with upgrading internet communications from IPv4 to IPv6.
Even if practical quantum computers are not yet available, it’s essential to begin investing in these changes, as explained by Toshiba Chief Digital Officer Taro Shimada: “Sectors such as finance, health and government are now realizing the need to invest in technology that will prepare and protect them for the quantum economy of the future. Our business plan goes far deeper and wider than selling quantum cryptographic hardware. We are developing a quantum platform and services that will not only deliver quantum keys and a quantum network but ultimately enable the birth of a quantum internet”. Toshiba expects the QKD market to grow to approximately $20 billion worldwide in FY 2035.
How Photonics Impacts QKD
Qubits can be photons, electrons, atoms, or any other system that can exist in a quantum state. However, using photons as qubits will likely dominate the quantum communications and QKD application space. We have decades of experience manipulating the properties of photons, such as polarization and phase, to encode qubits. Thanks to optical fiber, we also know how to send photons over long distances with relatively little loss. Besides, optical fiber is already a fundamental component of modern telecommunication networks, so future quantum networks can run on that existing fiber infrastructure. All these signs point towards a new era of quantum photonics.
Photonic QKD devices have been, in some shape or form, commercially available for over 15 years. Still, factors such as the high cost, large size, and the inability to operate over longer distances have slowed their widespread adoption. Many R&D efforts regarding quantum photonics aim to address the size, weight, and power (SWaP) limitations. One way to overcome these limitations and reduce the cost per device would be to integrate every QKD function—generating, manipulating, and detecting photonic qubits—into a single chip. The further development of the integrated quantum photonics (IQP) chip is considered by many as a critical step in building the platform that will unlock quantum applications in much the same way as integrated circuits transformed microelectronics.
In the coming articles, we will discuss more how to combine photonic integration with quantum technologies to address the challenges in quantum communications.
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What is computer generation?
A computer is a machine manipulating data or information electronically. It can store, retrieve, and analyze the information. A computer can now be used to follow instructions, send email messages, play online games, and browse the internet. Editing or making spreadsheets, reports, and sometimes even videos can also be used. Yet the development of this complex structure began approximately 1940 with the very first Computer Generation and has since evolved. The computer revolution is always marked as a technological breakthrough that has fundamentally altered the unique way for computers work, culminating in ever smaller, cheaper, increasingly efficient, and much more efficient machines. Reference is often made to the development of computer technology in relation to the various types of computing devices. Computer revolution completely changed the way computers function, resulting in ever smaller, cheaper, more efficient, and much more secure computers.
The first computer generation:
Vacuum tubes were used in the first generations of computers. These computer systems made use of vacuum tubes as storage circuits and electromagnetic drums. As just a consequence they were very massive, taking up practically whole rooms and costing a lot to maintain. Those were all ineffective materials that provided a lot of temperatures, pulled enormous energy, and ultimately produced a lot of the heat that caused continuous failures. These machines of the first century focused on ‘machine language’ (that is the most simple programming language which computers use to communicate). Information was dependent on the paper tape and punches cards. Performance appeared on publish-outs. The generation’s two significant devices were the UNIVAC and ENIAC computer.
Figure: 1 Vacuum tube
The second generation of computers:
A transistor computer also referred to as a second-gen computer, is a computer that uses single transistors rather than vacuum tubes. … By 1947, the transistor’s invention drastically changed the production of computers. In television sets, phones, and computers the transistor supplemented the obsolete vacuum tube. As a consequence, computer equipment has now shrunk in scale. In 1956 the transistor had been at work on the device. Together with early developments in magnetic-core memory, transistors contributed to lighter, cheaper, more stable, and much more energy-efficient second-gen computers than their counterparts. The initial supercomputers expanded by IBM and LARC by Sperry-Rand were the very first large-scale devices to take full advantage of this transistor technique. Both built for atomic power research labs, these computers were able to manage huge amounts of data, which by atomic researchers was a skill in much availability. The computers were costly and many were too efficient for the computing wants of the business community, thereby reducing their appeal. Only two LARCs have ever been constructed; one from the Lawrence Radiation Labs in Livermore, California, going to name just after the device, and another in the United States.
Figure: 2. Transistor
The third generation of computers:
Computers of the third generation were machines that increased prevalence to the invention of the integrated circuit (IC). As we recognize them nowadays they were the very first move towards computers. Their key innovation was the use of integrated circuits, which made it possible to slim them down to be as lightweight as big toasters. Despite this, they acquired the title microcomputers because they’re very small in comparison to computers of the 2nd gen that would fill entire floors and houses. Much-known machines in this period also include the DEC PDP range and the IBM-360 series of computers. Computers quickly became much more accessible, and then developers and found it interesting have become more popular, contributing to more advances in the area of computer programming and also hardware. It was around this period that several high-level programming languages, including such C, Pascal, COBOL, and FORTRAN, began public sphere use. In this period, magnetic storage has become more common too.
Figure: 3. Integrated circuit
The fourth generation of computers:
The fourth-generation time frame was from 1971-80. The (VLSI) large scale built-in circuits are used on this gene’s computers. Such circuits have 5000 transistors as well as other components of the circuit. The computers of the fourth-generation are becoming more powerful, compact, reliable, and affordable. There are many numerous additional tools including such time-sharing, real-time networking, fourth-generation decentralized os was used. This generation uses all the high-level languages including Java, C, C++, PHP. Such machines can also be used for incorporation in the LSI (a massive scale). The fourth generation is the third generation expansion. First-generation computers covered the entire room area, but new computers will fit in the hand. This generation of computers uses microprocessor chips. In the fourth generation of computers, object-oriented programming has been used. There are different kinds of language in object-oriented programming, including Java, Visual Basic, etc. These object-oriented applications are intended to solve particular issues and need no advanced training sessions. It includes queries and substations of applications. The first business that can build the microchips was the Intel. IBM produced the first Fourth Generation home computer. Such machines had to operate a minimal amount of energy. The Computer’s fourth-generation had the first supercomputer that could reliably conduct several calculations. Such supercomputers have been used in telecommunications as well. The ability for processing expanded to many gigabytes, and even terabytes of data.
Figure: 4. Micro processor
The fifth generation of computers:
The Fifth Generation project is a major Japanese research study which aims to produce a new form of the computer by 1991. It was initially launched after much discussion about the need for considerably more accessible computers that would proliferate “like air” across in order to take advantage of the aging population and personal development among many other things. The MITI people who funded the plan must’ve had a strong marketing strategist to select the project ‘s address because it’s very title has generated a lot of excitement around the world. Computers of the 5th generation will be in the stage of development, focused on artificial intelligence. The fifth generation’s aim is to create a computer that is smart enough to learn and self-organization and can react to real language input. For this research, Quantum computing and Quantum and Nanotechnology would be used. Therefore we may assume that machines of the fifth century should have the strength of human intelligence.
Figure: 5. Artificial intelligence


Back in 1958, in the earliest days of the computing revolution, the US Office of Naval Research organized a press conference to unveil a device invented by a psychologist named Frank Rosenblatt at the Cornell Aeronautical Laboratory. Rosenblatt called his device a perceptron, and the New York Times reported that it was “the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself, and be conscious of its existence.”
Those claims turned out to be somewhat overblown. But the device kick-started a field of research that still has huge potential today.
A perceptron is a single-layer neural network. The deep-learning networks that have generated so much interest in recent years are direct descendants. Although Rosenblatt’s device never achieved its overhyped potential, there is great hope that one of its descendants might.
Today, there is another information processing revolution in its infancy: quantum computing. And that raises an interesting question: is it possible to implement a perceptron on a quantum computer, and if so, how powerful can it be?
Today we get an answer of sorts thanks to the work of Francesco Tacchino and colleagues at the University of Pavia in Italy. These guys have built the world’s first perceptron implemented on a quantum computer and then put it through its paces on some simple image processing tasks.
In its simplest form, a perceptron takes a vector input—a set of numbers—and multiplies it by a weighting vector to produce a single-number output. If this number is above a certain threshold the output is 1, and if it is below the threshold the output is 0.
That has some useful applications. Imagine a pixel array that produces a set of light intensity levels—one for each pixel—when imaging a particular pattern. When this set of numbers is fed into a perceptron, it produces a 1 or 0 output. The goal is to adjust the weighting vector and threshold so that the output is 1 when it sees, say a cat, and 0 in all other cases.
Tacchino and co have repeated Rosenblatt’s early work on a quantum computer. The technology that makes this possible is IBM’s Q-5 “Tenerife” superconducting quantum processor. This is a quantum computer capable of processing five qubits and programmable over the web by anyone who can write a quantum algorithm.
Tacchino and co have created an algorithm that takes a classical vector (like an image) as an input, combines it with a quantum weighting vector, and then produces a 0 or 1 output.
The big advantage of quantum computing is that it allows an exponential increase in the number of dimensions it can process. While a classical perceptron can process an input of N dimensions, a quantum perceptron can process 2N dimensions.
Tacchino and co demonstrate this on IBM’s Q-5 processor. Because of the small number of qubits, the processor can handle N = 2. This is equivalent to a 2x2 black-and-white image. The researchers then ask: does this image contain horizontal or vertical lines, or a checkerboard pattern?
It turns out that the quantum perceptron can easily classify the patterns in these simple images. “We show that this quantum model of a perceptron can be used as an elementary nonlinear classifier of simple patterns,” say Tacchino and co.
They go on to show how it could be used in more complex patterns, albeit in a way that is limited by the number of qubits the quantum processor can handle.
That’s interesting work with significant potential. Rosenblatt and others soon discovered that a single perceptron can only classify very simple images, like straight lines. However, other scientists found that combining perceptrons into layers has much more potential. Various other advances and tweaks have led to machines that can recognize objects and faces as accurately as humans can, and even thrash the best human players of chess and Go.
Tacchino and co’s quantum perceptron is at a similarly early stage of evolution. Future goals will be to encode the equivalent of gray-scale images and to combine quantum perceptrons into many-layered networks.
This group’s work has that potential. “Our procedure is fully general and could be implemented and run on any platform capable of performing universal quantum computation,” they say.
Of course, the limiting factor is the availability of more powerful quantum processors capable of handling larger numbers of qubits. But most quantum researchers agree that this kind of capability is close.
Indeed, since Tacchino and co did their work, IBM has already made a 16-qubit quantum processor available via the web. It’s only a matter of time before quantum perceptrons become much more powerful.
Ref: arxiv.org/abs/1811.02266 : An Artificial Neuron Implemented on an Actual Quantum Processor
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What is known as a quantum computer has been the subject of movies and series on dozens of occasions throughout history? Although the original concept may sound like science fiction, the truth is that quantum computers are already a reality. As their name suggests, this type of machine takes advantage of the properties of quantum mechanics to solve certain problems that classical computers are not capable of solving, problems that we will discuss below.
Quantum Computers: What They Are and What Differentiates Them From A Traditional PC
Before discussing the differences between a quantum computer and a conventional computer, it is useful to know the nature of the term “quantum”, which in this case refers to the type of information handled by this type of equipment.
As is well known, conventional computers work with the simplest unit of information we know, the bit. This unit contains exactly two states of information that are subdivided into 0 and 1. In the case of quantum computers, the minimum unit of information is known as a cubit or qubit.
Graphical representation of a cubit or qubit in the form of a Bloch sphere. The sphere represents both the possible states of the qubit and the states themselves based on the polarization of a photon.
Unlike a bit, which can only contain a single combination, a qubit can contain a simultaneous combination of 0 and 1. Hence, more complex units such as bytes, which are simple groupings of bits, are handled. It should be noted that the natural state of a qubit is represented by subatomic particles, such as photons or electrons.
To deal with this type of information, quantum computers require the use of certain systems and materials that are resistant to this type of particle. In other words, the computer does not have a conventional structure but uses a series of superconducting circuits whose cooling is designed to reach absolute zero and thus isolate the particles in a state that can be controlled.
Quantum Mechanics and Qubits: How Quantum Computers Work With Information
We have already mentioned that qubits can contain different strings of 0’s and 1’s at the same time. This is because qubits can be represented in different states. For this, quantum computers require the use of a series of systems to achieve what is known as quantum superposition, which is nothing more or less than the possibility of representing several states at the same time, i.e. several strings of 0 and 1. This means that the information contained in this type of particle is much greater than what we can find in a byte.
This is what an alanine molecule used in the NMR implementation of quantum computing looks like. Often, how such molecules are introduced into quantum computers is related to magnetic resonance systems.
Current systems are made up of microwaves and precision lasers that allow the state of the qubits to be controlled. One of the great challenges of current engineering has to do with these systems and their design. Creating a system that is capable of controlling these states while keeping the qubits in their natural state will raise the possibility of working with enormous amounts of information to levels never before recorded. And precisely another of the great challenges of current engineering is related to the combination of different qubits in groups known as chains, which overlap through what is known as quantum entanglement.
This phenomenon describes the pairwise grouping of qubits. In the same way that bits intertwine with each other to form a byte, the grouping of this unit follows the laws of quantum mechanics. The problem is that the current laws of physics do not explain this phenomenon, as controllability is subject to failure. And this is one of the major problems of quantum computers: the probability of error when performing calculations.
This is due to the behavior of the qubits themselves when interacting with each other and creating pairs with the rest of the particles in the surrounding environment. As we indicated in previous paragraphs, the control of qubit states is one of the great challenges of current engineering, since current systems try to solve what is known as quantum incoherence.
Such is the difficulty of grouping qubits, that the greatest achievement of current engineering has only grouped 128 qubits. This difference concerning conventional computers is known as quantum supremacy, which is precisely related to the possibility of solving calculations that conventional computers are not capable of solving regardless of the computational capacity they have. In 2019, Google announced having reached quantum supremacy with its computers, A year later, it was China that announced having reached this milestone through a research group at the University of Science and Technology of China in collaboration with Tsinghua University in Beijing.
The Race to Develop a Fully-fledged Quantum Computer
At present, very few companies have participated in the development of this type of equipment because of the investment and the difficulty of progress involved. The best known at present are Intel, Google, and IBM, which are in a race to develop the first viable quantum computer. For example, Google’s quantum computer, called Sycamore, has a capacity of 54 qubits and is capable of performing calculations that a conventional computer would take approximately 10,000 years to perform in just 3.5 minutes. That’s nothing.
As for Intel’s developments, the company has launched its chip, known as Horse Ridge, in 2020. This chip allows the integration of quantum processors of up to 128 qubits, the limit that has been achieved to date. On the other hand, companies such as D-Wave, which are involved in the development of this type of equipment, have proposed their computers to the scientific community in the fight against the cure for COVID-19. IBM has also created its commercial quantum computer, called IBM Q System One.
With a power of 20 qubits, the computer is housed in an airtight glass cube 2.7 meters wide by 2.7 meters high that helps maintain the correct temperature while absorbing vibrations in the environment. It is worth noting that such a feat was accomplished in 2019, no less.
So, What is Quantum Computer For?
The information processing capacity of quantum computers opens the door to a whole world of the future in different sectors. After all, the main limitation of the different developments in the industry today has to do with the processing capacity of conventional computer hardware.
The use of quantum computers in certain industries would help to develop advances in medicine, cybersecurity, autonomous driving systems, artificial intelligence, robotics, and many other sectors that depend on information processing. The enormous computing power of this type of equipment could accelerate the development of certain technologies, such as those related to graphene or the development of lithium-ion batteries with higher density. It would also be possible to simulate the behavior of certain particles in contact with others, giving us the possibility of emulating the birth of the Universe, an action that has already been attempted since the Higgs Boson was installed in Switzerland and which resulted in the discovery of the God particle.
In any case, everything points to the fact that this type of equipment will not be massively available for approximately 15 years. Their arrival in the home is not expected for at least a century, since both particle control and equipment size are not within the reach of the consumer market at the time of publication (although companies such as SpinQ Technology have already developed a desktop device for the general public). Needless to say, such proposals are a far cry from the capabilities of today’s most powerful computers, although they bring the possibilities of quantum computers closer to the market.
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A section of the light-based quantum computer created by researchers at the University of Science and Technology of China. (Image by Xinhua)
Scientists in Anhui develop a machine far exceeding classical supercomputers
Chinese scientists have created the world's first light-based quantum computer, called Jiuzhang, that can reliably demonstrate "quantum computational advantage", a milestone in which a quantum machine can solve a problem no classical supercomputer can tackle within a reasonable amount of time, according to a study published in the journal Science on Friday.
It is the second time that humanity has reached this milestone, after Google declared its 53-qubit quantum computer had achieved such a breakthrough last year.
However, Jiuzhang used a new method of manipulating 76 photons to do calculations instead of Google's, which uses superconductive materials.
Experts hailed the Chinese machine as a "state-of-the-art experiment" and a "major achievement" in quantum computing, as it proves the feasibility of photonic quantum computation, thus providing a fundamentally different approach to designing such powerful machines.
Quantum computers excel at running simulations that are impossible for conventional computers, leading to breakthroughs in materials science, artificial intelligence and medicine.
Moreover, most components of the light-based quantum machine can operate at room temperature, aside from its sensory equipment, which must be kept at -269.1 C.
This makes it significantly easier to make and maintain than superconducting quantum computers, the bulk of which must be kept at ultra-cold temperatures to ensure the materials can conduct electricity without any resistance.
Jiuzhang takes its name from an ancient Chinese mathematical text. It can perform an extremely esoteric calculation, called Gaussian boson sampling, in 200 seconds. The same task would take the world's fastest classical supercomputer, Fugaku, around 600 million years.
Fabio Sciarrino, a quantum physicist at Sapienza University of Rome, told Science News, an outlet based in the United States, that his first impression of the Chinese quantum computer was, simply, "wow".
According to interviews by the University of Science and Technology of China in Hefei, Anhui province, whose researchers created Jiuzhang, Barry Sanders, director of the Institute for Quantum Science and Technology at the University of Calgary, Canada, called the feat "one of the most significant results in the field of quantum computing" since Google's claim to quantum advantage last year was later challenged by IBM.
Anton Zeilinger, noted quantum physicist and president of the Austrian Academy of Sciences, said that, following this experiment, he predicts there is a very good chance that quantum computers may be used very broadly someday, according to the university's interviews.
"I'm extremely optimistic in that estimate, but we have so many clever people working on these things, including my colleagues in China. So, I am sure we will see quite rapid development."
Quantum machines' astronomical computing power arises from their basic building blocks, called quantum bits, or qubits, according to the University of Science and Technology of China.
Unlike bits of classical computers that present data as either 0s or 1s, similar to the on and off of a light switch, qubits can harness the strange property of quantum mechanics known as superposition and exist as 0s, 1s or everything in between, like the increments on a control knob.
Quantum machines can take computational shortcuts when simulating extremely complex scenarios, whereas conventional computers have to brute force their way to a solution, taking significantly more time in the process.
Moreover, quantum machines' computing power can increase exponentially as more qubits are added.
Therefore, Jiuzhang, which uses 76 photons as qubits, is about 10 billion times faster than the 53-qubit computer developed by Google, according to the university.
"The feat cements China's position in the first echelon of nations in quantum computing," the university said in a news release.
Pan Jianwei, who is recognized as China's top quantum scientist and one of the key researchers behind Jiuzhang, said the calculations they carried out can not only showcase the machine's computing prowess but also demonstrate potential practical applications in machine learning, quantum chemistry and graph theory.
"Quantum computing has already become a fierce competition ground among the United States, Europe and other developed regions," Pan said, adding that China's quantum computational advantage took about seven to 10 years to achieve, since the team first decided to tackle the boson-sampling problem around 2013.
However, Pan stressed that the photonic quantum computer is a highly specialized and unorthodox machine, characterized as an elaborate, interconnected tabletop setup of lasers, mirrors and detectors, and is currently only programed to do boson sampling. "It is not a general-purpose quantum computer," he said.
Lu Chaoyang, another key researcher behind Jiuzhang, said that, even if a machine is only good at one job, such as analyzing materials, it can still have great social and economic value if it can overcome an extremely challenging problem.
In the near future, scientists may increase Jiuzhang's possible output states－a key indicator of computing power－by 10 orders of magnitude, from 10 to the 30th power to 10 to the 40th power, Lu said. (China Daily)
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A Q-bit is like a classical bit but with a state that is 0 or 1 and neither at the same time. This opens a bunch of new possibilities for us, in context quantum computer has a computational advantage in a lot of the jobs that classical computers just cannot perform in a reasonable amount of time. This state in which the Q-bit is a 0 and a 1 is called a superposition. One very importent fact of Q-bits in superposition is that when we measure will fall to either 0 or 1 on a 50-50 chance.
Before you read this article you should read the part one which you can find here, this makes sure that you know what we are going to talk about.
Read this if you want to know exactly how RSA works this, it will also help you in part 3 of this series.
Advantages of Q-bits
Now that we have a refresher on what Q-bits are lets take a look at how they can be helpful. Lets say that we have 3 bits and the same number of Q-bits, we can have a total of 3 * 2 number of possibilities or combinations of bits, but with the same number of Q-bits we have 3^2 (3 Squared) total possibilities or combinations, because each one of those 3 bits has an extra state called a superposition.
This gives Quantum Computers a massive exponential computational advantage over Classical Computers. This however doesn't mean that Quantum Computer will be better or faster at all the task that a Classical Computer can do, but it does mean for specific computation a Quantum Computer will win by default because a Classical Super Computer would take years to perform it or will not even be able to perform it. There is also a lack of good Quantum Algorithms, this however will be fixed as we get better at making these Quantum Computers and making them available to people who develop algorithms. Remember as of right now the main advantage we have over classical supercomputer is running quantum algorithms, but that would be the only reason to choose it not the speed its actually very slow on classical algorithms, the only place where we see the speed is in Quantum Algorithms.
Shor's algorithm is the most famous Quantum algorithm,it is not a very special algorithm as you can essentially run it on your normal home PC, but it runs exponentially fast on a Quantum Computer. Am going to try and attempt to explain this algorithm without using a lot of math and physics, which is really hard to do since its pretty much all math and physics.
So here goes a 4 weeks worth of my study notes covering a complicated algorithm in one big paragraph! Shor's algorithm's "basic" functionality is that it can guess factors of given number N (Really Big Number), we already have a basic algorithm that finds factors called The Euclidean Algorithm, which tell us the factor of N, so we take a guess "g". This "g" doesn't have to be a factor of N it can be a number that shares a factor of N( how 4 isn't a factor of 6 but shares a number that is 2).if you want more reading on this basic algo read this.
So lets look at shor's algorithm, it helps us make a better guess "g" as a factor, if there are 2 whole number(x,y) which don't share a factor to N, if we raise x to a certain power x^p we will have k * y+1, (x^p = k * y+1).
So now the main problem for us is to guess the right p. So for a very large number N and a arbitrary starting guess "g", we would have an equation:
g^p = k * N+1. Now if we rearrange this in a clever way we would arrive at this useful equation: (g^(p/2) +1) * (g^(p/2) -1) = k * N.
This looks like the factor equation which we are trying to find (N = a * b). This is the math part of shor's algorithm. The clever part is the science, The advantage we have with Quantum Computer is that we can input multiple bits in superposition which will all simultaneously calculate all the possible values of "p" and "g", however the problem comes from the fact that even if we do that and have an answer in superposition we will only get one of the answers and a low probability of the right one.
To solve this we also calculate the frequency ( sin and cos graphs) and these are also superposition, which helps us cancel out the wrong superposition and arrive to the correct answer, in the "first try".
This whole thing happens so fast that its mind blowing, Classical Computers would take thousands of years to calculate it. This would easily break all the encryption that we use right now. This also poses a big threat to the Cyber Security of Computers/Networks and Applications.If you wanna read more on the Shor's Algotithm you should read this article
This Video can help you understand the algo itself in great detail here. We will talk about Quantum Encryption in the next article in this series, which will solve this problem of breaking all encryption.For updates on these articles follow secjuice on twitter or Me. I am sorry about not posting article's, its just hard will a lot of stuff going around me.
You should also check out IBM Q. As well as Qiskit, a programming language to write and run code on a quantum computer.


Flashlight beams don’t clash together like lightsabers because individual units of light—photons—generally don’t interact with each other. Two beams don’t even flicker when they cross paths.
But by using matter as an intermediary, scientists have unlocked a rich world of photon interactions. In these early days of exploring the resulting possibilities, researchers are tackling topics like producing indistinguishable single photons and investigating how even just three photons form into basic molecules of light. The ability to harness these exotic behaviors of light is expected to lead to advances in areas such as quantum computing and precision measurement.
In a paper recently published in Physical Review Research, Adjunct Associate Professor Alexey Gorshkov, Joint Quantum Institute (JQI) postdoctoral researcher Przemyslaw Bienias, and their colleagues describe an experiment that investigates how to extract a train of single photons from a laser packed with many photons.
In the experiment, the researchers examined how photons in a laser beam can interact through atomic intermediaries so that most photons are dissipated—scattered out of the beam—and only a single photon is transmitted at a time. They also developed an improved model that makes better predictions for more intense levels of light than previous research focused on (greater intensity is expected to be required for practical applications). The new results reveal details about the work to be done to conquer the complexities of interacting photons.
“Until recently, it was basically too difficult to study anything other than a few of these interacting photons because even when we have two or three things get extremely complicated,” says Gorshkov, whi is also a physicist at the National Institute of Standards and Technology and Fellow of the Joint Center for Quantum Information and Computer Science. “The hope with this experiment was that dissipation would somehow simplify the problem, and it sort of did.”
Trains, Blockades and Water Slides
To create the interactions, the researchers needed atoms that are sensitive to the electromagnetic influence of individual photons. Counterintuitively, the right tool for the job is a cloud of electrically neutral atoms. But not just any neutral atoms; these specific atoms—known as Rydberg atoms—have an electron with so much energy that it stays far from the center of the atom.
The atoms become photon intermediaries when these electrons are pushed to their extreme, remaining just barely tethered to the atom. With the lone, negatively charged electron so far out, the central electrons and protons are left contributing a counterbalancing positive charge. And when stretched out, these opposite charges make the atom sensitive to the influence of passing photons and other atoms. In the experiment, the interactions between these sensitive atoms and photons is tailored to turn a laser beam that is packed with photons into a well-spaced train.
The cloud of Rydberg atoms is kind of like a lifeguard at a water park. Instead of children rushing down a slide dangerously close together, only one is allowed to pass at a time. The lifeguard ensures the kids go down the slide as a steady, evenly spaced train and not in a crowded rush.
Unlike a lifeguard, the Rydberg atoms can’t keep the photons waiting in line. Instead they let one through and turn away the rest for a while. The interactions in the cloud of atoms form a blockade around each transmitted photon that scatters other photons aside, ensuring its solitary journey.
To achieve the effect, the researchers used Rydberg atoms and a pair of lasers to orchestrate a quantum mechanical balancing act. They selected the frequency of the first laser so that its photons would be absorbed by the atoms and scattered in a new direction. But this is the laser that is whittled down into the photon train, and they needed a way to let individual photons through.
That’s were the second laser comes in. It creates another possible photon absorption that quantum mechanically interferes with the first and allows a single photon to pass unabsorbed. When that single photon gets through, it disturbs the state of the nearby atoms, upsetting the delicate balance achieved with the two lasers and blocking the passage of any photons crowding too closely behind.
Ideally, if this process is efficient and the stream of photons is steady enough, it should produce a stream of individual photons each following just behind the blockade of the previous. But if the laser is not intense enough, it is like a slow day at the waterpark, when there is not always a kid eagerly awaiting their turn. In the new experiment, the researchers focused on what happens when they crowed many photons into the beam.
Model (Photon) Trains
Gorshkov and Bienias’s colleagues performed the experiment, and the team compared their results to two previous models of the blockade effect. Their measurements of the transmitted light matched the models when the number of photons was low, but as the researchers pushed the intensity to higher levels, the results and the models’ predictions started looking very different. It looked like something was building up over time and interfering with the predicted, desired formation of well-defined photon trains.
The team determined that the models failed to account for an important detail: the knock-on effects of the scattered photons. Just because those photons weren’t transmitted, doesn’t mean they could be ignored. The team suspected the models were being thrown off by some of the scattered light interacting with Rydberg atoms outside of the laser beam. These additional interactions would put the atoms into new states, which the scientists call pollutants, that would interfere with the efficient creation of a single photon train.
The researchers modified one of their models to capture the important effects of the pollutants without keeping track of every interaction in the larger cloud of atoms. While this simplified model is called a “toy model,” it is really a practical tool that will help researchers push the technique to greater heights in their larger effort to understand photon interactions. The model helped the researchers explain the behavior of the transmitted light that the older models failed to capture. It also provides a useful way to think about the physics that is preventing an ideal single photon train and might be useful in judging how effectively future experiments prevent the undesirable affects—perhaps by using cloud of atoms with different shapes.
“We are quite optimistic when it comes to removing the pollutants or trying to create less of them,” says Bienias. “It will be more experimentally challenging, but we believe it is possible.”
Original story by Bailey Bedford: https://jqi.umd.edu/news/scientists-see-train-photons-new-light
In addition to Bienias and Gorshkov, James Douglas, a Co-founder at MEETOPTICS; Asaf Paris-Mandoki, a physics researcher at Instituto de Física, Universidad Nacional Autónoma de México; JQI postdoctoral researcher Paraj Titum; Ivan Mirgorodskiy; Christoph Tresp, a research and development employee at TOPTICA Photonics; Emil Zeuthen, a physics professor at the Niels Bohr Institute; Michael J. Gullans, a former JQI postdoctoral researcher and current associate scholar at Princeton University; Marco Manzoni, a data scientist at Big Blue Analytics; Sebastian Hofferberth, a professor of physics at the University of Southern Denmark; and Darrick Chang, a professor at the Institut de Ciencies Fotoniques, were also co-authors of the paper.


First of two parts
One of the first steps toward becoming a scientist is discovering the difference between speed and velocity.
To nonscientists, it’s usually a meaningless distinction. Fast is fast, slow is slow. But speed, technically, refers only to rate of motion. Velocity encompasses both speed and direction. In science, you usually want to know more than just how fast something is going; you also want to know where it is going. Hence the need to know direction, and to analyze velocity, not just speed. Numbers like velocity that express both a magnitude and a direction are known as vectors.
Vectors are great for describing the motion of a particle. But now suppose you need to analyze something more complicated, where multiple magnitudes and directions are involved. Perhaps you’re an engineer calculating stresses and strains in an elastic material. Or a neuroscientist tracing the changing forces on water flow near nerve cells. Or a physicist attempting to describe gravity in the cosmos. For all that, you need tensors. And they might even help you unify gravitational theory with quantum physics.
Tensors accommodate multiple numerical values (a vector is actually a simple special case of a tensor). While the ideas behind tensors stretch back to Gauss, they were first fully described in the 1890s by the Italian mathematician Gregorio Ricci-Curbastro, with the help of his student Tullio Levi-Civita. (Tensors were given their name in 1898 by Woldemar Voigt, a German crystallographer, who was studying stresses and strains in nonrigid bodies.)
Ricci (as he is commonly known) was influenced by the German mathematician Bernhard Riemann in developing advanced calculus with applications to complicated geometrical problems. In particular, this approach proved valuable in studying coordinate systems. Tensors help make sense of the relationships in the system that stay the same when you change the coordinates. That turned out to be just the thing Einstein needed in his theory of gravity, general relativity. His friend Marcel Grossmann explained tensors to him and they became the essential feature of general relativity’s mathematics.
And now, in a recent development, some physicists think tensors of a sort could help solve the longstanding problem of unifying general relativity with quantum mechanics. It’s part of a popular new line of research using tensors to quantify quantum entanglement, which some physicists believe has something to do with gravity.
Entanglement is that spooky connection between separated particles that disturbed Einstein so much. Somehow a measurement of one of a pair of particles affects what you’ll find when you measure its distant partner, or so it seems. But this “entanglement” is a clear-cut consequence of quantum physics for particles that share a common origin or interaction. It leads to some weird phenomena, but it’s all very sensible mathematically, as described by the “quantum state.” Entangled particles belong to a single quantum state.
A quantum state determines the mathematical expression (called the wave function) that can be used to predict the outcome of measurements of a particle — whether the direction that it spins is pointing up or down, for instance. When describing multiple particles — such as those in materials exhibiting quantum properties such as superconductivity — quantum states can get very complicated. Coping with them is made easier by analyzing the network of entanglement among those many particles. And patterns of such network connections can be described using tensors.
“Tensor networks are representations of quantum many-body states of matter based on their local entanglement structure,” physicist Román Orús writes in a recent paper posted at arXiv.org. “In a way, we could say that one uses entanglement to build up the many-body wave function.”
Put another way, Orús says, the entire wave function can be thought of as built from smaller tensor subnetworks, kind of like Legos. Entanglement is the glue holding the Legos together.
“Tensor network methods represent quantum states in terms of networks of interconnected tensors, which in turn capture the relevant entanglement properties of a system,” Orús writes in another recent paper, to be published in Annals of Physics.
While the basic idea of tensor networks goes back decades, they became more widely used to study certain quantum systems in the 1990s. In the last few years, ideas from quantum information theory have spawned an explosion of new methods using tensor networks to aid various calculations. Instead of struggling with complicated equations, physicists can analyze systems using tensor network diagrams, similar to the way Feynman diagrams are used in other aspects of quantum physics.
“This is a new language for condensed matter physics (and in fact, for all quantum physics) that makes everything much more visual and which brings new intuitions, ideas and results,” Orús writes.
Most recently, tensor networks have illuminated the notion that quantum entanglement is related to gravity. In Einstein’s general relativity, gravity is the effect of the geometry of spacetime. Analyses suggest that the geometry in which a quantum state exists is determined by the entanglement tensor network.
“By pushing this idea to the limit,” Orús notes, “a number of works have proposed that geometry and curvature (and hence gravity) could emerge naturally from the pattern of entanglement present in quantum states.”
If so, tensor networks could be the key to unlocking the mystery of quantum gravity. And in fact, another clue to quantum gravity, known as the holographic principle, seems naturally linked to a particular type of tensor network. That’s a connection worth exploring further.
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As modern computers continue to reach the limits of their processing power, quantum computing is starting to offer hope for solving more specialized problems that require immensely robust computing. Quantum computers were once thought an impossible technology because they harness the intricate power of quantum mechanics and are housed in highly unconventional environments. But these machines now have the potential to address problems ranging from finding drugs that can target specific cancers to valuing portfolio risk, says Vern Brownell, founder and CEO of D-Wave Systems, the Canadian company that in 2010 introduced the world’s first commercially available quantum computer. In this interview with McKinsey’s Michael Chui, Brownell discusses what quantum computing is, how it works, and where it’s headed in the next five years. An edited transcript of their conversation follows.
We’re at the dawn of the quantum-computing age, and it’s really up to us to execute. It sounds grand. But I think this is such an important enabling technology and can help mankind solve problems that are very, very important.
What is quantum computing?
D-Wave Systems is the world’s first quantum-computing company. We have produced the world’s first commercial quantum computers. A quantum computer is a type of computer that directly leverages the laws of quantum mechanics to do a calculation.
And in order to do that, you have to build a fairly exotic type of computer. You have to control the environment very carefully. The whole point of building a quantum computer is, basically, for performance, to solve problems faster than you can with conventional (or what we call classical) computers, meaning the types of computers that we all enjoy today and that have done such a great job. There are problems that scale better, or they can perform better, using quantum computers rather than classic computers. And that’s really why everyone is trying to build a quantum computer: to take advantage of that capability that’s inherent in quantum mechanics.
How do quantum computers work?
You probably will remember from your physics classes that a quantum mechanical object, if it’s disturbed, it’s frozen in one state or it becomes classical. So every quantum computer has, as its building block, something called a qubit, a quantum bit. And a quantum bit is like the digital bit that’s in every computer; digital bits are sort of the building blocks of all computers.
But a qubit has this special characteristic where it can be in what’s called a superposition of zero and one at the same time. So if you step back from that, this object is actually in two different states at the same time. And it’s not like it’s half in this state and half in the other; it’s in those two states at the same time. It sounds spooky. Einstein called it spooky. But it is a fundamental law of quantum mechanics and it is the building block of a quantum computer.
So these qubits are all in this superposition, which is a very delicate state. And whenever a cosmic ray or some kind of interference hits that computation, it freezes it out to a classical state. So the trick is to keep the calculation going in this superposition for the duration of the computational cycle.
The environment in which the system operates is kept at a temperature that is near absolute zero. So you probably remember, –273 degrees centigrade is the lowest temperature, called a thermodynamic limit or the lowest temperature that’s physically possible in the universe. This machine runs at 0.01 degrees kelvin, or 10 degrees millikelvin, above that.
So unless there’s any other intelligent life in the universe, this is the coldest environment in the universe that this machine has to run in. For instance, interstellar space is about 4 degrees kelvin, which is much, much warmer than our operating temperature.
That’s not the only part of it. We have to create a magnetic vacuum and an air vacuum. So there’s this coffee-can-sized environment that has this incredibly low temperature and this magnetic vacuum that is probably among the purest environments in the universe. There are no naturally occurring environments like this.
You don’t buy a quantum computer for the economics. But that will change, as I said, as the power of the machine grows. There can certainly be just an economic benefit of using this for certain problem types versus using classical computers.
What problems do quantum computers solve?
There are different types of quantum computers. The type that we build is called a quantum annealer. And so I’ll talk about the types of problems that quantum annealers do. Much of what you’ll hear about quantum computing is related to gate-model quantum computing, which is another approach that’s very valid. The problem with it is that it’s very, very hard to implement. And it’s probably more than ten years away.
We believe that one of the most important applications of quantum computing is in the category of machine learning. So we’ve developed, together with our partners, algorithms that can leverage this quantum-computing capability to do machine learning better than you could with just classical resources alone, even though the state of the art in classical computing and machine learning is quite high. They’re doing some amazing things with scale-out architectures and GPUs
and special-purpose hardware. We believe that the advantages that quantum computing can have can even take that to the next level.
Another is in the whole optimization area, and it’s called sampling. So there are optimization problems all around us. We’re trying to find the best answer out of a complex set of alternatives. And that could be in portfolio analysis and financial services. It could be trying to find the right types of drugs to give a cancer patient—lots of meaty, very impactful types of applications that are in the sampling world that we believe are very relevant to this.
Google and NASA, for instance, are customers of ours. And Google has created what they call the Quantum Artificial Intelligence Lab, where they’re exploring using our computer for AI applications or learning applications. And NASA has a whole set of problems that they’re investigating, ranging from doing things like looking for exoplanets to [solving] logistic problems and things like that. I’d say within five years, it’s going to be a technology that will be very much in use in all sorts of businesses.


Vortices of Light on the Cheap
Vector vortex laser beams have a unique polarization pattern that varies with position around a dark center, and this nonuniformity can, for example, lead to tighter focusing than is possible with the usual uniform polarization. Now researchers have produced vector vortices using just a low-cost laser along with a reflecting element, bypassing the usual specialized equipment. This relatively simple design could allow switching between different polarization patterns with a small change to the laser current.
Vector vortices are similar to optical vortices; both have beam cross sections with a donut-shaped intensity pattern. In an optical vortex, the light’s phase varies around the dark hole in the center of the beam, while the polarization direction is uniform. But in a vector vortex beam it’s the polarization that changes as you move around the center. Researchers have shown that a beam with varying polarization can be focused to a smaller spot size than a beam with uniform polarization . This tight focusing could benefit optical trapping and light-based etching techniques. Researchers are also exploring correlation properties of vector vortices, which resemble quantum entanglement.
One of the most common methods for producing vector vortices is to use a liquid-crystal spatial light modulator that can impose a polarization direction at specific points in the cross section of a beam. Other elements, such as cone-shaped reflectors, can also be inserted into a beam’s path to generate nonuniform polarization configurations. But a simpler method has now been revealed by Thorsten Ackemann of the University of Strathclyde in Glasgow, UK, and his colleagues. The team discovered that a type of semiconductor laser—specifically, a vertical-cavity surface-emitting laser (VCSEL)—could produce a variety of different vector vortices when aimed at a frequency-specific mirror. “You don’t need to engineer these polarization states,” Ackemann says. “They arise spontaneously.”
The potential for generating vector vortices with a VCSEL was predicted twenty years ago . The reasoning then was based on the highly symmetric character of a VCSEL lasing cavity. Most lasers are not symmetric—for example, they may have rectangular cross sections—and this asymmetry largely determines the polarization direction of the emitted light. By contrast, a VCSEL emits from a cylindrical cavity, so the polarization can often switch between, say, horizontal and vertical directions. This polarization “competition” suggests that there might be intermediate states where the two polarization modes coexist to form a spatially nonuniform pattern. Ackemann suspects that no previous experiments had detected a nonuniform polarization (vector vortex) because VCSELs have just enough asymmetry that one polarization mode always wins—at least for a little while before switching to the other.
In their system, Ackemann and his colleagues were able to compensate for the intrinsic asymmetry of their VCSEL by adding a volume Bragg grating (VBG)—essentially a mirror that only reflects one frequency. The VBG, which was placed in front of the VCSEL, created a new resonance cavity between the reflecting surface and the emitting surface of the laser. The feedback from this cavity helped to lock the laser frequency at the reflection frequency. As the team varied the current supplied to the VCSEL (which affects the beam’s intensity and frequency), they were surprised to find that the polarization became nonuniform. The reason for this change is not entirely clear, but Ackemann believes that minute tilts of the VBG can offset laser-based anisotropies and force multiple polarization modes to have the same frequency, so that none dominates.
The observed polarization configurations depended on the current supplied to the VCSEL. For most values, the polarization stayed uniform, but for certain current inputs, the team recorded vortices with radial, hyperbolic, or spiral patterns. This dependence on current, which is linked to temperature-induced changes in the resonant frequency of the VCSEL-VBG cavity, might be used in future devices to switch vortices on and off. Such control is possible with spatial light modulators, but these devices are expensive.
Qiwen Zhan of the University of Dayton in Ohio says that creating vector vortices with a VCSEL could be useful in many applications, with the main advantages being “the compactness of the device and the capability of adjusting the nonuniform polarization states through electrical tuning.” However, he agrees with the authors that applications will have to wait for a more comprehensive study of the VCSEL-VBG resonance cavity to understand the mechanisms that lead to vortex formation.
This research is published in Physical Review Letters.
Michael Schirber is a Corresponding Editor forPhysics based in Lyon, France.


What is Quantum Computing?
Quantum Computing, very simplistically, is computing with the hardware, software, and devices that follow the principles of Quantum Physics.
Let’s discuss the basics of Quantum Computing in layman’s terms. Currently, the “bit” in semiconductor technology can hold the state of ‘0’ or ‘1’. But, a quantum bit, or qubit, can take a state of ‘0’ and ‘1’ simultaneously. In current state microprocessor-based computing, states ‘0’ or ‘1’ can be considered to be electronic logic gate managed voltages like, for example, zero (0) volt or five (5) volts. In the case of quantum computing, the state is a probabilistic outcome. The potential outcome of a calculation in a quantum computer is close to the actual result with the highest probability. Ideally, the total number of outcomes of quantum computing through Superposition could be infinite. Quantum Computer needs a low-temperature and nearly empty environment to reduce the computational noise impacting the outcome of the calculation. An interesting fact about quantum computing is that with the increase in the number of qubits involved in any calculation, the computational power of the quantum computer increases exponentially as every qubit can represent two states – 2 to the power n, where n is the number of qubits.
Other than Superposition, there are two interesting concepts in Quantum Computing. Entanglement is the concept of Quantum Physics that explains the phenomenon of the interaction of two qubits across the universe. Interference in Quantum Computing is somewhat related to Superposition that explains the bias of outcome towards a particular state.
Why do we need Quantum Physics?
Simply stated, to explain a few complexities of our universe that Classical Physics has fallen short of explaining to the fullest satisfaction. Phenomena like Blackbody radiation, Photoelectric effect, Hydrogen atom’s behavior with heat, etc cannot be satisfactorily explained with Classical Physics.
Why do we need Quantum Computing?
As proclaimed by Gordon Moore, the number of transistors on Intel’s processors at the time of their introduction has almost doubled every 18 to 24 months. But, this growth of computational power over time is slowing down because of the manageable limits of size, heat generated, and power required.
Quantum Computing is kind of following Neven’s Law of Quantum Computing that proclaims that Quantum computers are improving at a doubly exponential rate.
The growth of computing power in Moore’s Law was exponential by powers of 2: 21, 22, 23, 24. Doubly exponential growth represents the growth of computing power by powers of powers of 2.
How to transform Mathematical Concepts of Quantum Computing into a Physical Machine for day-to-day use?
We saw how the field of electronics made progress from valve-based transistors to semiconductor-based transistors before we got our modern days laptop or desktop. Long before that, we learned to use tools like Abacus that is nothing but a version of an analog computer. Now, the big question is around making the concepts of quantum physics and computing a reality for normal day-to-day use. What kind of inorganic or organic compound can represent the quantum phenomenon for regular use towards human computational needs?
It is still in the state of research and continuous development. Summary of types of Quantum Computers are as follows:
- Quantum Annealing Computers – by DWave Systems
- Universal Quantum Computers – IBM, Google
- Topological Quantum Computers – Microsoft
- Ion Trap Quantum Computers – IonQ
A number of vendors are also offering Quantum Computer Simulators over the Cloud.
Potential Use cases of Quantum Computing
Fundamentally, the best application areas for Quantum Computing are those that involve the massive volume of data and processing of those data but don’t need 100 percent accuracy and precision. Additionally, considering the current state of technology, those use cases should support the possibility of Hybrid Computing, that is, the use of both Quantum Computers as well as Classical Computers, taking advantage of respective computing advantages. At the first step, Quantum Computers will narrow the options of possible solutions because of its probabilistic nature, and at the final step, Classical Computers will deliver the final solution with defined accuracy or precision.
Biochemistry and Pharmaceuticals
These are huge potential benefits of Quantum Computing in the field of Biochemistry to reduce the time and effort needed for the Synthesis of Molecules. Modeling of Molecules impacting quicker Drug Discovery is a very important application area, directly impacting human life.
In Cancer Treatment, Quantum Computing will have a positive impact in Intensity Modulated Radio Therapy (IMRT) with better optimization of dosage calculations.
In the field of Materials Production, Quantum Computer will drastically improve the manufacturing process of fertilizer, impacting global food production and agriculture.
Quantum Computing will positively change the way Algorithm-driven High-Frequency Trading takes place in Finacial Companies.
Smart City and government
Management of driverless cars through a big city with continuous optimization can only be handled with the massive computational power of Quantum Computers. Transportation and Logistics of the future will be positively impacted by Quantum Computing.
Weather forecasting is expected to improve with the use of Quantum Computer. Energy Generation and Distribution and related calculation of utilization prediction, grid optimization will function with better accuracy.
Needless to mention that Quantum Computing drives the performance with better optimization using massive data quicker and better.
Areas of AI like unsupervised machine learning, computer vision, etc will make AI more effective to society with Quantum Computing.
Quantum Computing is expected to disrupt the way present-day cybersecurity using Cryptography functional. Researchers in the field of Quantum Cryptography are busy formulating quantum-ready encryption algorithms. Some of the algorithms like McEliece Cryptography are being thought to mitigate the impacts of Quantum Computing.
The underlying premise of cryptography for Blockchain to function has to evolve to remain effective in the era of Quantum Computing. Concepts like Quantum Resistant Ledger are active research areas now.
Conclusion – How should we prepare for Quantum future?
When the global organizations are undertaking AI-first strategy, a few industry sectors like Finance, Logistics, Biotechnology, etc already started evaluating the potential impacts of Quantum Computing. Need of the time is to be aware of the disruptive impact on Cybersecurity and of the transformative impact on business-centric innovation with Quantum Computing.


Review of Short Phrases and Links|
This Review contains major "Electron Spin"- related terms, short phrases and links grouped together in the form of Encyclopedia article.
- Electron spin is the electromagnetic field's angular momentum.
- Electron spin is basically a relativistic effect in which the electron's momentum distorts local space and time.
- Electron spin is roughly analogous to the intrinsic spin of the top.
- The electron spin is the key to the Pauli exclusion principle and to the understanding of the periodic system of chemical elements.
- The electron spin is more of an implosion or explosion in higher dimensional space.
- Paramagnetism results from the electron spin of unpaired electrons.
- The hybrid technology, "thermo-spintronics," would convert heat to electron spin.
- Electron spin resonance (ESR) is a related technique which detects transitions between electron spin levels instead of nuclear ones.
- The train of short optical pulses reduces the continuous density of electron spin precession modes to just three frequencies (blue).
- This is illustrated in Figure 3 for the case when the excess electron spin is initialized to.
- In this study, the interaction of TDH with cell membranes was investigated using electron spin resonance (ESR) techniques.
- Figure 1 depicts a storage qubit (an electron spin in a QD) interacting with a traveling qubit (a single photon) inside a quantum network node.
- Such a system is realized by a single electron spin bound in a semiconductor nanostructure and interacting with surrounding nuclear spins.
- Furthermore, this electron spin could explain the necessity for the number of neutrons added to the nucleus with protons.
- This discovery of the relativistic precession of the electron spin led to the understanding of the significance of the relativistic effect.
- In retrospect, the first direct experimental evidence of the electron spin was the Stern-Gerlach experiment of 1922.
- It does not incorporate the electron spin, and, more fundamentally, the time derivative enters this equation in second order.
- The interaction of the electron spin with the magnetic field is of the same order and should be included together with the E2 and M1 terms.
- The exact same result comes from the quantum mechanics of an electron spin in a magnetic field.
- Because the electron spin has only two allowed projections along any axis, we cannot add a third electron to the n = 1 state.
- The term "electron spin" is not to be taken literally in the classical sense as a description of the origin of the magnetic moment described above.
- This splitting is called fine structure and was one of the first experimental evidences for electron spin.
- Uhlenbeck and Goudsmit later identified this degree of freedom as electron spin.
- Goudsmit on the discovery of electron spin.
- George Uhlenbeck and Samuel Goudsmit one year later identified Pauli's new degree of freedom as electron spin.
- Note that the experiment was performed several years before Uhlenbeck and Goudsmit formulated their hypothesis of the existence of the electron spin.
- Electron spin plays an important role in magnetism, with applications for instance in computer memories.
- Therefore he obtained the precession of the point like magnet instead of the electron spin.
- It predicts electron spin and led Dirac to predict the existence of the positron.
- We call this motion the electron spin and treat it quantum mechanically as another kind of angular momentum.
- In 1921, Otto Stern and Walter Gerlach performed an experiment which showed the quantization of electron spin into two orientations.
- The book looks at applications to the electronic structure of atoms including perturbation and variation methods and a study of electron spin.
- Indeed, the explicit unit imaginary in the Dirac equation is automatically identified with the electron spin in the reformulation.
- Samuel Goudsmit (1902 – 1978) was a Dutch -American physicist famous for jointly proposing the concept of electron spin with George Eugene Uhlenbeck.
- Samuel Goudsmit (1902–1978) was a Dutch-American physicist famous for jointly proposing the concept of electron spin with George Eugene Uhlenbeck.
- Pauli met the train at Hamburg, Germany, to find out Bohr's opinion about the possibility of electron spin.
- He found Pauli and [Otto] Stern waiting for him, wanting to know what he thought of electron spin.
- It is this magnetic moment that is exploited in NMR. Electron spin resonance is a related technique which exploits the spin of electrons instead of nuclei.
- Electron spin resonance is a related technique which exploits the spin of electrons instead of nuclei.
- Each nucleus of spin I splits the electron spin levels into (2I + 1) sublevels.
- Taking electron spin into account, we need a total of four quantum numbers to label a state of an electron in the hydrogen atom: n,,, and s z.
- Classically this could occur if the electron were a spinning ball of charge, and this property was called electron spin.
- Uhlenbeck, along with Samuel Goudsmit, proposed the concept of electron spin, which posits that electrons rotate on an axis.
- Not only is there electron spin that has to be taken into account, but there is also the electron repulsion terms between the electrons.
- These atoms or electrons are said to have unpaired spins which are detected in electron spin resonance.
- When the idea of electron spin was first introduced in 1925, even Wolfgang Pauli had trouble accepting Ralph Kronig's model.
- When Paul Dirac derived his relativistic quantum mechanics in 1928, electron spin was an essential part thereof.
- The following year, Paul Dirac discovered the fully relativistic theory of electron spin by showing the connection between spinors and the Lorentz group.
- Goudsmit, along with George Uhlenbeck, proposed the concept of electron spin, which posits that electrons rotate on an axis.
- Uhlenbeck and Goudsmit one year later identified this degree of freedom as electron spin.
- Physics > Quantum Theory > Quantum Mechanics > Paul Dirac
- Nature > Matter > Particles > Electrons
- Encyclopedia of Keywords > Thought > Concept > Proposing
Books about "Electron Spin" in


15 Mar Switching the Twist in X Rays with Magnets
• Physics 14, 34
Scientists create a pattern of nanomagnets—called an artificial spin ice—that can control the orbital angular momentum of a scattered x-ray beam.
A beam of x rays with a spiral wave front can be used to characterize spin and chiral textures, such as magnetic vortices, hedgehogs, and skyrmions, inside a material. However, generating these twisted x rays isn’t trivial. Over the past two years, scientists have achieved this goal by designing and fabricating extremely precise x-ray optics devices [1, 2]. These devices are passive, meaning that their effect on light is fixed, like that of a lens or a mirror. Now Justin Woods from the University of Kentucky and his colleagues have realized an active device that can control the properties of an x-ray beam on the fly . The team used an engineered nanomagnet array—called an artificial spin ice—that twists x rays by different amounts. By changing the temperature or by using an external magnetic field, the team showed that they could control the amount of twisting and the direction of the outgoing beams. This flexibility could be advantageous for probing or controlling electronic and magnetic systems.
Scientists’ ability to discover and observe fundamental processes in nature has been historically connected to their capability to harness the properties of light. In the early 1960s, the development of the laser and nonlinear optics allowed scientists to exquisitely control the wavelength (energy), polarization (spin angular momentum), wave vector (linear momentum), amplitude, and phase of light. In the 1990s, scientists realized that light beams can also possess a property—the orbital angular momentum (OAM)—which involves the rotation of the beam’s phase around its central axis (OAM) .
The OAM of light is associated with spiral wave fronts of an electromagnetic wave. Different modes exist, distinguished by the “light topological charge,” which corresponds to the number of spirals in the spatial evolution of the phase. OAM is of fundamental interest to the manipulation of light, impacting current and future applications that include optical manipulation of particles, super resolution imaging, optical metrology, optical communications, and quantum computing [5, 6]. Moreover, by extending the OAM of light to shorter wavelengths it should be possible to more sensitively probe chiral and topological structure in matter.
However, imprinting OAM onto ultraviolet and x-ray beams is technically challenging. In the extreme ultraviolet (EUV) region of the electromagnetic spectrum, exciting advances in high harmonic generation have made it possible to generate beams with high spatial and temporal coherence and to tailor their properties by sculpting the driving laser field. These advances have enabled subwavelength imaging  as well as the generation of EUV OAM beams [8, 9]. In the x-ray regime, any optical device requires ultraprecise fabrication—to within a fraction of the x-ray wavelength, corresponding to less than an angstrom (
In their work, Woods and colleagues have engineered and tested a new approach for generating x-ray beams carrying OAM . They fabricated a device based on an artificial spin ice, which consisted of nickel-iron nanomagnets arranged in a two-dimensional lattice. The team designed their spin-ice array so that it contained a specific topological defect consisting of a double dislocation, which looks like a hole in the strings of a tennis racket. The fundamental working principle of their artificial spin ice can be understood as a “fork-dislocation hologram,” which is a slightly warped diffraction grating that has been used since the 1990s to generate visible OAM beams . Similarly, when an x-ray beam struck the spin-ice dislocation, multiple x-ray beams (diffraction modes) scattered out in different directions, each with a different amount of OAM—or light topological charge (Fig. 1).
Interestingly, the defect in the artificial spin ice had two separate scattering effects on an incoming x-ray beam. The double dislocation in the arrangement of electric charges had a “structural topological charge” of 2, which meant it generated diffraction modes with even-order OAM (light topological charge of
, etc.). By contrast, the dislocation in the magnetic spins had a topological charge of 1, which meant it generated odd-order OAM modes (light topological charge of
, etc.) in x rays that were resonant with an absorption line of iron. However, this magnetic scattering was only possible when the artificial spin ice had an antiferromagnetic (antiparallel) ordering of its spins. Thus, the researchers could turn off this scattering by inducing an antiferromagnetic-to-paramagnetic phase transition. This transition was dependent on the temperature and on the external magnetic field. The team showed that they could switch the OAM of the outgoing x-ray beam from a mix of odd and even orders to only even orders—either by increasing the temperature from 270 K to 380 K or by applying a magnetic field.
The use of artificial spin ice with structural topological defects offers new capabilities for x-ray OAM beam generation. Adaptive optical components that can control the OAM properties of x-ray light can enhance our ability to probe and image chiral and topological nanostructures, chiral molecules used in medicines, and magnetic and other materials relevant to nanotechnologies. More opportunities to manufacture other types of active x-ray devices will come from emerging nanomaterials and nanodevices that are reconfigurable or controllable through phase transitions. In combination with devices that create structured EUV and soft x-ray beams through extreme nonlinear optics, these advances can greatly expand the capabilities of structured, short-wavelength light for capturing the time-resolved dynamics and functions of topological structures.
- J. C. T. Lee et al., “Laguerre–Gauss and Hermite–Gauss soft x-ray states generated using diffractive optics,” Nat. Photon. 13, 205 (2019).
- L. Loetgering et al., “Generation and characterization of focused helical x-ray beams,” Sci. Adv. 6, eaax8836 (2020).
- J. S. Woods et al., “Switchable x-ray orbital angular momentum from an artificial spin ice,” Phys. Rev. Lett. 126, 117201 (2021).
- L. Allen et al., “Orbital angular momentum of light and the transformation of Laguerre-Gaussian laser modes,” Phys. Rev. A 45, 8185 (1992).
- Y. Shen et al., “Optical vortices 30 years on: OAM manipulation from topological charge to multiple singularities,” Light Sci. Appl. 8, 90 (2019).
- B. Wang et al., “Coherent Fourier scatterometry using orbital angular momentum beams for defect detection,” Opt. Express 29, 3342 (2021).
- D. F. Gardner et al., “Subwavelength coherent imaging of periodic samples using a 13.5 nm tabletop high-harmonic light source,” Nat. Photon. 11, 259 (2017).
- C. Hernández-García et al., “Attosecond extreme ultraviolet vortices from high-order harmonic generation.,” Phys. Rev. Lett. 111, 083602 (2013).
- L. Rego et al., “Generation of extreme-ultraviolet beams with time-varying orbital angular momentum,” Science 364, eaaw9486 (2019).
- V. Y. Bazhenov et al., “Laser beams with screw dislocations in their wavefronts,” JETP Lett. 52, 429 (1990).


We use physics every day. The laws of motion and momentum govern our movements, and the law of gravity keeps us from floating away — but what about quantum physics? We hear the words used in popular media — it’s mentioned repeatedly in shows like “The Big Bang Theory,” but what do they actually mean? Let’s take a closer look at the field of quantum physics, and how scientists are able to study it.
What Is Quantum Physics?
Quantum physics is similar to standard physics. Classic physics focuses on ordinary nature, things we can see and touch without the need for additional tools. Think of Newton’s apple, when he allegedly discovered the theory of gravity.
Quantum physics is based on a theory called quantization, or the process of transitioning from an understand physical phenomena — like Newton’s apple — to something we can’t see or touch. In essence, quantum physics is the science of the smallest particles in the universe and how they interact with the things around them. Quantum physicists study subatomic particles — photons, electrons, neutrons, quarks, etc. — but how can you study something you can’t see?
Quantum physics, also known as quantum mechanics, made an appearance in the scientific communities in the early 1900s when Albert Einstein published his theory of relativity. However, this field can’t be attributed to any one scientist.
In 1900, a physicist named Max Planck found himself facing a dilemma. According to the laws of physics at the time, if a box was heated up in an environment where no light could escape, it would produce an infinite amount of ultraviolet radiation. At the time, scientists assumed light was a continuous wave. When heating the box didn’t work as they predicted, Planck started to think that light didn’t exist as a wave, but rather as small amounts of energy known as quanta.
He was right. Einstein later theorized that light existed as individual particles, which in 1926 were named photons.
Studying the Universe’s Smallest Particles
How can you study something that is too small for even the most powerful microscope to see? The technology actually dates back to the early 1800s during the discovery and development of the periodic table. Our first glimpse into subatomic particles didn’t come from physics, but rather from chemistry. The first subatomic particle we discovered was the electron, because of the discharge effects of electricity in some gases. Then came protons, the nucleus of the atom and neutrons.
The 1930s brought us the first particle accelerators, and while they were not as high-tech or advanced as the ones we use today, they enabled scientists of the time to accelerate proton beams and measure the size of an atom’s nucleus. Today’s accelerators work on the same principles, producing a beam of charged particles scientists can use to study other subatomic components. They can detect them directly or discover their presence because of the reaction of the charged particles.
The Quantum Uncertainty Principle
Of course, nothing is ever easy in quantum physics. In 1927, Werner Heisenberg of Germany theorized that it is impossible to measure both the position and the velocity of an object at the same time. This theory later became known as the Quantum or Heisenberg Uncertainty Principle, and is one of the foundations of modern quantum mechanics.
It doesn’t work for items we can see. You can easily tell the velocity and position of an apple falling from a tree — 5.8 meters per second squared, based on the law of gravity — but it’s not as easy to determine either of these things when you’re talking about a particle that’s impossible to view with the naked eye.
Remember Schrodinger’s thought experiment, in which a cat was in a box with poison? The cat is both alive and dead until it is observed to be one or the other. That applies to the Heisenberg Uncertainty Principle as well. Any attempt to measure the velocity or position of a subatomic particle will affect both measurements in such a way that no actual analysis is possible. The mere act of observation changes the outcome of the experiment.
This is what makes quantum physics so challenging as a field. Anything we learn is colored by the act of learning it — but that doesn’t mean we haven’t made any significant discoveries.
Recent Discoveries in Quantum Physics
Quantum physics has taken off in recent years. 2018, in particular, was a phenomenal year for scientific advancements. Scientists trying to create quantum computers managed to pack 18 qubits of information into six photons. We’ve discovered that life on this planet may rely on some form of quantum entanglement, with particles linked together at a subatomic level.
We’ve found that there are actually two types of water molecules — one where the hydrogen and oxygen atoms point in the same direction, and one where they’re pointing in opposite directions. Military radar technology may even be getting an upgrade thanks to quantum mechanics. By using entangled photons, scientists hope to create a stealth-busting radar that will notify them if they are being tampered with or encountering problems. This is based on the readings generated by photons back at base.
This is just a fraction of the amazing discoveries we’ve made in the last year alone.
The Future of Quantum Physics
We’ve barely scratched the surface of the quantum universe, and as new discoveries trickle in, they’re likely to alter our understanding of everything — from science to life itself. It’s an exciting time to be alive, and we can’t wait to see what new advances are on the horizon.


For a scientist whose career was made by his work on black holes, it might seem a little confusing to read that Stephen Hawking now thinks that they don’t exist. But that’s what “Information Preservation and Weather Forecasting for Black Holes,” the study Hawking published last week on arXiv, says: “there are no black holes.”
While this might seem surprising–after all, there’s a huge amount of (indirect) evidence that black holes exist, including a massive one several million times the mass of our Sun at the centre of the Milky Way—it’s really not. It’s Hawking’s latest attempt to solve a paradox that he, and other astrophysicists, have been grappling with for a couple of years.
So what’s he talking about? Here’s the background: black holes are objects which are so massive, with such strong gravity, that even light can’t escape. The distance from the black hole, beyond which nothing gets out, is the event horizon. However, Hawking made his name in the 1970s when he published a paper showing that black holes don’t just suck stuff up, endlessly—they spew out a beam of so-called “Hawking radiation” as they absorb other matter. That means black holes actually lose mass over time, eventually whittling away to nothing.
Black holes are frustrating, though, because their extreme gravity exposes the major inadequacy in our current scientific understanding of the universe - we don’t know how to reconcile quantum mechanics and general relativity. With general relativity, we can make accurate predictions about objects with certainty, but on the tiny scale of quantum mechanics it’s only possible to talk about the behaviour of objects in terms of probability. When we do the maths on what happens to things that fall into black holes, using relativity gives results that break quantum mechanics; the same goes vice versa.
One of the key things about quantum mechanics is that it tells us information can’t be destroyed–that is, if you measure the radiation given off by a black hole, you should be able to build up a picture of what matter fell into the hole to create it. However, if general relativity holds, and nothing can escape from inside the event horizon, then that should apply to that quantum information–any radiation that’s coming out is, Hawking showed, random. It’s the black hole “information paradox.” Either give up quantum mechanics, or accept that information can die.
Hawking was in the “information can die” camp, until 2004, when it became clear—thanks to string theory—that quantum mechanics held up (and there’s an excellent in-depth explanation of this in Nature that explores this story more fully if interested). There was just one problem—nobody could work out *how* information was getting out of black holes, even if it was happening mathematically.
And, just in case this wasn’t all entirely confusing, it turns out that our best post-2004 theory about what’s been going on gives rise to an entirely new paradox—the “firewall.”
It’s to do with quantum entanglement, where two particles are created that are identical on the quantum level. The way it works isn’t exactly clear yet—it could be something to do with string theory and wormholes—but it means that measuring the properties of one particle will give readings that mirror those found on its entangled particle. It might lead to teleportation technology, but scientists aren’t sure yet.
Joseph Polchinski from the Kavli Institute for Theoretical Physics in Santa Barbara, California published a paper in 2012 that worked out the information paradox could be solved if Hawking radiation was quantum entangled with the stuff falling in. But, due to the limitations of entanglement, if this is true, that would mean that at the event horizon a massive amount of energy was given off by particles entering and leaving.
Hence “firewall”—anything crossing the event horizon would be burnt to a crisp. And even though most scientists, including Polchinski, thought this couldn’t possibly be right—it completely contradicts a lot of the stuff underlying general relativity, for example—nobody’s yet managed to disprove it.
The choice for physicists, once again, was to: a) accept the firewall, and throw out general relativity, or b) accept that information dies in black holes, and quantum mechanics is wrong.
Still with me? Here’s where Hawking’s latest paper comes in.
(That title—“Information Preservation and Weather Forecasting for Black Holes”—might make some more sense too, hopefully.)
Hawking’s proposed solution, building on an idea first floated in 2005, is that the event horizon isn’t as defined as we’ve come to imagine it. He instead proposes something called an “apparent horizon,” which light and other stuff can escape from:
"The absence of event horizons mean that there are no black holes—in the sense of regimes from which light can't escape to infinnity. There are however apparent horizons which persist for a period of time."
Black holes should be treated more like massive galactic washing machines. Stuff falls in and starts getting tossed around, mixed up with other stuff in there, and only eventually is allowed to escape out again when ready. This happens because the quantum effects around a black hole, like weather on Earth, churn so violently and unpredictably that it’s just impossible to either predict the position of an event horizon or expect uniform effects for stuff crossing it. While the theoretical basis, that information is preserved, remains, in practice it's so difficult as to be impractical.
It’s a fudge of an idea, which tries to have its general relativity and quantum mechanics cakes, and eat them, too. Possible weaknesses, as Nature points out, are that it could imply that escaping from black holes is easier than it is in reality. It could also be the apparent horizons are just as much of a firewall as the traditional conception of an event horizon. Hawking's peers have yet to have a go at assessing his idea, so we'll have to wait to see whether the idea has merit—or whether it merely gives rise to yet more paradoxes.
This piece first appeared on newstatesman.com.
Image via Shutterstock.