Every time you send an email, connect to your bank account or check your medical examination, you rely on random numbers to protect the security of your online activity. Cryptography is the set of tools we use to keep us safe online, and random numbers are the foundation in which cryptography is built upon. In other words, if we could not generate unpredictable random digits, secure online communications would not be possible.
While there are many ways to generate “random numbers”, not all of them are good enough for cryptographic use. For instance, computers are unable to produce random digits on their own, unless we help them with external hardware means. The reason is simple: a computer is a machine designed to reliably execute one instruction after another, in a completely predictable and repeatable way.
That said, computers have functions and instructions to generate so-called pseudo-random numbers (PRNGs), which produce sequences of digits with certain “random” statistical properties. But the random numbers produced from a PRNG are completely predictable and therefore cannot be used “as is” for cryptographic applications.
The way to bring randomness (or unpredictability, to be more precise) to computers for cryptographic use is via so-called true random number generators (TRNGs).
How do true random number generators (TRNGs) work?
TRNGs are based on measuring a specific (random) physical process to produce random digits. Thus, the randomness of such numbers comes from the underlying physical process, which may indeed be completely unpredictable. TRNGs are the baseline for security applications.
TRNGs are hardware components and sophisticated engineering is required to build them properly. Unfortunately, current communication systems rely on weak TRNG designs, compromising security and/or performance of the communications. There are mainly two reasons for this reliance on weak TRNG designs. First, some systems do not even have a dedicated TRNG hardware component, due to cost or design choice, thus relying on generic components in the system to produce random samples (e.g., clock interrupts from the operating system). Second, many TRNGs are designed based on physical principles that are complex and therefore produce “random-looking” dynamics (e.g., chaos), but which are, by principle, predictable and deterministic, which a sufficiently motivated attacker or a badly operated system may reveal to compromise security.
Building reliable, fast and unpredictable TRNGs is essential for the present and future of cryptography. And Quantum technologies are now being used to produce quantum-enhanced TRNGs, that is How do quantum number generators work.
What is a quantum random number generator?
Quantum random number generators (QRNGs) are a special case of TRNG, that generate randomness by measuring quantum processes, which are, by nature non-deterministic. The advantages are multiple, including a fundamental advantage in using quantum indeterminacy, typically faster performances by leveraging photonics and most importantly, the ability to understand and verify the origin of unpredictability, which is a core assurance for the entire cybersecurity chain.
Until now, engineering high-quality, scalable and fast quantum random number generators has been a challenge to date, and this is the area Quside has been pushing to advance over the last decade. Our proprietary technology allows for fast, high-quality, and scalable production, leading to a solution that is ready for today’s unpredictability concerns and tomorrow’s performance requirements.
Fast and measurable random number solutions by Quside
Quside has been researching, engineering and producing high-quality QRNGs for over a decade. The proprietary technology that Quside has put together provides 3 major advantages:
Fast: Quside products can generate hundreds of Mb/s and even Gb/s already today. We leverage photonics to produce very fast random streams.
Measurable: using our peer-reviewed Randomness Metrology methods, our customers can access transparently quality metrics that directly relate to the quantum physical principle responsible for unpredictability.
Unpredictable: we use a largely peer-reviewed quantum process to generate randomness, thus harnessing nature to enhance entropy production.
Additionally, Quside has also put a major effort on scaling the technology, which can be today produced at scale using photonic integrated chips (PICs).
How are quantum random numbers generated?
About Quside’s phase-diffusion technology, Quside QRNGs are based on the phase-diffusion process in semiconductor lasers. The core element of the technology is converting microscopic quantum observables, which are delicate and hard to measure, into macroscopic dynamics that are robust and easy to capture. To do this, we modulate a semiconductor laser from below to above its threshold level or produce a stream of phase randomized optical pulses. This is called gain-switching.
Then, we use an interferometer to convert the phase fluctuations into the amplitude domain, generating a stream of amplitude-randomized optical pulses at the output (see refs [2, 3] for two examples of interferometers that we use). Finally, a fast photodiode converts the photonic signal into the electronic domain, where standard electronics are used for turning the analog signal into the digital realm.
At the heart, the unpredictability of the phase-diffusion technology traces back to the process of spontaneous emission, which occurs as a result of the interaction between the quantum vacuum field and the laser’s gain medium. Quside’s technology exploits this quantum-mechanical process to produce quantum-based random numbers at multiple Gigabits per second.
More about the Randomness metrology
Testing randomness is a complex matter and the way it has been traditionally done is completely flawed. The question “how do you know it is random?” is a hard one to answer, and this is an area where we have been working since 2012, introducing our randomness metrology methodology in 2014 and collaborating with world-leading researchers from NIST, IQOQI and TU Delft to apply it in landmark experiments.
Our methodology defines strict quality bounds on all our devices to capture the quality of the unpredictability we produce, and the best part is that we can confidently do it in a transparent manner. This boosts trust and confidence with our customers, who do not have to rely on black boxes anymore for producing their cryptographic material.
In many traditional TRNGs, not based on quantum processes, it is extremely hard or even impossible to place rigorous quality bounds. As randomness is not emerging from a fundamentally random process.
Quantum Random Number Generator solutions
Start using fast and measurable quantum randomness with Quside. Securing communications is undeniably one of the most important endeavors of our society today. New cryptographic standards are now emerging, to enhance even further our protection and governments are releasing their mandates to transition the security of their networks and data, as the Quantum Computing Preparedness Cybersecurity Act by the US government on July 14th, 2022.
Migrating to the new post-quantum standards with a hybrid security approach in mind is essential and the time to act is now and building a strong randomness generation foundation on which the new standards can rely upon is equally important.
Remember that no security can be achieved unless we can produce unpredictable random numbers, and the question is: are we producing them? How do we now? Using the highest quality randomness generation technologies and monitoring them properly is where Quside can get you to the next level.
Frequently Asked Question
What is a quantum random number generator?
It is a hardware component that is used to generate unpredictable random numbers, typically for cryptography or computation applications.
How do quantum number generators work?
A quantum random number generator (QRNG) generates streams of random digits by sampling a signal that contains sufficiently large quantum dynamics.
Who has developed the quantum random number generator?
There are various companies and research labs that have created and built QRNGs. Quside is a leading supplier of high-performance QRNGs.
Why do we need QRNG?
QRNGs provide several advantages to generate random numbers in applications as cryptography, including the strongest form of unpredictability, the ability to measure the quality through first principles and typically faster performance.
Co-funder & CEO
PhD in quantum technologies at ICFO, where he developed the quantum randomness technologies that were transferred to Quside. 10 years of experience in quantum and photonics technologies, co-inventor of multiple patent families and co-author of 15+ papers in top scientific journals. Received the award MIT Innovators Under 35 Europe.
A research collaboration between Quside, ICFO, and others, has shown how using quantum random number generators provide the required quality and efficiency for safely running even the most complex stochastic simulations.
Quantum random numbers for physics-inspired optimization problems
Making decisions is commonly a challenge due to the uncertainty and overwhelming information needed to deal with a problem: from every engineering design, data analysis or most business decisions to...


A quantum mechanics experiment performed by physicists at the University of California in Santa Barbara has been honored by the journal Science as its Breakthrough of the Year. The researchers’ work may shed light on just what actually gravity is, among other things.
The team, led by Andrew Cleland, showed that a relatively large object’s reactions can be predicted by quantum mechanics theory.
“The real impact of our experiment is more in the foundations of physics in the sense that it helps show quantum mechanics still applies to large objects,” Cleland told TechNewsWorld.
“If you can do quantum mechanical experiments with objects that are big enough, you could see what effect gravity has on a quantum mechanical system.”
Although gravitation is the weakest of four fundamental forces, or interactions, that make up every physical phenomenon, it’s has several unique features, one of them being that it has infinite range.
About the Experiment
Cleland’s team, which consisted of himself, fellow physicist John Martinis and doctoral student Aaron O’Connell, basically took a microwave frequency mechanical resonator and wired it to a superconducting qubit, then cooled the whole thing to near absolute zero and zapped it with a little energy to see what would happen.
They then took this resonator and put it in a quantum superposition, a state in which it simultaneously had zero and one quantum of excitation. Energetically, this is the same as being in two places at the same time.
A qubit is a bit of quantum information. Like a bit in computing, it can have two possible values — a 0 or a 1. Unlike a bit, it can be 0, 1 or both together, which is called a “superposition.”
A superposition is a quantum mechanical property of a particle that lets it occupy all its possible quantum states simultaneously. The particle can be thought of as omnipresent in its superposition, if you like.
A superconducting qubit results when you use nanofabricated superconducting electrodes coupled through Josephson junctions.
A Josephson junction consists of a thin layer of non-superconducting material between two layers of superconducting material. Think of it as a ham sandwich without mayo, butter or condiments. Superconducting qubits go right through the non-superconducting material.
Cleland’s team cooled its gadget to its lowest-energy state, in this case zero. This is called the “ground state.”
“We got a dilution refrigerator; it’s a piece of commercial apparatus that anybody can buy for a couple of hundred thousand dollars,” Cleland said. “It’ll cool a few kilos of copper to about two hundredths of a degree above zero.”
His team then cooled the resonator to its quantum ground state, then applied one quantum unit, or phonon, of energy.
A phonon is a quantum mechanical description of a vibration in which a lattice uniformly oscillates at one frequency, known as the “normal mode.”
Cleland’s team then measured the result with “classical equipment,” Cleland said. The resonator has a resonance frequency of 6 GHz, and the energy exchange rate was 100MHz, Cleland stated.
The team had to do this repeatedly in order to get and verify its results.
“One of the features of quantum mechanics is that, when you do a measurement, you destroy the state that was prepared,” Cleland explained. “We prepped the system, measured, then recorded the measurement on a state that was prepared identically thousands of times.”
Possible Uses for the Discovery
Cleland’s team made its discovery while it was trying to build a quantum computer.
Quantum mechanics directly use quantum mechanical phenomena such as superposition and entanglement to work on data. Entanglement is a state in which two or more objects have their quantum states linked together so that you have to describe both and can’t describe either on its own.
Cleland’s team also might use it in quantum communications, wherein quantum information is encoded into invisible light.
Quantum information has no analog in standard information theory. The quantum nature of systems must be preserved in order to process information in a quantum computer or to distribute a secret key in quantum cryptography.
Quantum communications might be used in teleportation.
However, Cleland’s vision is a little more down-to-earth, in a sense — in the nearer future, the results of the experiment might help physicists better understand gravity.
“Quantum mechanics works really well for small objects like atoms and electrons,” Cleland said. “But for large mechanical systems, there’s not been any good demonstrations, and there’s been this question as to whether quantum mechanics really applies to big mechanical things.”
Even though the object used in the experiment was the size of a human hair, it was still “a trillion times bigger” than those used in previous experiments. It shows that the laws of quantum mechanics apply to relatively large objects.
Widespread, practical application of this kind of research is still a long way off, Rob Enderle, principal analyst at the Enderle Group, told TechNewsWorld. “It’s to prove quantum theories and build up a base of knowledge so that more complex and more practical applications can be derived.”
Eventually, products made using this knowledge might relate to near-instant communications over long distances, new sources of energy, and more efficient use of energy, Enderle stated.
“We are at the stage where we’re looking to see if it’s possible to walk,” Enderle remarked. “Then we have to figure out how to walk; then actually walk. Running is the goal.”
Short of a major breakthrough, we’re 20 to 50 years away from mass-market products based on advances in quantum mechanics, Enderle said.


LTAT.00.014 3-6 ECTS
Basqect* — Basics of Quantum Error Correction
Classical communication and computation devices are prone to errors, which makes (classical) error correcting codes necessary. For quantum devices, that problem is much larger: Not only are quantum errors more pervasive, but correcting them is more subtle, as inspecting the quantum state in order to fix it is not easily possible without destroying it.
While some low-hanging fruit in quantum communication and quantum computation can be reached on noisy quantum devices (e.g., simple Quantum Key Distribution in quantum communication, or simple simulations of condensed matter with quantum computing devices), for the second quantum revolution (arising from the ability to coherently manipulate quantum systems) to happen, quantum error must be corrected.
Luckily, based on Nobel-prize worthy ideas of Peter Shor from the late 1990s, correcting quantum error and "undoing" decoherence (due to interaction of the quantum device with the rest of the universe) is possible, and can be understood and applied by the math-capable student.
*) Pronounce like "basket" 🙄
Students with undergraduate degree in Math have additional options: Contact the instructors!
The course will focus on the predominant proposal for quantum error correction, which is based on so-called Stabilizer Codes. As the target audience is computer science students, it is necessary, though, to go through some math first. At the end of the course, the successful student will have an understanding of stabilizer codes, and how to use them to correct Pauli quantum errors.
- Math: Basics of group theory (normal subgroups, isomorphism theorems, group actions, centralizer, normalizer, etc)
- Quantum: Groups of unitary operators, Pauli groups and Clifford groups
- Math: Linear algebra over the field with 2 elements
- Quantum: GF(2)-arithmetic for Pauli (sub-)groups
- Quantum: Review of density operators and quantum channels
- Quantum: Stabilizer codes and their stabilizer groups, dimension of the code space, logical qubits, logical operations, error syndromes and error correction
- Examples: 5-qubit code, Steane 7-qubit code, 9-qubit Shor-code....
There are rumors according to which it might be possible to teach the subject matter more in a "physics style". The present course (designed and taught by mathematicians) is strictly mathematical, though, involving lots of yummy rigorous proofs.
This is a "Book Course", i.e., students mostly learn independently, based on lecture notes handed out to them...
... But this is the first time that this "Book Course" is being taught, and we will have to figure out what the best organization is. Current plan:
- The course takes place in weeks 1-8 of the semester
- Two class meetings per week: One with the instructor (to learn from / discuss with them), and one without the instructor (for students to learn from / discuss with each other).
- Homework (reviewing, reading, simple exercises, understanding) is not handed in / marked.
- The lecture notes will be created in parallel with actual black-board lectures.
- Pass-fail evaluation by exam (written? oral?)
- Javier Gil Vidal (classes)
- Assoc Prof Dirk Oliver Theis (design & content)
At the end of this 3-ECTS course, you can correct quantum errors — which is amazing! In terms of quantum communication, it already gets you somewhere.
The 6 ECTS reading course LTAT.00.0015 "Fatol Surf" (FAult TOLerance with SURFace codes) subsumes the content of LTAT.00.0014, and then, in the second half of the semester, moves to fault tolerant quantum computing based on surface codes (a special type of stabilizer codes) — the real deal. Fatol Surf is considerably more demanding though, than Basqect: It has no lectures, and the reading consists of a couple of research papers. The number of participants in Fatol Surf is limited; students specializing in quantum computing are preferred. The ultimate goal of Fatol Surf is: (1) to understand what really happens on a quantum computing device (e.g., quantum repeater, quantum computer) with error correction when a quantum algorithm is executed; and (2) to become able to experiment with quantum codes (surface or not) on quantum communication or computing devices, and so contribute to the quantum revolution.
This course's page on Quantum Computing at the University of Tartu.
ECTS and the relationship between Basqect and Fatol Surf
- Basqect (LTAT.00.0014) keeps you busy busy for a half semester (= 3 ECTS).
- Fatol Surf (LTAT.00.0015) keeps you busy for the full semester (= 6 ECTS), the first half of which is the content of Basqect.


In computing and telecommunications a bit is a basic unit of information storage and communication; it is the maximum amount of information that can be stored by a device or other physical system that can normally exist in only two distinct states. These states are often interpreted (especially in the storage of numerical data) as the s 0 and 1. They may be interpreted also as logical values, either "true" or "false"; or two settings of a flag or switch, either "on" or "off".
In information theory, "one bit" is typically defined as the uncertainty of a binary random variable that is 0 or 1 with equal probability, or the information that is gained when the value of such a variable becomes known.
In quantum computing, a quantum bit or qubit is a quantum system that can exist in superposition of two bit values, "true" and "false".
The symbol for bit, as a unit of information, is "bit" or (lowercase) "b"; the latter being recommended by the IEEE 1541 Standard (2002).
The encoding of data by discrete bits was used in the punched cards invented by Basile Bouchon and Jean-Baptiste Falcon (1725), developed by Joseph Marie Jacquard (1804), and later adopted by Semen Korsakov, Charles Babbage, Hermann Hollerith, and early computer manufacturers like IBM. Another variant of that idea was the perforated paper tape. In all those systems, the medium (card or tape) conceptually carried an array of hole positions; each position could be either punched through or not, thus potentially carrying one bit of information. The encoding of text by bits was also used in Morse code (1844) and early digital communications machines such as teletypes and stock ticker machines (1870).
Ralph Hartley suggested the use of a logarithmic measure of information in 1928. Claude E. Shannon first used the word bit in his seminal 1948 paper A Mathematical Theory of Communication. He attributed its origin to John W. Tukey, who had written a Bell Labs memo on 9 January 1947 in which he contracted "binary digit" to simply "bit". Interestingly, Vannevar Bush had written in 1936 of "bits of information" that could be stored on the punch cards used in the mechanical computers of that time.  The first programmable computer built by Konrad Zuse used binary notation for numbers, whose bits were realized as electrical relays which could be either "open" or "closed".
Transmission and processing
Bits can be implemented in many forms. In most modern computing devices, a bit is usually represented by an electrical voltage or current pulse, or by the electrical state of a flip-flop circuit. For devices using positive logic, a digit value of 1 (true value or high) is represented by a positive voltage relative to the electrical ground voltage (up to 5 volts in the case of TTL designs), while a digit value of 0 (false value or low) is represented by 0 volts.
In semiconductor memory, such as dynamic random-access memory or flash memory, the two values of a bit may be represented by two levels of electrical charge stored in a capacitor. In programmable logic arrays and certain types of read-only memory, a bit may be respresented by the presence or absence of a conducting path at a certain point of a circuit. In magnetic storage devices such as magnetic tape, magnetic disc, or magnetic bubble memory, it may be represented by the polarity of magnetization of a certain area of a ferromagnetic film. In optical discs, a bit is encoded as the presence or absence of a microscopic pit on a reflective surface.
Information capacity and information content
Information capacity of a storage system is only an upper bound to the actual quantity of information stored therein. If the two possible values of one bit of storage are not equally likely, that bit of storage will contain less than one bit of information. Indeed, if the value is completely predictable, then the reading of that value will provide no information at all (zero bits). If a computer file that uses n bits of storage contains only m < n bits of information, then that information can in principle be encoded in about m bits, at least on the average. This principle is the basis of data compression technology. Sometimes the name bit is used when discussing data storage while shannon is used for the statistical bit.
There are several units of information which are defined as multiples of bits, such as byte (8 bits), kilobit (either 1000 or 210 = 1024 bits), megabyte (either 8,000,000 or 8×220 = 8,388,608 bits), etc.
Computers usually manipulate bits in groups of a fixed size, conventionally named "words". The number of bits in a word varies with the computer model; typically between 8 to 80 bits; or even more in some specialized machines.
The International Electrotechnical Commission's standard IEC 60027 specifies that the symbol for bit should be "bit", and this should used in all multiples, such as "kbit" (for kilobit). However, the letter "b" (in lower case) is widely used too. The letter "B" (upper case) is both the standard and customary symbol for byte.
In telecommunications (including computer networks), data transfer rates are usually measured in bits per second (bit/s) or its multiples, such as kbit/s. (This unit is not to be confused with baud.)
When a bit within a group of bits such as a byte or word is to be referred to, it is usually specified by a number from 0 (not 1) upwards corresponding to its position within the byte or word. However, 0 can refer to either the most significant bit or to the least significant bit depending on the context, so the convention of use must be known.
Certain bitwise computer processor instructions (such as bit set) operate at the level of manipulating bits rather than manipulating data interpreted as an aggregate of bits.
Other information units
Other units of information, sometimes used in information theory, include the natural digit also called a nat or nit and defined as log2 e (≈ 1.443) bits, where e is the base of the natural logarithms; and the decit, ban or Hartley, defined as log210 (≈ 3.322) bits.. Conversely, one bit of information corresponds to about ln 2 (≈ 0.693) nats, or log10 2 (≈ 0.301) Hartleys. Some authors also define a binit as an arbitrary information unit equivalent to some fixed but unspecified number of bits.)
- Units of information
- Integral data type
- Primitive type
- Information entropy
- Binary arithmetic
- Ternary numeral system
- John B. Anderson, Rolf Johnnesson (2006) Understanding Information Transmission.
- Simon Haykin (2006), Digital Communications
- Norman Abramson (1963), Information theory and coding. McGraw-Hill.
- Darwin among the machines: the evolution of global intelligence, George Dyson, 1997. ISBN 0-201-40649-7
- National Institute of Standards and Technology (2008), Guide for the Use of the International System of Units. Online version.
- Amitabha Bhattacharya, Digital Communication


What is Digital Computer : The digital computer is a type of electronic device that can process and store data electronically. It was first created in the 1940s and has since been used in many different ways. A digital computer is a machine that manipulates and processes data using ones and zeros. The ones and zeros are the basic building blocks of digital information.
- What is a Digital Computer and What are its Key Components?
- History of Digital Computers: From Vacuum Tubes to Transistors
- Applications of Digital Computers: From Military to Business
- Future of Digital Computers: Emerging Technologies and their Impact
- What are Digital Computer and their Types?
- Where are Digital Computers used?
- Advantages and Disadvantages of Digital Computer
What is a Digital Computer and What are its Key Components?
Digital computers store information in bits, which are groups of one or more zeros and ones. Each bit can represent either a one (1) or a zero (0). A digital computer consists of a processor, memory, input/output devices, and a bus.
The processor is responsible for executing the instructions stored in memory to perform operations such as data manipulation, calculation, and text processing. Input/output devices allow the computer to communicate with other devices outside of it. The bus allows multiple devices to interact with each other.
History of Digital Computers: From Vacuum Tubes to Transistors
Digital computers are ubiquitous in modern society. From the humble vacuum tube computer of the 1950s, to today’s digital devices like smartphones and tablets, the history of digital computers is a story of technological innovation and dramatic change.
In this article, we’ll explore some of the key milestones in digital computer development, from vacuum tubes to transistors, and look at how these early machines paved the way for the modern age of computing.
The digital computer is a technology that emerged in the 1940s and 1950s. The first programmable computers were developed at the start of World War II, but it was not until the 1960s that digital computers became commercially available.
- The first commercial digital computer, the Ferranti Mark 1 was introduced in 1957.
- The first mass-produced computer, the IBM System /360 , was introduced in 1964.
- The first commercial personal computer, the IBM PC , went on sale in 1981.
- The mass-produced personal computer was the Macintosh , released by Apple in 1984.
- The first tablet computer was the Apple iPad , which began selling in 2010.
- The first microprocessor was developed in 1971 by the Intel Corporation.
In 1972, Intel released the 4004 , the first single-chip microprocessor that could be used to implement an entire computer system on a single integrated circuit.
- Delta Computer Systems: A History of Innovation
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- Ada Lovelace: The Mother Of Computer Programming
Applications of Digital Computers: From Military to Business
As digital computers have become more ubiquitous in business and military settings, their applications have expanded to include a diverse range of fields. Here are Six examples:
1) Financial analysis: By tracking stock prices, businesses can make better decisions about when to sell stocks and invest in new ventures. Digital computers also help researchers predict patterns in financial data.
2) Manufacturing: With 3D printing technology becoming more affordable, businesses can create customized products on-demand. Computers are also used to monitor the production process and optimize efficiency.
3) Surveillance: Businesses use video surveillance systems to keep an eye on their customers and employees. Digital cameras and software can detect movements that could indicate criminal activity.
4) Medicine: Doctors use digital images to screen patients for diseases. They can also use diagnostic tools like CAT scans and MRI scans to measure brain tumors and other problems.
5) Tourism With the rise of social media, tourists use cameras to capture their experiences and share them with friends. This helps increase the popularity of a place and draw in more visitors.
6) Security Cameras are a vital part of security systems that keep dangerous criminals and intruders out.
The Role Of Cameras In Surveillance Systems The role of digital cameras in surveillance systems gets you the highest quality pictures with excellent resolution.
Future of Digital Computers: Emerging Technologies and their Impact
Digital computers have been with us for almost 50 years and are still with us today. However, there are many emerging technologies that are poised to have a significant impact on digital computers in the future.
These include quantum computing, neuromorphic computing, and artificial intelligence. It is still too early to say which of these technologies will ultimately dominate, but they all hold promise for making digital computers even more powerful and efficient.
What are Digital Computer and their Types?
Digital computers are devices that use electric signals to process data. There are three main types of digital computers: central processing units (CPUs), graphics processing units (GPUs), and embedded systems. CPUs are the most common type of digital computer, and they include the processors used in laptops, desktops, servers, and other devices.
GPUs are designed for gaming and video rendering, but they can also be used to handle complex mathematical calculations. Embedded systems are tiny computer chips that can be found in everything from cars to smartwatches.
Where are Digital Computers used?
There are many places around the world where digital computers are used. Some common locations include research labs, factories, and offices.
Advantages and Disadvantages of Digital Computer
The advantages of digital computers are many. They are fast, efficient, and reliable. They can store large amounts of data, often making them the choice for businesses and organizations.
Some disadvantages of digital computers include their high price tag and their reliance on electrical power.
The digital computer has revolutionized the way we live and work. It has made our world more efficient, and it has allowed us to do things that we never thought possible. We can now interact with the world around us in ways that were once unimaginable, and we can do so without ever having to leave our homes. This technology is here to stay, and it will continue to evolve in ways that we can only imagine.


Images of the electron trap architecture. Top: Schematic representation of the experiment. Current of surface electrons, induced by ac voltage applied to the electrode underneath Reservoir 1, flows between Reservoirs 1 and 4, as shown by the red arrow. Middle: Cross section of the central microchannel around the gate area. Bottom: Photograph of the microchannel device on a copper sample cell, with subsequent close-up photographs of the central channel and surrounding reservoirs.Credit: Denis Konstantinov
The future of quantum computing is a hot topic not only for experts but also in many commercial and governmental agencies. Rather than processing and storing information as bits in transistors or memories, which limit information to the binary ‘1’ or ‘0’, quantum computers would instead use quantum systems, such as atoms, ions, or electrons, as ‘qubits’ to process and store “quantum information” in, which can be in an infinite number of combinations of ‘1 and 0’. Large technology corporations, such as Google, Microsoft, Intel, and IBM are investing heavily in related projects that may lead to realize the quantum computer and technologies. At the same time, universities and research institutes around the world are researching novel quantum systems, adoptable for quantum computing.
The Quantum Dynamics Unit at the Okinawa Institute of Science and Technology Graduate University (OIST), has recently made novel findings about electrons floating on the surface of liquid helium, a quantum system which may be a new candidate for quantum computing into reality. These results were published in Physical Review B.
One of the common problems in quantum computing research using solids is that it is very difficult to make perfectly identical qubits because intrinsic defects or impurities in the materials used randomly affect each individual qubit performance. “Our motivation for pursuing a liquid helium system is that it is intrinsically pure and free of defects, which theoretically allows for the creation of perfectly identical qubits. Additionally, we can move electrons in this liquid helium system, which is difficult or nearly impossible in other quantum systems,” explained Prof. Denis Konstantinov, head of the Quantum Dynamics Unit. Therefore, it is believed that adopting this system for quantum computing might bring the whole field to the next level.
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Utilizing electrons on a liquid helium surface for quantum computing requires isolating individual electrons on a helium surface and controlling their quantum degrees of freedom, either motional or spin. It may also require the movement of electrons to different locations, thus it is also important to understand the physics of the interaction between electrons and the helium surface. It was previously discovered that electrons on helium can form a two-dimensional crystal, and some unique phenomena occur when this crystal moves along the helium surface, due to the interaction between electrons and surface waves.
The OIST scientists, however, are the first to probe how these phenomena depend on the size of the electron crystal. To test this, Dr. Alexander Badrutdinov, Dr. Oleksandr Smorodin and OIST PhD student Jui-Yin Lin, built a microscopic channel device that contained an electron trap within to isolate a crystal of a relatively small number of electrons. This crystal would then be moved across the liquid helium surface by altering electrostatic potential of one of the device electrodes. This motion would be detected by measuring image charges, which are induced by the moving electrons, flowing through another electrode using a commercially available current amplifier and lock-in detector. “This research gave us some insights into the physics of the interaction between electrons and the helium surface, as well as expanded our micro-engineering capabilities” states Dr. Alexander Badrutdinov, a former member of the Quantum Dynamics Unit and the first author of the paper. “We successfully adopted a technology to confine electrons into microscopic devices, on the scale of few microns. With this technology we studied the motion of microscopic two-dimensional electron crystals along a liquid helium surface and saw no difference between the movement of large electron crystals, on the scale of millions to billions of electrons, and crystals as small as a few thousands of electrons, when theoretically, differences should exist.”
This research is the first step at OIST in the prospect of using this system for quantum computing. According to Konstantinov, “the next step in this research is to isolate an even smaller electron crystal, and ultimately, a single electron, and to move them in this system. Unlike other systems, this system has the potential to be a pure, scalable system with mobile qubits.” In theory, this type of system would have the potential to revolutionize the quantum computing research field.
A.O. Badrutdinov, A. V. Smorodin, D. G. Rees, J. Y. Lin, D. Konstantinov. Nonlinear transport of the inhomogeneous Wigner solid in a channel geometry. Physical Review B, 2016; 94 (19) DOI: 10.1103/PhysRevB.94.195311


What is Quantum Computing
There is an international race to build a quantum computer that transcends the capacity of conventional computers and to build ultra-secure communication networks – a race that has been called the space race of the 21st century.
These technologies have the potential to transform the information economy and create the industries of the future, solving in hours or minutes problems that would take conventional computers – even supercomputers – centuries, and tackling otherwise intractable problems that even supercomputers could not solve in a useful timeframe.
Present-day computers are really fast, and they are getting very powerful, however they aren’t fast enough to perform all of the calculations that we need them to in a useful time frame.
Quantum computers use quantum mechanics to perform certain complex calculations in a smaller number of steps than an ordinary computer. However, not all algorithms run faster on quantum hardware – only certain ones with particular features. Identifying exactly which problems can benefit from quantum computing is an active area of research today.
Potential applications include machine learning, scheduling and logistical planning, financial analysis, stock market modelling, software and hardware verification, rapid drug design and testing, and early disease detection and prevention.
A 2020 report from CSIRO revealed that quantum computing in Australia has the potential to create 10,000 jobs and A$2.5 billion in annual revenue by 2040, while spurring breakthroughs in drug development, industrial processes, and machine learning.
A quantum computer is a machine that performs its calculations by harnessing the unique features of quantum mechanics.
In ordinary computing, information is stored in bits, and each bit stores either a 0 or a 1. Many bits together can represent all sorts of information using binary code, which computers can process.
Quantum computers process quantum information, which is stored in quantum bits, called qubits (pronounced “KYU-bits”). A qubit can be any quantum object with two states – for example, a single electron (spin up or spin down) or a single photon (polarised horizontally or vertically).
In everyday life, we usually have a good intuition regarding how the physical world will behave. Drop a glass and it will smash on the floor. Punch a concrete wall and your fist won’t go through it. But in the world of the ultra-small – atoms and electrons – none of the normal rules apply. Instead particles follow quantum rules that are quite baffling.
Like a bit, a qubit can be in one of its two states, labelled 0 or 1, but unlike a bit, a qubit can also be in a superposition of 0 and 1. Superposition is a subtle concept. Measuring a qubit always gives either 0 or 1, but superpositions can be manipulated beforehand so that one of the two outcomes is more likely.
Multiple qubits together can be put into more complicated superpositions. Measuring the qubits always gives a binary string of 0s and 1s, but the likelihood of what string appears can be controlled beforehand, and this is what a quantum computer does.
In fact, quantum computers work by first creating a superposition of lots of different possible solutions to a problem – encoded in qubits – and then manipulating that superposition so that wrong solutions cancel out and right ones are strengthened. This is because the alternatives in a superposition can interfere like waves do. This makes the right answer much more likely to appear when you measure the qubits. For certain types of problems, these two steps can be completed very quickly – outperforming any ordinary computer in solving the original problem.
Building the quantum computer hardware that will work reliably, and is large enough, to process quantum information without errors is a big challenge. Worldwide there is a huge experimental effort to do just that. There are many different designs being explored to build a universal quantum computer – some of these include superconducting circuits, ion traps, optics, and silicon.
In Australia, the Centre for Quantum Computation and Communication Technology (CQC²T) is a world leader in two of the most promising types of hardware for a quantum computer: optical qubits (made of light) and silicon qubits (made of either nuclear or electron spins).
A large-scale universal quantum processor capable of outperforming today’s computers for a wide-range of useful applications needs to have millions of qubits and very few errors.
Small-scale quantum computers called noisy intermediate-scale quantum (NISQ) processors already exist and can be accessed through the internet – i.e., through “cloud quantum computing.” These devices are currently relatively small in qubit number and error prone but are very important in pointing the way forward. To achieve commercial success, we require larger-scale quantum computers with error correction, and that is likely to take at least another 5 years and will continue to improve over the next decade.
Quantum Communication technology has the potential to send messages securely against any sort of hacker, no matter how powerful their computer is – even a quantum computer! The basic idea is simple. Heisenberg’s Uncertainty Principle implies that if you find out one property of a particle you necessarily create uncertainty in other properties. That is, quantum particles are disturbed by measurements. Because of this, an eavesdropper trying to read a secret message encoded in photons will leave unmistakable traces of this transgression on the message itself. These traces clearly reveal the attempt to eavesdrop, ensuring detection before any of your valuable information is compromised.
Quantum communication protocols were first developed in the 1980s. There are short-range systems in commercial operation in many countries, including an Australian one developed by CQC²T researchers. Recently ground-satellite quantum encryption links have also been demonstrated by scientists in USTC, China [Sheng-Kai Liao et al., ‘Satellite-to-ground quantum key distribution’, Nature, 2017, 549:43; Ji-Gang Ren et al., ‘Ground-to-satellite quantum teleportation, Nature, 2017, 549:70.) and MPL, Germany [K Günthner et al., ‘Quantum-limited measurements of optical signals from a geostationary satellite’, Optica, 2017, 4:611].
A grand challenge, which is being tackled worldwide, including at CQC²T, is to extend the range of secure communications into a global network. Because quantum messages cannot be copied, this requires using quantum repeaters to realise a large-scale quantum network. Analogous to the fibre repeater links in global fibre optics networks, quantum repeaters are special-purpose quantum devices that bridge a connection between a distant quantum source and receiver are critical infrastructure for a globally connected quantum network. Designing and making them – and showing their viability – is an active area of research.


Imagine a window with an image etched into its surface, but when you walk around the other side, the image is entirely different.
- Nanoengineering manipulates the path light travels through a material
- This allows two separate images to be seen when viewed from opposite sides
- ‘Nonlinear optics’ could have applications in computing and lead to a faster internet
Although it sounds impossible, that’s essentially what researchers at the Australian National University (ANU) have achieved, with tiny translucent slides that can show two separate images, at the same time, when viewed from opposite sides. .
In one experiment, for example, scientists created a slide showing the Australian continent on one side and the Sydney Opera House on the other.
The advance in the field known as “nonlinear optics” could have applications in photonic computing – using visible light or infrared instead of electric current to perform numerical calculations.
These new light-based devices could eventually lead to a faster and cheaper internet, the researchers said.
Their research was published today in Nature Photonics.
How it works?
As you may have noticed, light generally travels the same path forward and backward through a material like glass or water.
To change that, the researchers created tiny glass slides coated with cylinder-shaped nanoparticles, each particle so small that 12,000 of them could fit in the cross-section of a human hair.
Each cylinder controlled the flow of light like traffic signs directing traffic, said ANU physicist and co-author Sergey Kruk.
“We were able to introduce an asymmetry in the way light travels,” he said.
“So when the light propagates forwards and when it propagates backwards, we get completely different results.”
The technical name for these “traffic signs” is “nonlinear dielectric resonators”.
The cylinders were made of two layers of silicon and silicon nitride. Each layer had a different index of refraction – a measure of how fast light travels through a medium, and therefore the material’s light-bending ability.
The different refractive indices of air and water, for example, explain why a spoon in a glass of water looks bent.
These cylinders could be positioned to be “light” or “dark” only for the rear or front directions, or “light” or “dark” for the front and rear.
By arranging these four types of cylinders into patterns, Dr. Kruk and his colleagues from China, Germany and Singapore were able to form images.
“Basically, slides are made up of individual pixels,” Dr. Kruk said.
“And we can put those pixels together in any pattern you like.”
Benjamin Eggleton, director of the Sydney Nano Institute, called the research “significant” and a “fundamental finding”.
“This is a heroic fundamental breakthrough,” said Professor Eggleton, who was not involved in the research.
The most obvious application, he said, was “nano-photonic components” for computing.
A The key element in electronic computing and the complex architecture of microchips is the diode which allows electrical current to flow in only one direction.
In photonics, or light-based computing, a diode is called an isolator.
The current harvest of Insulators are relatively large and complicated, but ANU’s research could lead to much smaller and simpler designs, Professor Eggleton said.
Photonic circuits, or optical computing, have been dubbed the future of computing because they can be smaller than electronic circuits, operate at higher speeds, use less power, and generate less heat.
“Many leading companies commercializing quantum computing technology rely on photonic circuits,” Professor Eggleton said.
“And on those circuits, you’ll need those insulators.”
Dr. Kruk has also seen applications in photonic circuits.
This could ultimately lead to faster and cheaper internet, he said.
Two years ago, for example, researchers built a clocked photonic circuit 44.2 terabits per second on 76 kilometers of optical fibers installed between two university campuses in Melbourne.
By comparison, that’s around 1 million times faster than the average Australian broadband download speed.
Physicists are just beginning to understand how intense light interacts with the structure of materials at the nanoscale, Dr Kruk said.
“At this point in technological development, we’ve gotten incredibly good at controlling electric currents, and we’re not so good at controlling beams of light.
“This [research] perhaps a first convincing step towards the establishment of a very sophisticated control of the traffic of the light beams.
“[This is] similar to a very sophisticated control of the traffic of electric currents, which we began to establish perhaps in the middle of the 20th century.”
Job , updated


In quantum computing, the quantum Fourier transform (QFT) is a linear transformation on quantum bits, and is the quantum analogue of the discrete Fourier transform. The quantum Fourier transform is a part of many quantum algorithms, notably Shor's algorithm for factoring and computing the discrete logarithm, the quantum phase estimation algorithm for estimating the eigenvalues of a unitary operator, and algorithms for the hidden subgroup problem. The quantum Fourier transform was discovered by Don Coppersmith.
The quantum Fourier transform can be performed efficiently on a quantum computer, with a particular decomposition into a product of simpler unitary matrices. Using a simple decomposition, the discrete Fourier transform on amplitudes can be implemented as a quantum circuit consisting of only Hadamard gates and controlled phase shift gates, where is the number of qubits. This can be compared with the classical discrete Fourier transform, which takes gates (where is the number of bits), which is exponentially more than .
The quantum Fourier transform acts on a quantum state vector (a quantum register), and the classical Fourier transform acts on a vector. Both types of vectors can be written as lists of complex numbers, in the quantum case it is a sequence of probability amplitudes for the different outcomes upon measurement. Because measurement collapses the quantum state to a single value (called basis state, or eigenstate), not every task that uses the classical Fourier transform can take advantage of the quantum Fourier transform's exponential speedup.
The best quantum Fourier transform algorithms known (as of late 2000) require only gates to achieve an efficient approximation.
The quantum Fourier transform is the classical discrete Fourier transform applied to the vector of amplitudes of a quantum state, where we usually consider vectors of length .
The classical Fourier transform acts on a vector and maps it to the vector according to the formula:
where and is an N-th root of unity.
Similarly, the quantum Fourier transform acts on a quantum state and maps it to a quantum state according to the formula:
(Conventions for the sign of the phase factor exponent vary; here we use the convention that the quantum Fourier transform has the same effect as the inverse discrete Fourier transform, and vice versa.)
Since is a rotation, the inverse quantum Fourier transform acts similarly but with:
In case that is a basis state, the quantum Fourier Transform can also be expressed as the map
where . We get, for example, in the case of and phase the transformation matrix
Most of the properties of the quantum Fourier transform follow from the fact that it is a unitary transformation. This can be checked by performing matrix multiplication and ensuring that the relation holds, where is the Hermitian adjoint of . Alternately, one can check that orthogonal vectors of norm 1 get mapped to orthogonal vectors of norm 1.
From the unitary property it follows that the inverse of the quantum Fourier transform is the Hermitian adjoint of the Fourier matrix, thus . Since there is an efficient quantum circuit implementing the quantum Fourier transform, the circuit can be run in reverse to perform the inverse quantum Fourier transform. Thus both transforms can be efficiently performed on a quantum computer.
with the primitive -th root of unity. The circuit is composed of gates and the controlled version of
As already stated, we assume . We have the orthonormal basis consisting of the vectors
The basis states enumerate all the possible states of the qubits:
where, with tensor product (or Kronecker product) notation , indicates that qubit is in state , with either 0 or 1. By convention, the basis state index is the binary number encoded by the , with the most significant bit. With this convention, we may write the quantum Fourier transform as:
It is also useful to borrow fractional binary notation:
With this notation, the action of the quantum Fourier transform can be expressed in a compact manner:
In other words, the discrete Fourier transform, an operation on n qubits, can be factored into the tensor product of n single-qubit operations, suggesting it is easily represented as a quantum circuit (up to an order reversal of the output). In fact, each of those single-qubit operations can be implemented efficiently using a Hadamard gate and controlled phase gates. The first term requires one Hadamard gate and controlled phase gates, the next one requires a Hadamard gate and controlled phase gate, and each following term requires one fewer controlled phase gate. Summing up the number of gates, excluding the ones needed for the output reversal, gives gates, which is quadratic polynomial in the number of qubits.
Consider the quantum Fourier transform on 3 qubits. It is the following transformation:
where is a primitive eighth root of unity satisfying (since ).
For short, setting , the matrix representation of this transformation on 3 qubits is:
The 3-qubit quantum Fourier transform can be rewritten as:
In the following sketch, we have the respective circuit for (with reversed order of output qubits with respect to the proper QFT):
As calculated above, the number of gates used is which is equal to , for .
Relation to quantum Hadamard transformEdit
Using the generalized Fourier transform on finite (abelian) groups, there are actually two natural ways to define a quantum Fourier transform on an n-qubit quantum register. The QFT as defined above is equivalent to the DFT, which considers these n qubits as indexed by the cyclic group . However, it also makes sense to consider the qubits as indexed by the Boolean group , and in this case the Fourier transform is the Hadamard transform. This is achieved by applying a Hadamard gate to each of the n qubits in parallel. Note that Shor's algorithm uses both types of Fourier transforms, both an initial Hadamard transform as well as a QFT.
- Coppersmith, D. (1994). "An approximate Fourier transform useful in quantum factoring". Technical Report RC19642, IBM.
- Michael Nielsen and Isaac Chuang (2000). Quantum Computation and Quantum Information. Cambridge: Cambridge University Press. ISBN 0-521-63503-9. OCLC 174527496.
- Hales, L.; Hallgren, S. (November 12–14, 2000). "An improved quantum Fourier transform algorithm and applications". Proceedings 41st Annual Symposium on Foundations of Computer Science: 515–525. CiteSeerX 10.1.1.29.4161. doi:10.1109/SFCS.2000.892139. ISBN 0-7695-0850-2. S2CID 424297.
- Fourier Analysis of Boolean Maps– A Tutorial –, pp. 12-13
- Lecture 5: Basic quantum algorithms, Rajat Mittal, pp. 4-5


CLASSIC MAGIC TRICK MAY ENABLE QUANTUM COMPUTING
A new project will use the electric field in an accelerator cavity to try to levitate a tiny metallic particle, allowing it to store quantum information
Quantum computing could solve problems that are difficult for traditional computer systems. It may seem like magic. One step toward achieving quantum computing even resembles a magician’s trick: levitation. A new project at the U.S. Department of Energy’s Thomas Jefferson National Accelerator Facility will attempt this trick by levitating a microscopic particle in a superconducting radiofrequency (SRF) cavity to observe quantum phenomena.
This is a line drawing of an accelerator cavity that will be used in a proof of principle project that aims to levitate a tiny metallic particle, allowing it to store quantum information.
Typically at Jefferson Lab and other particle accelerator facilities, SRF cavities enable studies of the atom’s nucleus. They do this by accelerating subatomic particles, such as electrons. This project will use the same type of cavity to instead levitate a microscopic particle of metal, between 1 and 100 micrometers in diameter, with the cavity’s electric field.
“No one has ever intentionally suspended a particle in an electric field in a vacuum using SRF cavities,” said Drew Weisenberger, a principal investigator on this project, as well as Chief Technology Officer and head of the Radiation Detector and Imaging Group in the Experimental Nuclear Physics Division at Jefferson Lab.
If the project team is able to levitate a particle, they might be able to then impart a quantum state on it by cooling the trapped particle to its lowest possible energy level (because that’s when quantum properties occur).
“Storing quantum information on a levitated nanoparticle is our ultimate goal, but for now, it is a proof of principle experiment,” said Pashupati Dhakal, another principal investigator on the project and a staff scientist at Jefferson Lab in the Accelerator Operations, Research and Development Division. “We want to know if we can trap and levitate particles inside the cavity using the electric field.”
Exploring the Quantum with Accelerator Cavities
The idea for this project came from observations of accelerator experts. They think they have already unintentionally levitated unwanted and rare nanoparticles of metal, such as niobium and iron, inside SRF cavities during particle accelerator operations. They suspect that this unintentional levitation has impacted the performance of SRF cavity components.
Researchers are attempting to use a several-decades-old technique called “laser trapping”, as a step toward reliably imparting a quantum state on a particle suspended in a laser beam. But, the Jefferson Lab project team thinks that SRF cavities may provide a better tool for those researchers.
“An electric field could go potentially beyond the capabilities of laser trapping,” Weisenberger said.
Intrinsic characteristics of SRF cavities will overcome some limits of laser trapping. A levitated particle in an SRF cavity that is under vacuum and chilled to super cold temperatures will only interact with the cavity’s electric field and not lose information to the outside, which is important for maintaining a quantum state.
“Like storing information on a computer chip, the quantum state will stay and not dissipate,” Weisenberger said. “And that could eventually lead to applications in quantum computing and quantum communications.”
This project, titled “SRF Levitation and Trapping of Nanoparticles Experiment,” is funded by the Laboratory Directed Research & Development program, which provides resources for Jefferson Lab personnel to make rapid and significant contributions to critical science and technology problems relevant to the mission of Jefferson Lab and the DOE.
A Multidisciplinary Approach
The project was conceived and launched by Rongli Geng in October 2021 before he transitioned to Oak Ridge National Laboratory. It has now shifted to a larger and more multi-disciplinary team led by Weisenberger and Dhakal, the current co-principal investigators.
Weisenberger’s team researches detector technology for nuclear physics research, whereas Dhakal’s work focuses on developing SRF cavities to accelerate electrons at high speeds. Weisenberger says that the multidisciplinary approach will bring together their expertise as they branch out together into the less familiar territory of this LDRD project.
Both principal investigators remark that the project is moving forward well, thanks to the diligence and expertise supplied by every member of the team. Team members include John Musson, Frank Marhauser, Haipeng Wang, Wenze Xi, Brian Kross and Jack McKisson.
“It’s an interesting step outside of the usual things that we do,” Weisenberger said. “The LDRD program lets loose Jefferson Lab scientists and engineers on a research question that isn’t directly related to what we’re actually hired to do, but is making use of all the expertise that we bring and it’s a great resource to tap to try to stretch. That’s what we’re doing with this project, stretching.”
Building and Testing
Before turning the project over the Weisenberger and Dhakal, Geng and his colleagues had determined the required parameters of the cavity and electric field with simulations and calculations.
“We have everything on paper but we have to make it into a reality,” Dhakal said.
The team is currently setting up the experiment in real life.
“We have to see if what was simulated can actually happen,” Weisenberger said.
First, they’ll assemble a mock-up of the experiment at room temperature. Then, they’ll circulate liquid helium around the outer surfaces of the cavity to cool it to superconducting temperatures approaching absolute zero.
Next comes the most difficult part. They must get a single microscopic particle in the correct region of the cavity while the cavity is locked up inside a containment vessel at superconducting temperatures, under vacuum, and with the electric field on.
“We’ve come up with a way to remotely launch a particle in the cavity under experimental conditions, we just have to test it now,” Weisenberger said. “In the research and development world, you often can’t do what you thought you could do. We try and test and run into problems, try to solve the problems, and keep going.”
This is a year-long project with the possibility of another year of funding, depending on how things go. It is also an early stage, proof of principle project. If it is ultimately successful, there would still be a long road of R&D before the concepts could be applied toward building quantum computers. Such computers would require levitating and imparting quantum states on tens to hundreds to thousands of much smaller particles predictably and reliably.
Still, the researchers are looking forward to the discoveries they hope this study will enable regarding microscopic particle levitation and potential observation of a quantum state.
“I’m optimistic,” Dhakal said. “Either way, we’ll discover something. Failure is just as much a part of R&D as success. You learn from both. Basically, whether the particle levitates or not, or whether we can impart the quantum state to it or not, it’s something that’s never been done before. It’s very challenging and exciting.”
The team already has a research paper in the works for this project, but only time will tell whether they can realize this bit of magic in the laboratory.
Content may have been edited for style and clarity.


Quantum computing is here to shake the existing mechanical, electrical and electronic systems. Modern electronics in particular will not be the same if quantum computing gains acceptance. Therere voices of support as well as dissent. In this post, well analyze future trends in quantum computing. Keep reading!
Quantum computers use atoms to perform calculation. The computation speed depends principally on Qubits (quantum bits). These quantum bits are the fundamental building blocks of a quantum computer. Recent developments in the field of quantum research expect to eliminate/drop the Moore’s law by 2020.
The future of Quantum computers as of now is not very certain particularly due to already known problems in areas such as de-coherence, error correction, output observance and cost related issues. But, if scientists succeed in developing a practically useful quantum computer, it may replace traditional computers in sectors such as robotics (Industrial Automation), cyber security, alternative energy etc. Such computers may also be deployed for solving emerging tactical problems like Tsunami alerts.
Quantum computers can scale up the possibility of enhancing computation power to a new and unanticipated peak point by providing a fast and efficient platform for high performance computing.
At present, we don’t have very efficient systems capable of solving tactical problems such as
Correct weather forecasting
Predicting right patterns in stock markets
Analyzing the molecular/ DNA part of human body in medical research.
Today, processor die size is drastically shrinking, but there is not enough software solutions developed for harnessing the full processor potential. Computing power over the next few years will perhaps get skyrocketed with the advent of quantum computers.
Many experts argue that the computing world today doesn’t even have the right programs to actually utilize a 1 GHz mobile processor in the best possible way. It’s not more processor speed but better programs we need urgently right now, or is it?
Have a look at some areas where quantum computers can play a vital role in near future:
Artificial intelligence (AI) was primarily meant to assist humans in executing complex jobs such as handling operations in the middle of a furnace blast or during space and military missions.
Today, robotic systems are heavily used in the industrial automotive world for boosting production. Introduction of quantum computing can give a major boost to AI by ensuring creation of even more powerful & intelligent robots. The capability of encoding information in fuzzy quantum states will multiply the power of these artificial creatures.
It would be possible to scan through large databases in few seconds with qubits.
Quantum AI techniques can dramatically speed up image acquisition and processing techniques.
Algorithms have already been developed and ready for implementation in quantum computers now. But recent failures in controlling Qubits inside laboratories, pose serious questions regarding the viability of quantum computing.
Developed robots featuring powerful qubit will be able to break maximum encryption code within near zero time. A quantum computer will possibly crack any possible password in an instant. No security algorithm will then be able to provide 100% security to content placed on web servers. As far as the Internet is concerned, everything (yes, everything) will have to be redefined using quantum computers.
Qubits (known as Quantum dots in solar terminology) can be largely deployed in solar panels to replace the current photovoltaic cells technology. Quantum dot is a nanoscale particle of semiconducting material that can be embedded. It can therefore revolutionize the renewable energy sector.
Qubits can also be used to make quantum batteries in order to store energy generated by powerful windmills.
Teleportation (if it ever becomes a reality) will allow transfer of matter from one place to another without traversing through physical medium. With this technology, (some say) time travelling can become possible which still is considered a myth. Quantum teleportation technology will enable humans to travel far distances without losing a moment as seen in fictional/sci-fi movies.
Right now, it’s all speculation.
Quantum computers can be connected in series to form a quantum network, thus building a smart grid. They will offer high encoding and decoding speeds with fast transfer of information (qubits).
Smart energy grids will offer high efficiency in energy delivery system. Additionally, quantum computers can also be used to process large amount of data coming from geothermal activities.
The already developed and much touted quantum computer from ‘D-Wave’ systems is 3600 times powerful than a conventional PC. But the project was declared a failure on application front by Google.
Questions about the real-world feasibility of such expensive projects remain unanswered.
But, given the fact that everything from cellphones, wireless networks and electricity was no less than a miracle few dozen years ago, quantum computing too may appear as a miracle at first and slowly become an integral part of our lives.
About Amy Baker
A computer science engineer, Amy holds a Masters Degree in Quantum Computing. She is based in Texas.
The content & opinions in this article are the author’s and do not necessarily represent the views of RoboticsTomorrow
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One day in 1900, German physicist Max Planck told his son that he had made a breakthrough as important as Isaac Newton’s discovery of the workings of the universe. Planck had reached the surprising conclusion that light behaves as if it is packaged in discrete amounts, or quanta, a seemingly simple observation that would lead to a powerful new field of physics called quantum mechanics.
Quantum mechanics is the most successful physical theory ever devised, and you learn what distinguishes it from its predecessor, classical mechanics. Professor Schumacher explains his ground rules for the course, which is designed to teach you some of the deep ideas and methods of quantum mechanics.
You investigate the age-old debate over whether the physical world is discrete or continuous. By the 19th century, physicists saw a clear demarcation: Matter is made of discrete atoms, while light is a continuous wave of electromagnetic energy. However, a few odd phenomena remained difficult to explain.
At the beginning of the 20th century, Max Planck and Albert Einstein proposed revolutionary ideas to resolve puzzles about light and matter. You explore Planck's discovery that light energy can only be emitted or absorbed in discrete amounts called quanta, and Einstein's application of this concept to matter.
Light propagates through space as a wave, but it exchanges its energy in the form of particles. You learn how Louis de Broglie showed that this weird wave-particle duality also applies to matter, and how Max Born inferred that this relationship makes quantum mechanics inherently probabilistic.
You explore the mystery of why atoms are stable. Niels Bohr suggested that quantum theory explains atomic stability by allowing only certain distinct orbits for electrons. Erwin Schrödinger discovered a powerful equation that reproduces the energy levels of Bohr's model.
One of the most famous and misunderstood concepts in quantum mechanics is the Heisenberg uncertainty principle. You trace Werner Heisenberg's route to this revolutionary view of subatomic particle interactions, which establishes a trade-off between how precisely a particle's position and momentum can be defined.
You focus on the Einstein-Bohr debate, which pitted Einstein's belief that quantum events can, in principle, be known in every detail, against Bohr's philosophy of complementarity—the view that a measurement of one quantum variable precludes a different variable from ever being known.
Beginning his presentation of quantum mechanics in simplified form, Professor Schumacher discusses the mysteries and paradoxes of the Mach-Zehnder interferometer. He concludes with a thought experiment showing that an interferometer can determine whether a bomb will blow up without necessarily setting it off.
The interferometer from the previous lecture serves as a test case for introducing the formal math of quantum theory. By learning a few symbols and rules, you can describe the states of quantum particles, show how these states change over time, and predict the results of measurements.
Many quantum particles move through space and also have an intrinsic spin. Analyzing spin gives you a simple laboratory for exploring the basic ideas of quantum mechanics, and it is one of your key tools for understanding the quantum world.
Macroscopic objects obey the snowflake principle. No two are exactly alike. Quantum particles do not obey this principle. For instance, every electron is perfectly identical to every other. You learn that quantum particles come in two basic types: bosons, which can occupy the same quantum state; and fermions, which cannot.
You discover that the tendency of bosons to congregate in the same quantum state can lead to amazing applications. In a laser, huge numbers of photons are created, moving in exactly the same direction with the same energy. In superconductivity, quantum effects allow electrons to flow forever without resistance.
Why is matter solid, even though atoms are mostly empty space? The answer is the Pauli exclusion principle, which states that no two identical fermions can ever be in the same quantum state.
At the fundamental level, bosons and fermions differ in a single minus sign. One way of understanding the origin of this difference is with the Feynman ribbon trick, which Dr. Schumacher demonstrates.
When two particles are part of the same quantum system, they may be entangled with each other. In their famous "EPR" paper, Einstein and his collaborators Boris Podolsky and Nathan Rosen used entanglement to argue that quantum mechanics is incomplete. You chart their reasoning and Bohr's response.
Thirty years after EPR, physicist John Bell dropped an even bigger bombshell, showing that a deterministic theory of quantum mechanics such as EPR violates the principle of locality—that particles in close interaction can't be instantaneously affected by events happening in another part of the universe.
Feynman diagrams are a powerful tool for analyzing events in the quantum world. Some diagrams show particles moving forward and backward in time, while other particles appear from nowhere and disappear again. All are possible quantum scenarios, which you learn how to plot.
The quantum vacuum is a complex, rapidly fluctuating medium, which can actually be observed as a tiny attraction between two metal plates. You also discover that vacuum energy may be the source of the dark energy that causes the universe to expand at an ever-accelerating rate.
You explore quantum information and quantum computing—Dr. Schumacher's specialty, for which he pioneered the concept "qubit," the unit of quantum information. You learn that unlike classical information, such as a book or musical recording, quantum information can't be perfectly copied.
The uncopyability of quantum information raises the possibility of quantum cryptography—an absolutely secure method for transmitting a coded message. This lecture tells how to do it, noting that a handful of banks and government agencies already use quantum cryptography to ensure the security of their most secret data.
What are the laws governing quantum information? Charles Bennett has proposed basic rules governing the relationships between different sorts of information. You investigate his four laws, including quantum teleportation, in which entanglement can be used to send quantum information instantaneously.
You explore the intriguing capabilities of quantum computers, which don't yet exist but are theoretically possible. Using the laws of quantum mechanics, such devices could factor huge numbers, allowing them to easily decipher unbreakable conventional codes.
What is the fundamental nature of the quantum world? This lecture looks at three possibilities: the Copenhagen, hidden-variable, and many-worlds interpretations. The first two reflect Bohr's and Einstein's views, respectively. The last posits a vast, multivalued universe encompassing every possibility in the quantum realm.
In this final lecture, you ponder John A. Wheeler's metaphor of the Great Smoky Dragon, a creature whose tail appears at the start of an experiment and whose head appears at the end. But what lies between is as uncertain as the mysterious and unknowable path of a quantum particle.
Kuratiert wunderbare wissenschaftliche Materialien für Menschen. Dokumentationen, Vorträge und Filme. Alles handelsfrei.


Researchers from MIT, Google, and elsewhere have designed a novel method for verifying when quantum processors have accurately performed complex computations that classical computers can’t. They validate their method on a custom system (pictured) that’s able to capture how accurately a photonic chip (PNP) computed a notoriously difficult quantum problem. Image © Mihika Prabhu.
Last year on October 23, 2019, a research paper was published in the journal Nature stating that the quantum speedup is achievable in a real-world system. Quantum computer is superior to classical computers that work on the principle of binary code; 0 and 1. Quantum computers use quantum bits (qubits) and qubits can represent both a 0 and 1 at the same time. Though a quantum computer may work on the state between 1 and 0, when qubits are measured the result is always either a 0 or a 1.
But how do we verify that the quantum chip- a crucial technical part of a quantum computer, is working correctly?
But not to worry, a team of researchers from MIT has described a novel protocol to efficiently verify that a Noisy Intermediate Scale Quantum (NISQ) chip has performed all the right quantum operations. The research work was published in the journal Nature Physics.
Quantum chips perform computations using quantum bits, called qubits. Qubits can represent the two states corresponding to classic binary bits - a 0 or 1 - or both states simultaneously called the quantum superposition of both states. The unique superposition state is where the quantum computer shows its prowess: superposition states enable quantum computers to solve complex problems that are practically impossible for classical computers and potentially serve as a breakthrough in material design, drug discovery, and machine learning, among other applications.
But achieving a fully workable quantum computer is not an easy task; full-scale quantum computers will require millions of qubits, which isn’t yet feasible. Scientists have been working on designing feasible quantum chips for the past years and recently they are busy developing Noisy Intermediate Scale Quantum (NISQ) chips that can contain around 50 to 100 qubits. The chip’s outputs can look entirely random, so it takes a long time to simulate steps to determine if everything went according to plan. And here the team validates their protocol on a notoriously difficult quantum problem running on a custom quantum photonic chip and successfully described a novel protocol to efficiently verify that a NISQ chip has performed all the right quantum operations indicating that they just do not operate randomly.
Jacques Carolan, first author and a postdoc in the Department of Electrical Engineering and Computer Science (EECS) and the Research Laboratory of Electronics (RLE) said, “As rapid advances in industry and academia bring us to the cusp of quantum machines that can outperform classical machines, the task of quantum verification becomes time-critical. Our technique provides an important tool for verifying a broad class of quantum systems. Because if I invest billions of dollars to build a quantum chip, it sure better do something interesting.”
The basic idea of testing the quantum chip was easy; they just fed an output quantum state generated by the quantum circuit back to a known input state.
By this, they were able to diagnose which circuit operations were performed on the input to produce the output since those operations should always match what researchers programmed and if not, the researchers can use the information to pinpoint where things went wrong on the chip.
The main idea of the new protocol was to divide and conquer where instead of doing whole thing in one shot, which takes a very long time, they do this unscrambling layer by layer. This divide and conquer rule allowed researchers to break problems and tackle them in a more efficient way.
The idea of divide and conquer was inspired by the working of neural networks - which solve problems through many layers of computation - and successfully build a novel quantum neural network (QNN) where each layer represents a set of quantum operations.
To run the QNN, they used traditional silicon fabrication techniques to build a 2-by-5-millimetre NISQ chip with more than 170 control parameters. The control parameters were tunable circuit components and can manipulate the photon path easier. Then a pair of photons having specific wavelengths were generated from an external component and injected into the chip and those photons travel through the chip’s phase shifters. The phase shifter was used to change the path of photons. This phenomenon eventually produces a random quantum output state which represents what would happen during computation. Now the output was measured by an array of external photodetector sensors.
Then the output was sent to QNN where the first layer uses complex optimization techniques to dig through the noisy output and pinpoint the signature of a single photon among all those scrambled together. Then as required it unscrambles that single photon from the group to identify what circuit operations return it to its known input state. Those operations should match exactly the circuit’s specific design for the task. Here all the subsequent layers do the same computation - removing from the equation any previously unscrambled photons - until all photons are unscrambled.
For instance, let’s say the input state of qubits fed into the processor was all zeroes and the NISQ chip executes a bunch of operations on the qubits to generate a massive, seemingly randomly changing number as output. An output number will constantly be changing as it’s in a quantum superposition. Layer by layer now the QNN selects chunks of that massive number and then and determines which operations revert each qubit back down to its input state of zero. If any operations are different from the originally planned operations, then researchers will know that something has gone awry. Researchers can inspect any mismatches between the expected output to input states, and use that information to tweak the circuit design.
Researchers were able to unsample two photons that had run through the boson sampling problem on their custom NISQ chip - and in a fraction of time it would take traditional verification approaches and also claimed that in addition for quantum verification purposes, this process helps to capture useful physical properties.


With the advent of quantum computing, the need for peripheral fault-tolerant logic control circuitry has reached new heights. In classical computation, the unit of information is a “1” or “0”. In quantum computers, the unit of information is a qubit which can be characterized as a “0”, “1”, or a superposition of both values (known as a “superimposed state”).
The control circuitry in classical computers is CMOS (semiconductor) based, due to its high-performance and low power dissipation. The “1’s” and “0’s” of a classical computer can be manipulated, stored, and easily read using CMOS chips that operate at room temperature. Most quantum computers today operate at cryogenic temperatures, to ensure that the qubit remains coherent (in a superimposed state) for as long as possible. The coherence times are typically very short (nanoseconds to milliseconds) in a quantum computer, prompting the need for control circuitry that can perform high-speed, fault-tolerant operations. This requirement could be met by conventional CMOS control circuitry if it could be operated at cryogenic temperatures.
The first attempt to characterize semiconductor materials at cryogenic temperatures was made by A.K. Jonscher in his 1964 Proceedings of the IEEE publication, entitled “Semiconductors at Cryogenic Temperatures” . His two basic conclusions were: 1) semiconductor devices have no major cryogenic application at that point in time due to “no real technological justification for going on a large scale to these extreme temperatures”, and 2) “the properties of semiconductor materials at cryogenic temperatures are so strikingly different from the familiar properties at higher temperatures, that it is reasonable to expect many more device applications to emerge as a result of continued research and development effort in this direction”. A few years later, IBM became interested in low-temperature semiconductor device operation [2-3] and concluded that MOSFET semiconductor devices show improved performance at cryogenic temperatures. With the advantages of low-temperature operation, scaling-down the cooling apparatus is still an obstacle in using semiconductor-based control circuitry.
Enter quantum mechanics. In 1959, Richard Feynman challenged the scientific community to employ quantum mechanics in the design of information processing systems. He envisioned new information systems and functions that involved quantized energy levels, and/or the interactions of quantized “spins” (angular momentum of quantum particles). His vision was realized in the 1980s, when it was demonstrated that quantum mechanical, energy-based equations could represent a universal Turing (computational) machine . In 1994, it was shown that a quantum computer could factor integer numbers much more quickly than a classical computer (“in polynomial time”) . This discovery was the catalyst that fostered continued interest in building quantum computing systems. That interest continues today at numerous commercial, research and academic organizations.
Even with the strong interest in building quantum computers, the fact remains that successful operation of this type of computer currently requires a cryogenic temperature environment. Quantum logic control circuitry will also need to operate at these cryogenic temperatures to function effectively in this environment. Thus, we have seen a resurgence of interest in the cryogenic temperature performance of CMOS-based circuitry.
Quantum computers do not require state-of-the-art CMOS circuitry, but CMOS devices operate differently at cryogenic and room temperatures. CMOS transistor performance (and the associated I-V performance) has recently been measured on 40 nm and 160 nm bulk CMOS devices, at both room temperature and at 4.2 degrees Kelvin (see Figure 1). Drive current increases at cryogenic temperatures due to an increase of the mobility in silicon at these temperatures. Unfortunately, other effects such as substrate freeze-out can limit the increase in drive current at these low temperatures.Control circuitry for quantum computers is currently being operated at room temperature. As mentioned earlier, this can be a problem due to the sensitivity of reading the “state” of qubits at higher temperatures. This challenge can be partially alleviated by operating the CMOS circuitry at or near cryogenic temperatures, in the same cryogenic freezers as the quantum computer. This integration can serve to reduce latency and increase overall system scalability. Despite some second order issues, CMOS transistors at low temperatures can perform various functions needed to work with a quantum computer. These functions include the ability to perform as I/V converters, low-pass filters, and A/D and D/A converters (see Figure 2). To achieve the desired performance of a fault-tolerant quantum computer system, a new generation of deep-submicron CMOS circuits will be required that operate at deep-cryogenic temperatures . Extrapolating this idea to its logical conclusion, one ends up with a quantum integrated circuit (QIC) where the array of qubits is integrated on the same chip as the CMOS electronics required to read the state of the qubits. This integration would clearly be the ultimate goal in achieving scalable, reliable, and high performing quantum computing.
In more futuristic applications, optical communications to and from the qubit may also be necessary. In this case, integrated CMOS circuits will also need to include micro- and nano-optical structures, such as light-guides and interferometers. These types of optical functions have been successfully demonstrated on room-temperature CMOS devices. Demonstrating this level of optical communications functionality at cryogenic temperatures may also be desirable in future quantum computing applications.


The quantum internet will change the world -- it will unlock applications ranging from ultra-secure communication to high-performance AI systems to unprecedented medical images. So what’s stopping us? Building a global quantum internet from today’s laboratory experiments requires the ability to transmit qubits over long distances. Quantum repeaters are the key to unlocking this.
Quantum repeaters vs. Classical repeaters
Before we look at the role of quantum repeaters in the quantum internet, let’s consider an analogous device -- a non-quantum, or “classical,” repeater.
The Internet transfers information in the form of bits along fiber optic cables. Some of these cables travel long distances, such as the SEA-ME-WE 3 undersea cable that reaches from Germany to Japan. However, as light passes through these fibers, it suffers from loss, or “attenuation,” as photons are absorbed by the fiber. To account for this, a “repeater” is inserted between nodes. Repeaters simply measure the signal coming in from one side, copy it, and retransmit it at higher power to the other side. As a result, the quantum internet is able to transmit information reliably over very long distances.
Loss is a problem in quantum networks as well, but unfortunately the same technique of measuring, copying, and retransmitting doesn’t translate to the quantum communications realm. This is due to a fundamental aspect of quantum information -- it cannot be copied. This fact is known as the no-cloning theorem.
It turns out that we can’t measure quantum states on their way from point A to point B without destroying them. This actually provides some of the amazing benefits of quantum communications, like ultra-secure communication, but also means that we can’t use the same idea from classical repeaters to avoid loss in quantum channels.
So, how can we avoid the problem of loss in a quantum network?
How quantum repeaters work
Despite their name, quantum repeaters actually use a very different strategy than classical repeaters to handle the problem of loss. The core idea is based on the technique of entanglement swapping.
The primary goal of quantum networks is to distribute entanglement between members of the network. Entanglement distribution unlocks all kinds of applications, including even transmitting qubits. Entanglement swapping is a clever idea that gets around the problem of loss without violating the no-cloning theorem.
Entanglement swapping uses teleportation to create long-distance entanglement from a chain of locally connected repeaters
Entanglement swapping works by generating a single long-distance entanglement from many short-distance entanglements. One of the biggest obstacles to distributing long-distance entanglement is the exponential loss incurred due to fiber attenuation. Say Alice and Bob are connected by a fiber that is too long to transmit photons at a reasonable rate. They can add a repeater in the middle that instead accepts entangled photons from both Alice and Bob and then converts those into entanglement between Alice and Bob. In this way, the photons only need to travel half the distance and have a higher chance of making it all the way to their destination.
While the act of “gluing” together two separate entanglement links may sound magical, the repeater can do this using a simple operation called teleportation. As long as the repeater has qubits that are entangled with pairs at each of Alice and Bob, it can perform a measurement and report to Alice and Bob the information they need to use their newly entangled connection. By building up a chain of repeaters, we can break down long distances into more manageable segments over which to send our photons.
Teleportation between two nodes has been experimentally demonstrated by many different research groups, in many different scenarios (through a free-space link over 143 kilometers, across the Danube, and over a ground-to-satellite uplink). Most recently, Caltech demonstrated teleportation using telecom wavelengths, the wavelength of choice for building a quantum internet on top of existing classical infrastructure. So if we already have such a plethora of successful quantum teleportation experiments, why can’t we build real quantum repeaters? Well, efforts are already underway for some early demonstrations. However, the first repeaters need to be designed to handle the limitations of current devices. In fact, a timeline of repeater technology has emerged, separating repeaters into three categories: 1st generation, 2nd generation, and 3rd generation. These generations do not necessarily make each other obsolete, but they show how networks can expand to support increasingly powerful applications as technology improves.
Three generations of quantum repeaters
Image adapted from: Muralidharan, S., Li, L., Kim, J. et al. Optimal architectures for long distance quantum communication. Sci Rep 6, 20463 (2016). https://doi.org/10.1038/srep20463
1st Generation Repeaters
Repeaters need to rely on quantum processors to accomplish their jobs. However, today’s quantum processors are very error prone. To make up for this, 1st generation repeaters will use a process called entanglement distillation. The idea behind entanglement distillation is that you can “distill” a high quality entanglement from many copies of low quality entanglement. While a network with 1st generation repeaters will enable some groundbreaking applications, it’s communication rate is highly limited by the process of distillation.
2nd Generation Repeaters
As error rates improve, quantum repeaters can transition from relying on entanglement distillation to quantum error correction to handle operation errors. Quantum error correction handles errors by encoding information into blocks of qubits, where errors can more easily be handled. This will allow networks to transfer information at much higher speeds and enable further applications.
3rd Generation Repeaters
Finally, once quantum devices have improved enough, quantum error correction will be able to be used to handle both loss and operation errors. Essentially, this allows nodes to trust that their information will travel safely to other nodes, without having to listen to hear from each repeater that entanglement was established. This will have a huge improvement on the rate of communication and unlock even more applications.
A global quantum internet using repeaters will enable game-changing applications
Quantum networks are already under development! For example, the Center for Quantum Networks, hosted at the University of Arizona, plans to develop the first quantum network enabling fully error-corrected quantum connectivity, enabled by quantum repeaters. Similar efforts are underway in National Labs and at Universities across the United States and the globe. Developing working quantum repeaters will be a key to the success of these efforts.