Encryption technologies are used to secure many applications and websites that you use daily. For example, online banking or shopping, email applications, and secure instant messaging use encryption. Encryption technologies secure information while it is in transit (e.g. connecting to a website) and while it is at rest (e.g. stored in encrypted databases). Many up-to-date operating systems, mobile devices, and cloud services offer built-in encryption, but what is encryption? How is it used? And what should you and your organization consider when using it?
What is encryption?
Figure 1 - Encryption encodes (or scrambles) information
Long description - Figure 1
Image shows how encryption encodes and protects the confidentiality of the information by stopping unauthorized individuals from accessing it, as they don't have the key to decrypt the message.
Encryption encodes (or scrambles) information. Encryption protects the confidentiality of information by preventing unauthorized individuals from accessing it.
For example, Alice wants to send Bob a message, and she wants to ensure only he can read it. To keep the information confidential and private, she encrypts the message using a secret key. Once encrypted, this message can only be read by someone who has the secret key to decode it. In this case, Bob has the secret key.
Eve is intentionally trying to intercept the message and read it. However, the message is encrypted, and even if Eve gets a copy of it, she can’t read it without acquiring the secret key.
If an individual accidentally receives a message that includes encrypted information, they will be unable to read the encrypted contents without the key to decrypt the message.
How is encryption used?
Encryption is an important part of cyber security. It is used in a variety of ways to keep data confidential and private, such as in HTTPS websites, secure messaging applications, email services, and virtual private networks. Encryption is used to protect information while it is actively moving from one location to another (i.e. in transit) from sender to receiver. For example, when you connect to your bank’s website using a laptop or a smartphone, the data that is transmitted between your device and the bank’s website is encrypted. Encryption is also used to protect information while it is at rest. For example, when information is stored in an encrypted database, it is stored in an unreadable format. Even if someone gains access to that database, there’s an additional layer of security for the stored information. Encryption is also used to protect personal information that you share with organizations. For example, when you share your personal information (e.g. birthdate, banking or credit card information) with an online retailer, you should make sure they are protecting your information with encryption by using secure browsing.
Many cloud service providers offer encryption to protect your data while you are using cloud based services. These services offer the ability to keep data encrypted when uploading or downloading files, as well as storing the encrypted data to keep it protected while at rest.
When properly implemented, encryption is a mechanism that you and your organization can use to keep data private. Encryption is seamlessly integrated into many applications to provide a secure user experience.
How can I use encryption?
Your organization likely already uses encryption for many applications, such as secure browsing and encrypted messaging applications.
If you access a website with padlock icon and HTTPS in front of the web address, the communication (i.e. the data exchanged between your device and the website’s servers) with the website is encrypted.
To protect your organization’s information and systems, we recommend that you use HTTPS wherever possible. To ensure that users are accessing only HTTPS-supported websites, your organization should implement the web security policy tool HTTP Strict Transport Security (HSTS). HSTS offers additional security by forcing users’ browsers to load HTTPS supported websites and ignore unsecured websites (e.g. HTTP).
Encrypted messaging applications
Most instant messaging applications offer a level of encryption to protect the confidentiality of your information. In some cases, messages are encrypted between your device and the cloud storage used by the messaging service provider. In other cases, the messages are encrypted from your device to the recipient’s device (i.e. end-to-end encryption). When using end-to-end encryption services, not even the messaging service provider can read your encrypted messages.
In deciding which tools to use, you need to consider both the functionality of the service and the security and privacy requirements of your information and activities. For further information, refer to protect how you connect.
Encryption is just one of many security controls necessary to protect the confidentiality of data.
What else should I consider?
Encryption is integrated into many products that are commonly used by individuals and organizations to run daily operations. When choosing a product that uses encryption, we recommend that you choose a product that is certified through the Common Criteria (CC) and the Cryptographic Module Validation Program (CMVP). The CC and the CMVP list cryptographic modules that conform to Federal Information Processing Standards. Although the CC and the CMVP are used to vet products for federal government use, we recommend that everyone uses these certified products.
The CCCS recommends
When choosing a suitable encryption product for your organization, consider the following:
- Evaluate the sensitivity of your information (e.g. personal and proprietary data) to determine where it may be at risk and implement encryption accordingly.
- Choose a vendor that uses standardized encryption algorithms (e.g. CC and CMVP supported modules).
- Review your IT lifecycle management plan and budget to include software and hardware updates for your encryption products.
- Update and patch your systems frequently.
Prepare and plan for the quantum threat to cyber security. For more information, please see Addressing the quantum computing threat to cryptography ITSE.00.017.
Encryption for highly sensitive data
Systems that contain highly sensitive information (e.g. financial, medical, and government institutions) require additional security considerations. Contact us for further guidance on cryptographic solutions for high-sensitivity systems and information: firstname.lastname@example.org.


Semiconductors are drivers of modern electronics, and they are the main enablers of our communications, computing, energy, transport, IoT systems and many more. Almost each and every device we have around us has a semiconductor in it, so no one can overestimate their importance in the world of technology. Today we’re trying to break down the notion of semiconductors, discover what’s inside this vital element and what trends are driving its development today.
A semiconductor as the name implies is a material that has electrical behavior between conductors and insulation. Conductors are substances that easily transmit electricity, while insulators poorly transmit electricity.
The semiconductor industry uses silicon as its primary material. Silicon is a good conductor, but it does not have the necessary characteristics to make a useful transistor. To change this, manufacturers add impurities to the silicon crystal structure. Impurities are atoms that do not belong to the regular arrangement of the crystal lattice. By adding these impurities, manufacturers can control how easily the electrons and holes move through the silicon.
Silicon is the basis for all modern electronic devices. Transistor technology was first developed using germanium, a semiconductor with similar properties to silicon. Germanium is still used today, but silicon is much easier to work with. Because of this, silicon is still the dominant semiconductor material.
Semiconductors are classified based on whether they are intrinsic or extrinsic. Intrinsic means that there are no impurities present in the material. Extrinsic means that the material requires doping to become conductive and therefore is considered a semiconductor.
Intrinsic semiconductors have no additional doping elements added to them. These materials do not need to be externally charged before they conduct electricity. Intrinsic semiconducting materials are often referred to as bulk materials. Examples of intrinsic semiconductors are silicon (Si) and germanium (Ge).
Extrinsic semiconductors are those that require doping to make them conductive. An example of an extrinsic semiconductor would be gallium arsenide, which is commonly used in transistors. Here, arsenic atoms have been added to the crystal structure of gallium to create positive charges called acceptor states. These states act as electron traps, causing the semiconductor to become electrically conductive.
The IT industry cannot be separated from the development of the semiconductor industry. Semiconductors examples are transistors, MOSFETs, ICs, and diodes. One of the semiconductor materials commonly used in a digital device (logic-based circuit) technology development is a transistor.
The invention of the transistor in 1947 helped in the development of second-generation computers into smaller, faster, more reliable, and more energy efficient than their predecessors. It was the era that transistors began their massive deployment which was started by Shockley until the birth of Fairchild Semiconductor which is considered as a pioneer in IC and transistor manufacturers.
In the early 1960s, successful second-generation computers began to emerge in business, universities, and in government. These second-generation computers are computers that use full transistors. From here was born the next generation of computers that use hardware-based LSI, VLSI, ULSI to supercomputers. The birth of computer networking technology as well as the Internet, which is also supported by semiconductor-based devices, brought IT technology into the modern state as we know it today.
Semiconductor has revolutionized electronic hardware, especially since the invention of the transistor. Semiconductors make hardware more compact and have better computing-related capabilities. The effect is that electronic components are now easier to obtain at affordable prices in the marketplace. This makes it easy for new developers to conduct research and innovation.
LANARS provides hardware development services for creating new products and businesses, as well as for improving existing ones.
The semiconductor, commonly known as the chipset, is the most important component. Despite their small size, semiconductor chips are the brains of an electronic system. In digital devices, the presence of semiconductors is needed to increase the speed of digital signal processing, including memory for data storage.
As we are now in the industrial era 4.0, the need for semiconductor chips continues to grow. The semiconductor industry is also considered the lifeblood that is essential in accelerating digital transformation. The development of computers, the telecommunication industry, automotive equipment, especially electric vehicles (EVs), as well as digitalization in many sectors require the readiness of the semiconductor industry to prepare the required resources.
In the midst of increasing demand for semiconductors, the global COVID-19 pandemic in 2020 hit almost the entire industry with a lockdown policy. This also has an impact on the supply of semiconductors, resulting in reduced supply, which has an impact on other industries. The affected industries include computers, Smart-TVs, smartphones, tablets, game consoles, and various electronic gadgets to the automotive industry.
On the other hand, the COVID-19 pandemic has also increased the need for computers and gadgets in line with the school-from-home or work-from-home policies. This condition causes the semiconductor price trend to rise from the 2020 period to the present time. The implication results in 2021 the major players of semiconductor chipsets such as TSMC actually reap profits caused by the shortage of global chipset supply.
According to a report from research firm TrendForce, if the top 10 chipset manufacturers combined, they will get a total revenue of US$127.4 billion in 2021. This figure is an increase of 48% compared to the previous year. As for 2022 itself, as reported by Deloitte, some observers say that semiconductor sales are expected to grow back by 10%, and could exceed US$ 600 billion for the first time in 2022. In the future, semiconductor trends will continue to be needed by various industries, although there is economic uncertainty is predicted, chipset availability is also expected to recover in 2023.
Moore's Law predicts that the number of transistors in integrated circuits (IC) will double every year, is used as a reference by the semiconductor industry to set their research and development targets. This is evidenced by the birth of microprocessor capabilities that are increasing every year. But even Moore's law will eventually meet an impenetrable limit, increasing computer performance by adding transistors has so far been done by reducing the size of the transistor so that it can fit more in the same area. A few years ago, physicist Michio Kaku noted that there was a point where the silicon material used to make the transistor — or any substitute for it — could not be reduced any further.
Several studies have initiated the use of other materials for the development of semiconductors. Third-generation semiconductor materials, such as gallium nitride (GaN) and silicon carbide (SiC), promise high-temperature resistance, high breakdown voltage, high frequency, high power, and high radiation resistance.
However, for a long time, the use of these materials was limited to a narrow range of fields due to their complex processing methods and high cost.
In recent years, breakthroughs in material growth and device fabrication have helped reduce the cost of third-generation semiconductor materials, enabling a wider range of applications. For example, SiC-based devices used for car inverters and GaN-based fast chargers appeared on the market.
Semiconductor technology trends that have also been widely discussed to improve chip capabilities include parallel computing, quantum computing, to protein computers that work with DNA.
Semiconductor is a material that has electrical properties between conductors and insulators. Semiconductors bring drastic changes in the technological development of mankind. From Shockley and Fairchild who make transistors to large manufacturers of chipset makers to giants like Intel that use semiconductors to create technology that plays a very important role in the development of computers, gadgets, household appliances, automation, telecommunications, and so on.
The technological trend proclaimed by Moore’s Law has already occurred, and it is predicted that the number of transistor densities in a wafer will also be achieved. Therefore, there are various developments carried out to maximize semiconductors such as the use of third-generation materials, quantum computing, etc. semiconductor trends will continue to be needed by various industries, although economic uncertainty is predicted, chipset or semiconductors availability is also expected to recover in 2023.


- Advances in quantum computing could help us simulate large complex molecules.
- These simulations could uncover new catalysts for carbon capture that are cheaper and more efficient than current models.
- We can currently simulate small molecules up to a few dozen qubits but need to scale this to the order of 1 million.
Imagine being able to cheaply and easily “suck” carbon directly out of our atmosphere. Such a capability would be hugely powerful in the fight against climate change and advance us towards the ambitious global climate goals set.
Surely that’s science fiction? Well, maybe not. Quantum computing may be just the tool we need to design such a clean, safe and easy-to-deploy innovation.
In 1995 I first learned that quantum computing might bring about a revolution akin to the agricultural, industrial and digital ones we’ve already had. Back then it seemed far-fetched that quantum mechanics could be harnessed to such momentous effect; given recent events, it seems much, much more likely.
Much excitement followed Google’s recent announcement of quantum supremacy: “[T]he point where quantum computers can do things that classical computers can’t, regardless of whether those tasks are useful”.
The question now is whether we can develop the large-scale, error-corrected quantum computers that are required to realize profoundly useful applications.
The good news is we already concretely know how to use such fully-fledged quantum computers for many important tasks across science and technology. One such task is the simulation of molecules to determine their properties, interactions, and reactions with other molecules – a.k.a. chemistry – the very essence of the material world we live in.
While simulating molecules may seem like an esoteric pastime for scientists, it does, in fact, underpin almost every aspect of the world and our activity in it. Understanding their properties unlocks powerful new pharmaceuticals, batteries, clean-energy devices and even innovations for carbon capture.
To date, we haven’t found a way to simulate large complex molecules – with conventional computers, we never will, because the problem is one that grows exponentially with the size or complexity of the molecules being simulated. Crudely speaking, if simulating a molecule with 10 atoms takes a minute, a molecule with 11 takes two minutes, one with 12 atoms takes four minutes and so on. This exponential scaling quickly renders a traditional computer useless: simulating a molecule with just 70 atoms would take longer than the lifetime of the universe (13 billion years).
This is infuriating, not just because we can’t simulate existing important molecules that we find (and use) in nature – including within our own body – and thereby understand their behaviour; but also because there is an infinite number of new molecules that we could design for new applications.
That’s where quantum computers could come to our rescue, thanks to the late, great physicist Richard Feynman. Back in 1981, he recognized that quantum computers could do that which would be impossible for classical computers when it comes to simulating molecules. Thanks to recent work by Microsoft and others we now have concrete recipes for performing these simulations.
A quantum catalyst to tackling climate change?
One area of urgent practical importance where quantum simulation could be hugely valuable is in meeting the SDGs – not only in health, energy, industry, innovation and infrastructure but also in climate action. Examples include room-temperature superconductors (that could reduce the 10% of energy production lost in transmission), more efficient processes to produce nitrogen-based fertilizers that feed the world’s population and new, far more efficient batteries.
One very powerful application of molecular simulation is in the design of new catalysts that speed up chemical reactions. It is estimated that 90% of all commercially produced chemical products involve catalysts (in living systems, they’re called enzymes).
A catalyst for “scrubbing” carbon dioxide directly from the atmosphere could be a powerful tool in tackling climate change. Although CO2 is captured naturally, by oceans and trees, CO2 production has exceeded these natural capture rates for many decades.
The best way to tackle CO2 is not releasing more CO2; the next best thing is capturing it. “While we can’t literally turn back time, [it] is a bit like rewinding the emissions clock,” according to Torben Daeneke at RMIT University.
There are known catalysts for carbon capture but most contain expensive precious metals or are difficult or expensive to produce and/or deploy. “We currently don’t know many cheap and readily available catalysts for CO2 reduction,” says Ulf-Peter Apfel of Ruhr-University Bochum.
Given the infinite number of candidate molecules that are available, we are right to be optimistic that there is a catalyst (or indeed many) to be found that will do the job cheaply and easily. Finding such a catalyst, however, is a daunting task without the ability to simulate the properties of candidate molecules.
And that’s where quantum computing could help.
We might even find a cheap catalyst that enables efficient carbon dioxide recycling and produces useful by-products like hydrogen (a fuel) or carbon monoxide (a common source material in the chemical industry).
Quantum computing to the rescue – what will it take?
We can currently simulate small molecules on prototype quantum computers with up to a few dozen qubits (the quantum equivalent of classical computer bits). But scaling this to useful tasks, like discovering new CO2 catalysts, will require error correction and simulation to the order of 1 million qubits.
It’s a challenge I have long believed will only be met on any human timescale – certainly by the 2030 target for the SDGs – if we use the existing manufacturing capability of the silicon chip industry.
The path forward
At a meeting of the World Economic Forum’s Global Future Councils last month a team of experts from across industry, academia and beyond assembled to discuss how quantum computing can help address global challenges, as highlighted by the SDGs, and climate in particular.
As co-chair of the Global Future Council on Quantum Computing, I was excited that we were unanimous in agreeing that the world should devote more resources, including in education, to developing the powerful quantum computing capability that could help tackle climate change, meet the SDGs more widely and much more. We enthusiastically called for more international cooperation to develop this important technology on the 2030 timescale to have an impact on delivering the SDGs, in particular climate.
So the real question for me is: can we do it in time? Will we make sufficiently powerful quantum computers on that timeframe? I believe so. There are, of course, many other things we can and should do to tackle climate change, but developing large-scale, error-corrected quantum computers is a hedge we cannot afford to go without.
This article is republished from the World Economic Forum.


The drive to solve problems faster and more efficiently is never going to stop, and this has led to the enhancements in existing technologies as well as invention several new ones. This hunger, combined with the competitive spirit of scientific research, has led humankind to a new era, the era of Quantum computing.
Quantum computers and quantum computing are technical and complicated as they sound. Quantum computers have been in development for an extended period but have never found practical usage. Scientists believe these technological marvels to be significantly faster than your conventional desktop or even the existing supercomputers. But how do quantum computers work? Read ahead to know all about quantum computing.
How do Quantum Computers work?
Quantum Computers work by performing calculations on the probability of an object’s state before it is even measured. Classic computers work on performing operations on 0 or 1 — the binary states. These 0 or 1 are the definite positions of the physical states. quantum computers can calculate much more data than classic computers using this probability of the state of an object.
Just like modern computing required bits to process data, Quantum computing requires qubits to process and analyse data. Qubit is the quantum state of the object, which is the undefined property of the object before it is detected. These properties include the spin of electrons or the states of a coin when tossed or the polarisation of the photon.
These quantum states of the object can look random but are inter-related or entangled. The superpositions are mathematically relatable to the result, and by putting the quantum states into unique algorithms, we can make advancements in the fields never touched before.
Quantum computers can help solve complex mathematical equations, improve machine learning techniques, producing better security codes and even tackle more complex scenarios.
Because of the potential of processing data at a very high speed and ability to solve complex equations, there are different tech giants such as D-Wave Systems, IBM and Google, that are claiming to be very close to achieving quantum supremacy.
Quantum supremacy is showcasing that a programmable quantum device can solve a problem that classical computers practically cannot within a viable time.
The Race for Quantum Computing
D-Wave Systems is one of the leading Quantum computer manufacturers, and have been producing, selling and setting up quantum computers at various organisation worldwide such as the University of Southern California, Google, NASA and Los Alamos National Lab. D-Wave has already produced a 2048 qubit quantum computer and has announced a much bigger quantum computer.
The company has announced its fifth generation, a 5000-qubit quantum computer that will release in mid-2020. D-Wave has named it Advantage, which uses the company’s latest Pegasus topology that provides better and higher connectivity. This helps in solving more complex problems than before.
In the same year, on October 23, 2019, Google announced that they had achieved Quantum Supremacy. The company said that they have successfully solved a problem that would take a considerable amount of time, even on the most powerful supercomputer available today. Using a quantum computer named Sycamore, researchers at Google performed random circuit sampling. Random circuit sampling is a sequence of random operation done on qubits.
After performing all operations multiple times, they measured the values of the qubits. The researchers received a number distribution close to random but were still interrelated because of quantum effects. Performing all these operations on the most powerful computing platform available will take around 10,000 years, while Sycamore took 200 seconds to complete the operation and all calculations according to the team.
“With the first quantum computation that cannot reasonably be emulated on a classical computer, we have opened up a new realm of computing to be explored”, wrote Google researchers John Martinis and Sergio Boixo in a Google AI blog.
But does this stop here?
Even before Google announced quantum supremacy, IBM published a report on October 21, 2019, in which the tech giant claimed that the calculations by 53 and 54 qubits Symacore circuits can be done using the classic algorithms and within a couple of days.
IBM has also been working on its quantum computer, which has now been available on the cloud by IBM. The company has named it IBM Q System One, and organisations can pay and reserve their time on the machine.
Major businesses and companies such as Goldman Sachs, Samsung, JPMorgan Chase & Co. among other big-wigs, are investing their time and wealth in System One to see how quantum computing can be used in real-life scenarios. IBM has been developing and increasing the number of qubits in IBM Q since May 2016, when it was first launched.
There has been a lot of development in this field, but we still haven’t reached the stage where we can put this technology into daily-life use. There are a lot of areas in which your laptop is much powerful and efficient than quantum computers.
Even with the continuous developments and advancements, practical quantum computers are a thing of the future. It will take at least a decade — if not more — for them to replace the computers we are using. To fit enough number of qubits that can solve any problem thrown at it will take years in development.
But if we develop a practical quantum computer, it can track down any information available, decode all the security measures of any platform, mine cryptocurrency with no hassle, and search for a piece of information in a million database within seconds. The possibilities are endless and might even be beyond our imaginations, but the technology needs to evolve, and only time will tell what it has to offer.
Also read: TPU vs GPU vs CPU
A BTech student whose interest lies in automobiles, tech, music, coding and badminton.


Nanoscale discovery could help cool overheating in electronics
A team of physicists at CU Boulder has solved the mystery behind a puzzling phenomenon in the nano realm: why some ultra-small heat sources cool faster if you move them close together. The results, published today in the journal Proceedings of the National Academy of Sciences (PNAS), could one day help the tech industry to design faster electronic devices that overheat less.
“Often times heat is a difficult consideration in electronics design. You build a device and then find it heats up faster than you want,” said study co-author Joshua Knobloch, postdoctoral research associate. at JILA, a joint research institute between CU Boulder and the National Institute of Standards and Technology (NIST). “Our goal is to understand the fundamental physics involved so that we can design future devices to effectively manage heat flow.”
The research began with an unexplained observation: In 2015, researchers led by physicists Margaret Murnane and Henry Kapteyn at JILA were experimenting with metal bars several times thinner than the width of a human hair on a silicon base. When they heated these bars with a laser, something strange happened.
“They behaved in a very counterintuitive manner,” Knobloch said. “These nanoscale heat sources don’t usually dissipate heat efficiently. But if you pack them together, they cool much faster.”
Now researchers know why this is happening.
In the new study, they used computer simulations to track the passage of heat from their nanoscale bars. They found that when they brought the heat sources closer together, the energy vibrations they produced began to bounce off each other, dispersing the heat and cooling the bars.
The group’s findings highlight a major challenge in designing the next generation of tiny devices, such as microprocessors or quantum computing chips: when you scale yourself down to very small scales, heat doesn’t always behave like you think so.
Atom by atom
Heat transmission in devices is important, the researchers added. Even tiny flaws in the design of electronics like computer chips can allow temperature to build up, increasing wear and tear on a device. As tech companies strive to produce ever smaller electronic devices, they will need to pay more attention than ever to phonons, vibrations of atoms that carry heat in solids.
“The heat flow involves very complex processes, which makes it difficult to control,” Knobloch said. “But if we can understand how phonons behave on a small scale, then we can tailor their transport, which allows us to build more efficient devices.”
To do this, Murnane and Kapteyn and their team of experimental physicists joined forces with a group of theorists led by Mahmoud Hussein, professor in the Ann and HJ Smead department of aerospace engineering sciences. His group specializes in the simulation or modeling of the movement of phonons.
“On an atomic scale, the very nature of heat transfer is emerging in a new light,” said Hussein, who also has a courtesy appointment in the physics department.
The researchers essentially recreated their experiment from several years ago, but this time, entirely on a computer. They modeled a series of silicon bars, laid side by side like the slats of a railroad track and heated them.
The simulations were so detailed, Knobloch said, that the team was able to track the behavior of every atom in the model, millions in all, from start to finish.
“We were really pushing the memory limits of the Summit supercomputer at CU Boulder,” he said.
Direct the heat
The technique paid off. The researchers found, for example, that when they spread their silicon bars far enough apart, heat tended to escape from these materials in a predictable way. Energy leaked out of the bars and into the material below, dissipating in all directions.
However, when the bars got closer, something else happened. As the heat from these sources dispersed, it effectively forced that energy to flow more intensely away from the sources, like a crowd of people in a stadium jostling against each other and leaping by. the exit. The team called this phenomenon “directional heat channeling”.
“This phenomenon increases heat transport down into the substrate and away from heat sources,” Knobloch said.
Researchers suspect that engineers may one day exploit this unusual behavior to better understand how heat flows through small electronic devices, directing that energy along a desired path, instead of letting it run freely and freely.
For now, researchers see the latest study as what scientists from different disciplines can do when working together.
“This project was an exciting collaboration between science and engineering, where the advanced methods of computational analysis developed by Mahmoud’s group were essential to understanding the behavior of new materials discovered earlier by our group using new extreme ultraviolet quantum light sources, “said Murnane, also a professor of physics.
CU Boulder’s other co-authors on the new research include Hossein Honarvar, postdoctoral researcher in aerospace engineering sciences and JILA and Brendan McBennett, graduate student at JILA. Former JILA researchers Travis Frazer, Begoña Abad and Jorge Hernandez-Charpak also contributed to the study.
New heat management material keeps computers cool
Directional thermal channeling: Phenomenon triggered by tight compression of heat sources, Proceedings of the National Academy of Sciences (2021). DOI: 10.1073 / pnas.2109056118
Provided by the University of Colorado at Boulder
Quote: Nanoscale Discovery Could Help Cool Overheating in Electronics (2021, September 20) Retrieved September 20, 2021 from https://phys.org/news/2021-09-nano-scale-discovery- cool-overheating-electronics. html
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Sodium is a chemical element with symbol Na (from Ancient Greek Νάτριο) and atomic number 11. It is a soft, silver-white, highly reactive metal. In the Periodic
In this video we'll look at the atomic structure, valence electrons, Given: Density = 0.97 g/cm 3, Molar mass (M) = 23 g/mol To find: Radius of sodium atom (r) Formula: 1. Density (ρ) = `"M n"/("a"^3 "N"_"A")` 2. For bcc unit cell, r = `(sqrt3 "a")/4` Calculation: For a bcc lattice, number of atoms per unit cell is 2. ∴ n = 2. From formula (i), 2011-10-11 Down to the atom: Through different imaging methods, electron microscopy can provide direct observation of oxygen atoms and sodium cations, pointing … Sodium at standard temperature and pressure is a soft silvery metal that combines with oxygen in the air and forms grayish white sodium oxide unless immersed in oil or inert gas, which are the conditions it is usually stored in. Sodium metal can be easily cut with a knife and is a good conductor of electricity and heat because it has only one electron in its valence shell, resulting in weak Sodium Atom Sodium atoms are ionized mostly by charge transfer with the ambient NO+ and O2+ ions, with a small contribution from solar photoionization. From: Encyclopedia of Atmospheric Sciences , 2003 Atomic Mass of Sodium Atomic mass of Sodium is 22.9897 u.
natrium) — химический элемент первой группы, третьего периода Показывать компактно. ↑ Atomic weights of the elements 2013 ( IUPAC Technical Report) (англ.) — IUPAC, 1960. — ISSN 0033-4545; 1365- 3075; Chemical element, symbol: Na, atomic number: 11 and atomic weight 22,9898. It's a soft metal, reactive and with a low melting point, with a relative density of 0 Sodium. Symbol, Na, Atomic number, 11. Atomic mass 2 Mar 2020 Answer: Atomic structure of a sodium ion : Explanation : Sodium atom have 11 electrons ,thus we have to draw 3 rings around the word"Na" The Kossel shell structure of sodium. Atomic spectrum.
5 Sep 2020 Most atoms do not have eight electrons in their valence electron shell. As demonstrated here, a sodium atom (Na) has one valence electron
Fig. 35. EDX map and point analysis for alloy RC AM50 exposed in the presence of 400 ppm CO2 and 70 µg/cm² Natriumklorid, NaCl: vanligt koksalt, består av jonerna Na+ och Cl-. Natriumklorid förekommer rikligt i naturen. Det utvinns ur saltgruvor eller genom avdunstning Thus, sodium ion (Na+) has eight valence electrons.
The octet rule is a result of trends in energies and is useful in explaining why atoms form the ions that they do. Now consider an Na atom in the presence of a Cl
an equal number of protons and electrons. In sodium ion, there are 1 1 protons but 1 0 electrons.
In writing the electron configuration for sodium the first two electrons will go in the 1s orbital. Since 1s can only hold two electrons the next 2 electrons for sodium go in the 2s orbital.
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To balance this charge (this is a NEUTRAL metal atom!) there must be 11 electrons, 11 negatively charged particles circling the nucleus.
3) What type of bonding does water show? 4) What type of bonding is this? Fulleren Qubit kol nanorör Molekyl Atom, Sodium Atom 24, atom, Bloch sfär png Buckminsterfullerene Molecule Atom Science, kemi, allotropy, atom png
As Book 2 ends, we discover that the elements are incredibly sad with tears running down their dear little atom faces. Sodium's investigation to find a way to
Standardize 0.1 mol/L sodium hydroxide NaOH titrant with KHP using KHP has one acidic hydrogen atom, and reacts with NaOH on a 1:1 stoichiometric basis.
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Find sodium atom stock images in HD and millions of other royalty-free stock photos, illustrations and vectors in the Shutterstock collection. Thousands of new, high-quality pictures added every day.
Moles = Number of sodium atoms/ Avogadro's number Sodium atoms = 1.56 x … 2016-02-20 2000-01-01 The Sodium Zeeman Effect The sodium spectrum is dominated by the bright doublet known as the Sodium D-lines at 588.9950 and 589.5924 nanometers. From the energy level diagram it can be seen that these lines are emitted in a transition from the 3p to the 3s levels.
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When we write the configuration we'll put all 11 electrons in orbitals around the nucleus of the Sodium atom. In writing the electron configuration for sodium the first two electrons will go in the 1s orbital. Since 1s can only hold two electrons the next 2 electrons for sodium go in the 2s orbital. The nex six electrons will go in the 2p orbital.
=> 6.022 * 10^23 atoms weigh 23 grams => 1 atom weighs 23/(Avogadro number) grams => 3.8 In covalent bonds, two atoms share pairs of electrons, while in ionic bonds, In the diagram above, we see a neutral atom of sodium, Na, losing an electron. 5 Sep 2020 Most atoms do not have eight electrons in their valence electron shell. As demonstrated here, a sodium atom (Na) has one valence electron Download 606 Sodium Atom Stock Illustrations, Vectors & Clipart for FREE or amazingly low rates! New users enjoy 60% OFF. 158903279 stock photos online.
Om du vill flytta en atom, kan du dra i den med verktyget med pilar i ändarna. Om du vill sodium chloride. Välj sedan Chrystallography Open Database, vid
Sodium (Na), chemical element of the alkali metal group (Group 1 [Ia]) of the periodic table. Sodium is a very soft silvery-white metal. Sodium is the most common alkali metal and the sixth most abundant element on Earth, comprising 2.8 percent of Earth’s crust. Name: Sodium Symbol: Na Atomic Number: 11 Atomic Mass: 22.98977 amu Melting Point: 97.72 °C (370.87 K, 207.9 °F) Boiling Point: 883 °C (1156 K, 1621 °F) Number of Protons/Electrons: 11 Number of Neutrons: 12 Classification: Alkali Metal Crystal Structure: Cubic Density @ 293 K: 0.971 g/cm 3 Color: silvery Atomic Structure Sodium is an atom that has 11 protons and 12 neutrons in its nucleus and 11 electrons circling around its nucleus. Like other light atoms such as carbon, sodium forms inside of stars that are beginning to run out of fuel, and it scatters all over space when that star explodes in a supernova. Sodium is soft, and you can cut it with a knife.
To give an idea of how large this number is, 1 mole of pennies would be enough money to pay all the expenses of each country on earth for about the next billion years. Se hela listan på periodic-table.com First calculate the moles of Na. 1 mole atoms = 6.022×10^23 atoms Use dimensional analysis to convert atoms to moles. 9.76*10^12 atoms Na x (1 mol Na/6.022*10^23 atoms Na) = 1.621*10^-11 mol Na Calculate mass in grams of Na by multiplying mole Na An atom of sodium-23 (Na-23) has a net charge of + 1. identify the number of protons, neutrons, and electrons in the atom. How did you determine the number of each type.


When looking back into the deep past of the Universe, which means looking out over vast cosmological distances of space, there are observed a peculiar set of galaxies emitting a tremendous amount of energy. These early galaxies, known variously as quasars, blazars, radio galaxies and radio-loud quasars, are all bodies classified as active galactic nuclei. These objects are some of the most energetic phenomena in the universe, if the name blazar was not at all evident of this fact. Active galactic nuclei represent a confirmation of physicist Nassim Haramein’s prediction that black holes are the spacetime structure that forms the seed around which galaxies and stars form. Indeed, it is now widely understood that the early formation of galaxies, producing active galactic nuclei, are in fact due to the action of supermassive black holes – black holes in upwards of a million to a billion solar masses.
The super-anatomy of these central galactic black holes are as intriguing as the enigmatic beacons they form in the deep field of space. Although all major galaxies probably have a supermassive black hole at the central region, as this is the structure that initiates galaxy formation in the first place, active galactic nuclei are thought to represent a different early phase of this process when the super massive black holes were extremely active, emitting large amounts of energy (and probably matter as well); forming the first galaxies. Additionally, as a consequence of accreting the pre-galactic material, massive amounts of matter were being both gravitated into the central black holes as well as emitted from their poles. The inflowing matter forms an ultra-hot accretion disc around the equatorial region of the black hole, and relativistic jets (charged particles, or electron-positron plasma, moving at relativistic speeds) stream along the axis of rotation and can extend up to hundreds of thousands of light years.
"The implied alignment of the spin axes of massive black holes that give rise [to] the radio jets suggest the presence of large-scale spatial coherence in angular momentum” – A. Taylor & P. Jagannathan
These extremely energetic and massive structures are readily identified when viewing deep space images collected in the radio wave band of the electromagnetic spectrum. The scale of observation is grand: gathering light from numerous galaxies across several million parsecs of space. Equally, the instrumentation used to gather light from such distant and vast sources is colossal. Think of the Arecibo Radio Telescope, featured in such films as Contact, to get an idea of how massive these telescopes can be.
One such telescope under proposal is the Square Kilometer Array, which will be one of the largest scientific observational instruments ever constructed, as the “lens” of the telescope is essentially a square kilometer in area. This telescope, when completed, will be contributory in determining fundamental cosmological parameters and probing the earliest epochs of galaxy formation.
In a recent study using the Giant Meterwave Radio Telescope, South African astronomers made a remarkable discovery when analyzing the alignment of the spin axis of 64 galaxies.
The orientation of the axis of rotation of active galactic nuclei are directly observable because of the long plasma jets streaming from the poles of the central supermassive black hole, with strong electromagnetic emissions in the radio frequency range. Reported in the Monthly Notices of the Royal Astronomical Society, the astrophysicist team analyzed the orientation of the radio jet position angles and found that a surprisingly large number of supermassive black holes were aligned with their axes of spin.
Statistical analysis revealed that there was a 0.1% probability of such an alignment occurring by chance – strongly indicating that there is some as yet unseen force that is producing strong coherence among cosmological-scale objects. Moreover, this may imply that conditions during the earliest epochs of galactic formation deviate from complete isotropy, referring to the uniformity of the distribution of matter.
It has long been presumed that the universe is homogeneous and isotropic (the same in all locations) with no identifiable axis or orientation. Indeed, this is known as the cosmological principle. Yet, one of the 20th century’s greatest minds, Kurt Gödel, provided an exact solution of the Einstein field equations that described a rotating universe. In commentary about Gödel’s work, physicist Stephen Hawking said:
"These models could well be a reasonable description of the universe that we observe, however observational data are compatible only with a very low rate of rotation. The quality of these observations improved continually up until Gödel's death, and he would always ask "is the universe rotating yet?" and be told "no, it isn't."
In more recent events, there have been several findings that suggest that the universe is indeed not entirely homogenous and isotropic. Such examples come from the so called axis of evil identified during an analysis of the microwave background radiation, dark flow, Shamir’s report on the Sloan Digital Sky Survey showing that left-twisted galaxies were much more common than right-swirling galaxies; as well as structural mapping such as the BOSS Great Wall and Laniakea.
While the strong correlation of spin alignment of multiple super massive black holes across cosmological distances may seem puzzling -- since under standard presumptions there should be very little to no interaction of galactic nuclei across such vast distances -- Haramein has long described the dynamics and properties of spacetime that would naturally produce such correlated orientation and entanglement of objects that has been observed in this latest study.
Haramein has explained the structural and geometric properties of space and matter from the smallest to the largest scale, and it is in consideration of the largest scale structure, the universe itself, that we gleam an understanding of how and why these vast arrays of galaxies are uniformly aligned in their axis of rotation. Namely, just as we have seen from indications of the “axis of evil”, “dark flow”, the great wall and great voids, the universe is not isotropic, but instead has a definite orientation.
Haramein has identified this large-scale structure as a double-toroidal counter-rotating geometry. Thus, not only are phenomena like “dark flow” and seeming accelerated expansion of space observed, but just as in the most recent discovery: strong alignment of galaxies as well. The reason for this – the uniform spin of the universe, which has a strong correlating (entangling) effect on the objects that are uniformly effected by the Coriolis forces of the spinning structure. Spin dynamics naturally produce strong coherence.
From this profound theory, we see that spin is not the result of matter accretion in the early universe, but instead it is the intrinsic spin and high curvature of spacetime that engenders gravitational accretion of matter into the structures that are observed. Since spin “came first”, we would expect there to be a remarkably high degree of correlation of the spin axes of primordial active galactic nuclei.In the paper The Origin of Spin: A Consideration of Torque and Coriolis Forces in Einstein’s Field Equations and Grand Unification Theory, Haramein and Elizabeth Rauscher evaluate the inclusion of torque and Coriolis effects in Einstein’s field equations of spacetime geometry (gravity). The main result of such a consideration is that spin is an intrinsic characteristic of spacetime itself, explaining galactic formations, polar jets, accretion discs, spiral arms, and galactic halos without the need for exotic forms of dark matter constructs. Remarkably, this is an instrumental facet of a Grand Unification Theory as the torque and Coriolis effects of spacetime produce the bodies and particle interactions that are observed at the atomic and hadron scale.
With further consideration, could it be possible that there are additional forces that would allow for the preservation of such strong alignment over time? For instance, it is possible that galactic magnetic field interactions, which have been observed at cosmological scales, could be at play in stabilizing the strong alignment of the polar radio jets of the supermassive black holes and maintaining the anisotropy over long periods of time. Indeed, instrument such as the Square Kilometer Radio Telescope will allow for the study and analysis of galactic magnetic field interactions to see to what degree this can be involved in large-scale galactic interactions.
There is another important interaction however that may be involved in the strong correlation of the spin axes observed in the supermassive black holes, and like the intrinsic spin of spacetime described by Haramein, it is another intriguing spacetime geometrical object. Known technically as Einstein Rosen Bridges (ER bridges) for the two physicist who first described their properties through maximally extended Schwarzschild solutions of Einstein’s field equations, we know them more colloquially as wormholes.
Haramein has long described how the black holes that form the hearts of stellar and galactic objects are connected in a vast spacetime wormhole network. Meaning that black holes will be entangled across vast spatial and temporal distances, much like what has been observed in the radio jet spin alignment.
Interestingly, more recent advances in Unified Physics have equated spacetime wormholes with the phenomenon of quantum entanglement. This is summarized by the statement that Einstein Rosen bridges produce Einstein Rosen Correlations, expressed concisely as ER = EPR. This means that not only does spacetime geometry entangle astronomical-scale black holes, but miniature ones as well (what are referred to as fundamental particles).
What we are observing in this latest study may very well be quantum entanglement at a cosmological scale, as a result of the fluid dynamics of spacetime, linking together the connected universe.
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How do you stop light in midflight and hold on to it – even for a fraction of a second? This ability could be crucial to such future quantum optical systems as secure communications or new kinds of information technologies. A group led by Dr. Ofer Firstenberg at the Weizmann Institute of Science recently demonstrated a method in which individual particles of light – photons – are trapped and released on demand in way that might, in the future, be used as memory for quantum information. A description of their quantum optical memory was recently published in Science Advances.
Photons can carry information in the same way that electrons do, explains Firstenberg, who is in the Institute’s Physics of Complex Systems Department. In addition, they can travel long distances, for example in optical fibers, without losing that information; so in future quantum memory and information technologies, photon-based systems may be better than electronic ones for certain kinds of communication and remote sensing. Like electronic systems, photon-based systems need to package and synchronize multiple bits of information. To create such “photon packages,” the timing of the photons must be controlled. Existing devices – photon sources – are able to shoot single photons, but they do so randomly. There is no way to predict exactly when the photon will escape the source or how much time will elapse until the next one is freed. One way to deal with this lack of control is to find a way of capturing the photons, holding them in one place and releasing them on demand – that is, temporarily storing particles of light.
Although Firstenberg and his group are not the first to store photons, they are the first to do so in a way that works at room temperature and is relatively fast, very efficient and noiseless (with no distortion in the information). They called their system FLAME, for Fast Ladder Memory. It consists of laser sources and a small amount of pure atomic gas – in this case, of the element rubidium. The electrons of the rubidium atoms act as the “photon memory,” and strong laser pulses are used for the writing and reading processes. The flying photons are first stored in electrons that have been excited – that is, the electrons’ orbit around the nuclei moves out a notch. Then some tens of nanoseconds later – long enough to synchronize the output from many fast photon sources – the memory is read, returning the electrons to their normal ground state and the photons to their flight.
FLAME, explain the scientists, is considered to be almost completely free of noise – unwanted disturbances that often plague such systems — because what goes in is what comes out. “The photons that are released from the electrons are identical to those we put in – with the exact same properties and propagation direction. So something like one in 10,000 might be a photon we did not put there. As a quantum memory, the system is fantastic,” says PhD student Ran Finkelstein, who led this study together with Dr. Eilon Poem in Firstenberg’s lab.
These findings were published in Science Advances, together with the results of similar experiments conducted at Oxford University, UK.
Today, the experimental setup takes up a large table – mostly covered in lasers, mirrors and lenses, but the actual trapping takes place in a container the size of a thumb. Eventually, the scientists hope to miniaturize the process: An atomic gas containing billions of atoms can be contained in a sealed space of one cubic millimeter, and since the atoms return to their original state, it can be reused almost indefinitely. “We need only three elements – a photon source, a contained gas cloud and a strong laser,” says Finkelstein. “This is not a delicate system that works only in ultrahigh vacuum or at very low temperatures. Eventually we’ll be able to insert a system like this in something the size of a cell phone.”
Farther in the future, the idea of using photons to convey information in such processes as quantum computing, communications or sensing could involve one of the stranger aspects of quantum physics – a phenomenon known as entanglement. Famously called “spooky action at a distance,” when two particles are entangled, a change to one results in an instantaneous change in the other – meaning information is somehow shared non-locally (that is, there is no way information could have been passed from one to the other by standard means). “If the trapped photons were first entangled with other photons some distance away, this would be quantum communication in the true sense of the word – really based on principles of quantum mechanics that we can’t observe in the everyday world,” says Poem. Quantum communication, if it could be developed, would be almost impossible to tamper with, and thus researchers believe it could be especially useful for new kinds of encryption.
Eventually we’ll be able to insert a system like this in something the size of a cell phone
Firstenberg and his group plan to test entangled photons in the FLAME setup, and they have other ideas as well, for new experiments with their quantum optical system. For example, they intend to create more complex components, such as logic gates for the information carried by the stored photons.
“While we still don’t know which future quantum information systems will prevail,” says Firstenberg, “there are some things for which we know photons are best. For example, the recent discovery of gravitational waves in a distant galaxy relied on powerful optical sensors. Our communications are already sent by light waves through thin optic fibers; photon ‘quantum bits’ can travel in similar fibers. So quantum memory systems based on single photons may have applications in the not-too-distant future.” True quantum information processing with photons may be in the distant future, but the current research in Firstenberg’s lab in developing efficient and noiseless optical quantum memory is bringing that future closer.
Photons move at, well, the speed of light, and they are easily destroyed. Each and every one of us constantly destroys photons as our eyes take in and absorb light. And they are extremely faint, so it takes a very fine “net” to catch them, even for a fraction of a second. So why do scientists search for ways of trapping and using single photons? Photons can be varied and manipulated in ways that electrons cannot, and if they are left undisturbed they can travel through transparent materials or vacuum practically forever without losing their strength. Since single photons obey the laws of quantum mechanics, researchers hope to find ways of applying some of that “quantum weirdness” to create new types of computation, memory and especially communications. Several ideas for using photons to secure communications have been suggested. If a single photon were used as a “key,” for example, anyone trying to intercede in the transmission would destroy that key. Similarly, if photons at either end were entangled, a change in the photon at the receiving end would alert the recipient that tampering had occurred.
Dr. Ofer Firstenberg’s research is supported by the Sir Charles Clore Research Prize; the Laboratory in Memory of Leon and Blacky Broder, Switzerland; and the European Research Council.


Quantum superposition has been used to compare data from two different sources more efficiently than is possible, even in principle, on a conventional computer. The scheme is called “quantum fingerprinting” and has been demonstrated by physicists in China. It could ultimately lead to better large-scale integrated circuits and more energy-efficient communication.
Quantum fingerprinting offers a way of minimizing the amount of information that is transferred between physically separated computers that are working together to solve a problem. It involves two people – Alice and Bob – each sending a file containing n bits of data to a third-party referee, whose job is to judge whether or not the two files are identical. A practical example could be a security system that compares a person’s fingerprint to a digital image.
Proposed theoretically in 2001, quantum fingerprinting can make a comparison in an exponentially more efficient way than is possible using conventional computers. While the only way to ensure a complete comparison is to send the two files in their entirety, it turns out that a reasonably accurate comparison can be achieved classically by sending just the square root of the number of bits.
Quantum mechanics allows comparisons with even less data because a quantum bit (qubit) of information can exist not just as a zero or a one but, in principle at least, also in an infinite number of intermediate states. The vast increase in the number of possible combinations of states for a given number means that the number of physical bits that need to be transmitted scales logarithmically with the number of bits in the two files. As such, quantum fingerprinting permits an exponential reduction in data-transmission rates over classical algorithms.
The original proposal for quantum fingerprinting involved using log n highly entangled qubits, which Norbert Lütkenhaus of the University of Waterloo in Canada says is still many more qubits than can be implemented using today’s technology. In 2014 he and Juan Miguel Arrazola, now at the National University of Singapore, unveiled a more practical scheme. This involves Alice and Bob encoding their n bits in the optical phase of a series of laser pulses, and then sending those pulses to a beam splitter (the referee). The pairs of pulses arrive at the beam splitter one at a time – if the two pulses have the same phase they exit from one port, whereas opposite phases cause them to leave from a second port. In this way, the two files are judged to be identical if there is no signal at the second port.
The ramp up in efficiency is due to the fact that each pulse can be made from a tiny fraction of a single photon. This means that, on average, the pulses contain less than one photon, which is achieved by attenuating the laser light. This means n pulses can be encoded using just log n photons. As Lütkenhaus points out, the number of photons cannot be made arbitrarily small because there needs to be a reasonable chance that a photon is detected when the phases are different, for the referee to obtain the right answer: that the files are or are not identical. “The scheme gives us an asymptotically accurate result,” he says. “The more photons I put in, the closer I get to the black and white probability.”
Last year, Lütkenhaus and Arrazola, working with Hoi-Kwong Lo, Feihu Xu and other physicists at the University of Toronto, put the scheme into practice by modifying a quantum-key-distribution system sold commercially by the firm ID Quantique in Geneva. They showed that they could match files as large as 100 megabits using less information than is possible with the best-known classical protocol. They did admit, however, that their scheme, while more energy efficient, took more time to carry out.
Now, a group led by Jian-Wei Pan and Qiang Zhang of the University of Science and Technology of China in Hefei has beaten not only the best existing classical protocol but the theoretical classical limit (which is some two orders of magnitude lower). The researchers did so by using more tailor-made equipment – in particular, they employed superconducting rather than standard avalanche photon detectors, which reduced the number of false-positive signals from the beam splitter and so improved the accuracy of the yes/no outputs, and designed a novel kind of interferometer.
Pan and colleagues successfully compared two roughly two-gigabit video files by transmitting just 1300 photons along 20 km of spooled fibre-optic cable, which is about half of what would be needed classically. Next, they plan to test their system by placing Alice, Bob and the referee at different points in a city such as Shanghai.
Despite Pan’s demonstration, Lütkenhaus thinks that quantum fingerprinting probably won’t be commercialized because its superiority over classical systems depends on fairly artificial conditions, such as the referee being unable to talk back to Alice and Bob. However, he says that the research “opens the door” to other, potentially more useful, applications. One example is database searching when the searcher doesn’t have access to the whole database, while the owner of the database can’t see the search terms. “For this, we have made a protocol but not the technology,” he says.
The work is reported on the arXiv preprint server.


While the word “quantum” has only started trending in the technology space during the last decade, many past technologies already relied on our understanding of the quantum world, from lasers to MRI imaging, electronic transistors, and nuclear power. The reason quantum has become so popular lately is that researchers have become increasingly better at manipulating individual quantum particles (light photons, electrons, atoms) in ways that weren’t possible before. These advances allow us to harness more explicitly the unique and weird properties of the quantum world. They could launch yet another quantum technology revolution in areas like sensing, computation, and communication.
What’s a Quantum Computer?
The power of quantum computers comes chiefly from the superposition principle. A classical bit can only be in a 0 or 1 state, while a quantum bit (qubit) can exist in several 0 and 1 state combinations. When one measures and observes the qubit, it will collapse into just one of these combinations. Each combination has a specific probability of occurring when the qubit collapses.
While two classical bits can only exist in one out of four combinations, two quantum bits can exist in all these combinations simultaneously before being observed. Therefore, these qubits can hold more information than a classical bit, and the amount of information they can hold grows exponentially with each additional qubit. Twenty qubits can already hold a million values simultaneously (220), and 300 qubits can store as many particles as there are in the universe (2300).
However, to harness this potential processing power, we must understand that probabilities in quantum mechanics do not work like conventional probabilities. The probability we learned about in school allowed only for numbers between 0 and 1. On the other hand, probabilities in quantum mechanics behave as waves with amplitudes that can be positive or negative. And just like waves, quantum probabilities can interfere, reinforcing each other or cancelling each other out.
Quantum computers solve computational problems by harnessing such interference. The quantum algorithm choreographs a pattern of interference where the combinations leading to a wrong answer cancel each other out. In contrast, the combinations leading to the correct answer reinforce each other. This process gives the computer a massive speed boost. We only know how to create such interference patterns for particular computational problems, so for most problems, a quantum computer will only be as fast as a conventional computer. However, one problem where quantum computers are much faster than classical ones is finding the prime factors of very large numbers.
How Quantum Computers Threaten Conventional Cryptography
Today’s digital society depends heavily on securely transmitting and storing data. One of the oldest and most widely used methods to encrypt data is called RSA (Rivest-Shamir-Adleman – the surnames of the algorithm’s designers). RSA protocols encrypt messages with a key that results from the multiplication of two very large numbers. Only someone who knows the values of these two numbers can decode the message.
RSA security relies on a mathematical principle: multiplying two large numbers is computationally easy, but the opposite process—figuring out what large numbers were multiplied—is extremely hard, if not practically impossible, for a conventional computer. However, in 1994 mathematician Peter Shor proved that an ideal quantum computer could find the prime factors of large numbers exponentially more quickly than a conventional computer and thus break RSA encryption within hours or days.
While practical quantum computers are likely decades away from implementing Shor’s algorithm with enough performance and scale to break RSA or similar encryption methods, the potential implications are terrifying for our digital society and our data safety.
In combination with private key systems like AES, RSA encrypts most of the traffic on the Internet. Breaking RSA means that emails, online purchases, medical records, company data, and military information, among many others, would all be more susceptible to attacks from malicious third parties. Quantum computers could also crack the digital signatures that ensure the integrity of updates to apps, browsers, operating systems, and other software, opening a path for malware.
This security threat has led to heavy investments in new quantum-resistant encryption. Besides, existing private key systems used in the enterprise telecom sector like AES-256 are already quantum resistant. However, even if these methods are secure now, there is no guarantee that they will remain secure in the future. Someone might discover a way to crack them, just as it happened with RSA.
Quantum Key Distribution and its Impact on the Telecom World
Given these risks, arguably the most secure way to protect data and communications is by fighting quantum with quantum:protect your data from quantum computer hacking by using security protocols that harness the power of quantum physics laws. That’s what quantum key distribution (QKD) does: QKD uses qubits to generate a secret cryptographic key protected by the phenomenon of quantum state collapse. If an attacker tries to eavesdrop and learn information about the key, they will distort the qubits irreversibly. The sender and receiver will see this distortion as errors in their qubit measurements and know that their key has been compromised.
Quantum-safe encryption will take part in people’s day-to-day lives through upgrades to laptops, phones, browsers, and other consumer products. However, most of the burden for quantum-safe communication will be handled by businesses, governments, and cloud service providers that must design and install these systems. It’s a hugely complex change that’s on par with upgrading internet communications from IPv4 to IPv6.
Even if practical quantum computers are not yet available, it’s essential to begin investing in these changes, as explained by Toshiba Chief Digital Officer Taro Shimada: “Sectors such as finance, health and government are now realizing the need to invest in technology that will prepare and protect them for the quantum economy of the future. Our business plan goes far deeper and wider than selling quantum cryptographic hardware. We are developing a quantum platform and services that will not only deliver quantum keys and a quantum network but ultimately enable the birth of a quantum internet”. Toshiba expects the QKD market to grow to approximately $20 billion worldwide in FY 2035.
How Photonics Impacts QKD
Qubits can be photons, electrons, atoms, or any other system that can exist in a quantum state. However, using photons as qubits will likely dominate the quantum communications and QKD application space. We have decades of experience manipulating the properties of photons, such as polarization and phase, to encode qubits. Thanks to optical fiber, we also know how to send photons over long distances with relatively little loss. Besides, optical fiber is already a fundamental component of modern telecommunication networks, so future quantum networks can run on that existing fiber infrastructure. All these signs point towards a new era of quantum photonics.
Photonic QKD devices have been, in some shape or form, commercially available for over 15 years. Still, factors such as the high cost, large size, and the inability to operate over longer distances have slowed their widespread adoption. Many R&D efforts regarding quantum photonics aim to address the size, weight, and power (SWaP) limitations. One way to overcome these limitations and reduce the cost per device would be to integrate every QKD function—generating, manipulating, and detecting photonic qubits—into a single chip. The further development of the integrated quantum photonics (IQP) chip is considered by many as a critical step in building the platform that will unlock quantum applications in much the same way as integrated circuits transformed microelectronics.
In the coming articles, we will discuss more how to combine photonic integration with quantum technologies to address the challenges in quantum communications.
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From designing new polymers and pharmaceuticals to modeling climate change and cracking encryption, quantum computing’s potential applications have sparked a global quantum arms race.
What is Quantum Computing?
Since the birth of the single-chip microprocessor 50 years ago, computers have performed calculations by manipulating bits of information – ones and zeros – using tiny transistors baked into silicon chips. Modern processors cram tens of billions of transistors into a chip the size of a fingernail.
Quantum computing does away with transistors. Instead, the ones and zeros – dubbed “qubits” – are recorded by changing the state of quantum objects, for example changing the magnetic orientation or “spin” of elementary particles like electrons.
Today’s most powerful quantum computers can only string together a few dozen qubits, but they are already putting the most powerful traditional supercomputers to shame at some tasks.
It’s not simply a question of raw processing power. While the electrical charge of a single transistor can either represent a one or a zero, a single qubit can actually represent both one and zero simultaneously thanks to the quirks of quantum mechanics.
This allows quantum computers to process multiple outcomes simultaneously and dramatically reduce the number of steps required to tackle complex problems – solving them in minutes rather than millennia.
Who Is Leading the Way?
Credit: Quantum Computing by IBM, by Microsoft, Google’s Sycamore, Alibaba’s supercomputer
Using the building blocks of the universe to power the next generation supercomputers might seem like science fiction, but quantum computing is already a reality. The US and China are pouring billions of dollars into research and development, while Europe is also investing heavily and breakthroughs are occurring around the globe.
Along with universities, private sector tech giants such as IBM, Microsoft, Google, Amazon, Alibaba and Baidu are also paving the way. At the same time, startups are working to solve some of the challenges which must be overcome for quantum computing to reach its full potential.
In October 2019, Google’s Californian research lab became the first to achieve “quantum supremacy”, performing a calculation that would be practically impossible for even the most powerful classical supercomputer. Google’s 53-qubit Sycamore processor performed a calculation in 200 seconds which would have taken the world’s most powerful supercomputer 10,000 years.
The University of Science and Technology of China achieved quantum supremacy only 14 months later, claiming its Jiuzhang quantum computer to be 10 billion times faster than Google’s.
What Challenges Lay Ahead?
While quantum supremacy is a major achievement, if quantum computing is a moonshot then quantum supremacy is only the equivalent of Yuri Gagarin’s first space flight. Many challenges still lie ahead and fully-fledged, fault-tolerant quantum computers may still be more than a decade away.
So far, quantum supremacy has only been achieved using computers and calculations especially designed to demonstrate quantum computing’s strengths, but not to solve real-world problems.
A key milestone will be to achieve “practical” quantum supremacy when tackling real-world challenges, says Professor Andrea Morello. Winner of the American Physical Society‘s inaugural Rolf Landauer and Charles H. Bennett Award in Quantum Computing, Morello leads one of the University of New South Wales’ quantum computing research teams in Sydney, Australia.
Practical quantum supremacy may still be a decade away, Morello says. It is difficult to predict which problem will be solved first, but one possibility is calculating a chemical reaction in order to synthesize a new pharmaceutical.
Achieving practical quantum supremacy will require error correction and fault tolerance, similar to traditional computers. Error correction proves challenging at the quantum level, where qubits are highly susceptible to interference and only remain stable for milliseconds, Morello says:
“Google’s quantum supremacy was achieved using ‘uncorrected’ qubit gates and, while this is impressive, error correction becomes important when you’re aiming for practical quantum supremacy so you can trust the outcome enough to apply it to the real world. Quantum error correction has been demonstrated in the laboratory and right now a lot of resources are being invested into bringing it to fruition.”
How Are Quantum Computers Used Today?
Summit supercomputer (Credit: Oak Ridge National Laboratory)
While progress continues towards practical quantum supremacy, intermediate quantum computers still offer an advantage over classical computers in certain optimized applications, says GlobalData graduate analyst Sam Holt.
“Fully-fledged, universal and fault-tolerant quantum computers may be more than a decade away, but a flurry of recent partnerships have explored use cases on intermediate devices. In January 2021, for example, Roche announced a collaboration with Cambridge Quantum Computing to develop quantum simulations for new drug discovery for Alzheimer’s disease.”
Roche employs noisy-intermediate-scale-quantum (NISQ) algorithms that lack error correction but are still useful for some tasks.
Another intermediate approach to quantum computing proposes installing low-qubit processors alongside traditional processors to act as “quantum accelerators”. This allows certain aspects of processing to benefit from the quantum advantage, similar to the way a CPU can hand off specific tasks to a dedicated graphics card.
Even once practical quantum supremacy is achieved, Holt says it is likely that businesses in a wide range of industries will choose to rent time on cloud-based quantum computers rather than invest in their own hardware.
“Quantum cloud offerings from companies such as IBM are enabling widespread quantum computing. Quantum computing’s primary applications are in simulation, optimization, linear algebra and factorisation. These capabilities are increasingly becoming key requirements across a wide array of industries. Companies in these fields that are not at least investigating how quantum may transform their business risk getting left behind.”
What Are the Applications for Quantum Computing?
Even when error correction and practical quantum supremacy are achievable, traditional computers will still be considerably smaller, cheaper and more practical for most calculations, Morello says:
“Using a quantum computer to solve most problems is like using a 747 to go to the supermarket. Just like a jumbo jet, quantum computing proves its worth when you need to do the heavy lifting.”
Chemistry is shaping up as quantum computing’s first killer application, potentially helping humanity address some of its greatest challenges. Today the production of ammonia, the main ingredient of fertilizer, requires high-temperature furnaces which consume 2% of the world’s energy and produce 1% of its CO2 output. Bacteria can produce ammonia at room temperature and quantum computing may be the key to understanding and replicating this process.
In manufacturing, quantum computing could be used to develop new chemicals, polymers, and alloys. Industrial manufacturing still struggles to duplicate many materials with astonishing properties which exist in nature, such as spider silk.
By weight, spider silk is comparable with steel when it comes to tensile strength, but silk is not forged in a furnace. Because spider silk is a protein made by DNA, quantum computing’s superior ability to model at a subatomic level may unlock the ability to manufacture similar materials in an eco-friendly way, Morello says:
“Quantum computing is a truly disruptive technology that can have gigantic value for science, for industry and for society. It’s such a genuinely transformational technology that the vast majority of its applications will be things we haven’t even thought of yet – quantum computing will help open up new worlds.”


Quantum computing is a theoretical computing model that uses a very different form of data handling to perform calculations. The emergence of quantum computing is based on a new kind of data unit that could be called non-binary, as it has more than two possible values. A traditional computer works on bits of data that are binary, or Boolean, with only two possible values: 0 or 1. In contrast, a quantum bit, or "qubit," has possible values of 1, 0 or a superposition of 1 and 0, in the case of an unknown value. According to scientists, qubits are based on physical atoms and molecular structures. However, many find it helpful to theorize a qubit as a binary data unit with superposition.
Quantum Computing Fundamentals
All computing systems rely on a fundamental ability to store and manipulate information. Current computers manipulate individual bits, which store information as binary 0 and 1 states. Quantum computers leverage quantum mechanical phenomena to manipulate information. To do this, they rely on quantum bits, or qubits.
Three quantum mechanical properties — superposition, entanglement, and interference — are used in quantum computing to manipulate the state of a qubit.
Superposition: Superposition refers to a combination of states we would ordinarily describe independently. To make a classical analogy, if you play two musical notes at once, what you will hear is a superposition of the two notes.
Entanglement: Entanglement is a famously counter-intuitive quantum phenomenon describing behavior we never see in the classical world. Entangled particles behave together as a system in ways that cannot be explained using classical logic.
Interference: Finally, quantum states can undergo interference due to a phenomenon known as phase. Quantum interference can be understood similarly to wave interference; when two waves are in phase, their amplitudes add, and when they are out of phase, their amplitudes cancel.
Quantum Computing Models
There are a number of quantum computing models, distinguished by the basic elements in which the computation is decomposed. The four main models of practical importance are:
- Quantum gate array (computation decomposed into a sequence of few-qubit quantum gates)
- One-way quantum computer (computation decomposed into a sequence of one-qubit measurements applied to a highly entangled initial state or cluster state)
- Adiabatic quantum computer, based on quantum annealing (computation decomposed into a slow continuous transformation of an initial Hamiltonian into a final Hamiltonian, whose ground states contain the solution)
- Topological quantum computer(computation decomposed into the braiding of anyons in a 2D lattice)
The quantum Turing machine is theoretically important but the direct implementation of this model is not pursued. All four models of computation have been shown to be equivalent; each can simulate the other with no more than polynomial overhead.
Quantum Computers Vs. Conventional Computers
Although people often assume that quantum computers must automatically be better than conventional ones, that's by no means certain. So far, just about the only thing we know for certain that a quantum computer could do better than a normal one is factorization: finding two unknown prime numbers that, when multiplied together, give a third, known number. In 1994, while working at Bell Laboratories, mathematician Peter Shor demonstrated an algorithm that a quantum computer could follow to find the "prime factors" of a large number, which would speed up the problem enormously. Shor's algorithm really excited interest in quantum computing because virtually every modern computer (and every secure, online shopping and banking website) uses public-key encryption technology based on the virtual impossibility of finding prime factors quickly (it is, in other words, essentially an "intractable" computer problem). If quantum computers could indeed factor large numbers quickly, today's online security could be rendered obsolete at a stroke. But what goes around comes around, and some researchers believe quantum technology will lead to much stronger forms of encryption. (In 2017, Chinese researchers demonstrated for the first time how quantum encryption could be used to make a very secure video call from Beijing to Vienna.)
Does that mean quantum computers are better than conventional ones? Not exactly. Apart from Shor's algorithm, and a search method called Grover's algorithm, hardly any other algorithms have been discovered that would be better performed by quantum methods. Given enough time and computing power, conventional computers should still be able to solve any problem that quantum computers could solve, eventually. In other words, it remains to be proven that quantum computers are generally superior to conventional ones, especially given the difficulties of actually building them. Who knows how conventional computers might advance in the next 50 years, potentially making the idea of quantum computers irrelevant—and even absurd.
History of Quantum Computing
Quantum computing tends to trace its roots back to a 1959 speech by Richard P. Feynman in which he spoke about the effects of miniaturization, including the idea of exploiting quantum effects to create more powerful computers. This speech is also generally considered the starting point of nanotechnology.
Of course, before the quantum effects of computing could be realized, scientists and engineers had to more fully develop the technology of traditional computers. This is why, for many years, there was little direct progress, nor even interest, in the idea of making Feynman's suggestions into reality.
In 1985, the idea of "quantum logic gates" was put forth by the University of Oxford's David Deutsch, as a means of harnessing the quantum realm inside a computer. In fact, Deutsch's paper on the subject showed that any physical process could be modeled by a quantum computer.
Nearly a decade later, in 1994, AT&T's Peter Shor devised an algorithm that could use only 6 qubits to perform some basic factorizations ... more cubits the more complex the numbers requiring factorization became, of course.
A handful of quantum computers has been built. The first, a 2-qubit quantum computer in 1998, could perform trivial calculations before losing decoherence after a few nanoseconds. In 2000, teams successfully built both a 4-qubit and a 7-qubit quantum computer. Research on the subject is still very active, although some physicists and engineers express concerns over the difficulties involved in upscaling these experiments to full-scale computing systems. Still, the success of these initial steps does show that the fundamental theory is sound.
Applications of Quantum Computing
Quantum computing could:
- Speed up the development of drugs; improve chemical industry manufacturing; desalinate seawater; and even suck carbon dioxide out of the atmosphere to curb climate change.
- Result in the invention of room temperature superconductors that would be impervious to power drain during electrical transmission.
- Handle problems of image and speech recognition, and provide real-time language translation.
- Greatly enhance big data processing from sensors, medical records and stock fluctuations.
- And generate many other similarly important applications not yet imaginable.
The Advantages and Disadvantage of Quantum Computing
Advantages of Quantum Computing
- The main advantage of quantum computing is it can execute any task very faster when compared to the classical computer, generally the atoms changes very faster in case of the traditional computing whereas in quantum computing it changes even more faster. But all the tasks can’t be done better by quantum computing when compared to traditional computer.
- In quantum computing qubit is the conventional superposition state and so there is an advantage of exponential speedup which is resulted by handle number of calculations.
- The other advantage of quantum computing is even classical algorithm calculations are also performed easily which is similar to the classical computer.
Disadvantages of Quantum Computing
- The main disadvantage of computing is the technology required to implement a quantum computer is not available at present. The reason for this is the consistent electron is damaged as soon as it is affected by its environment and that electron is very much essential for the functioning of quantum computers.
- The research for this problem is still continuing the effort applied to identify a solution for this problem has no positive progress.
Artificial Intelligence (AI)
Artificial Neural Network (ANN)
- Definition: What is Quantum Computing? Techopedia
- Quantum Computing Fundamentals IBM
- Quantum Computing Models Wikipedia
- What can quantum computers do that ordinary computers can't? Explain That Stuff
- History of Quantum Computing ThoughtCo
- Possible Applications of Quantum Computing OWDT
- The Advantages and Disadvantage of Quantum Computing 1000projects.org


Learn about parallel computing, the rise of heterogeneous processing (also known as hybrid processing), and the prospect of quantum engineering as a field of study!
Parallel computing used to be a way of sharing tasks between processor cores.
When processor clock rates stopped increasing, the response of the microprocessor companies was to increase the number of cores on a chip to increase throughput.
But now, the increased use of specialized processing elements has become more popular.
A GPU is a good example of this. A GPU is very different from an x86 or ARM processor and is tuned for a different type of processing.
GPUs are very good at matrix math and vector math. Originally, they were designed to process pixels. They use a lot of floating point math because the math behind how a pixel value is computed is very complex.
A GPU is very useful if you have a number of identical operations you have to calculate at the same time.
GPUs used to be external daughter cards, but in the last year or two the GPU manufacturers are starting to release low power parts suitable for embedded applications. They include several traditional cores and a GPU.
So, now you can build embedded systems that take advantage of machine learning algorithms that would have traditionally required too much processing power and too much thermal power.
This is an example of a heterogeneous processor (AMD) or hybrid processor. A heterogeneous processor contains cores of different types, and a software architect figures out which types of workloads are processed by which type of core.
Andrew Chen (professor) has predicted that this will increase in popularity because it’s become difficult to take advantage of shrinking the semiconductor feature size.
This year or next year, we will start to see heterogeneous processors (MOOR) with multiple types of cores.
Traditional processors are tuned for algorithms on integer and floating point operations where there isn’t an advantage to doing more than one thing at a time. The dependency chain is very linear.
A GPU is good at doing multiple computations at the same time so it can be useful when there aren’t tight dependency chains.
Neither processor is very good at doing real-time processing. If you have real time constraints – the latency between an ADC and the “answer” returned by the system must be short – there is a lot of computing required right now. So, a new type of digital hardware is required. Right now, ASICs and FPGAs tend to fill that gap, as we’ve discussed in the All about ASICs podcast.
Quantum cores (like we discussed in the what is quantum computing podcast) are something that we could see on processor boards at some point. Dedicated quantum computers that can exceed the performance of traditional computers will be introduced within the next 50 years, and as soon as the next 10 or 15 years.
To be a consumer product, a quantum computer would have to be a solid state device, but their existence is purely speculative at this point in time.
Quantum computing is reinventing how processing happens. And, quantum computers are going to tackle very different types of problems than conventional computers.
There is a catalog on the web of problems and algorithms that would be substantially better on a quantum on a computer than a traditional computer.
People are creating algorithms for computers that don’t even exist yet.
The Economist estimated that the total spend on quantum computing research is over 1 Billion dollars per year globally. A huge portion of that is generated by the promise of these algorithms and papers. The interest is driven by this.
Quantum computers will not completely replace typical processors.
Lee’s opinion is that the quantum computing industry is still very speculative, but the upsides are so great that neither the incumbent large computing companies nor the industrialized countries want to be left behind if it does take off.
The promise of quantum computing is beyond just the commercial industry, it’s international and inter-industry. You can find long whitepapers from all sorts of different governments laying out a quantum computing research strategy. There’s also a lot of venture capitalists investing in quantum computing.
Is this research and development public, or is there a lot of proprietary information out there? It’s a mixture, many of the startups and companies have software components that they are open sourcing and claim to have “bits of physics” working (quantum bits or qbits), but they are definitely keeping trade secrets.
19:50 Quantum communication means space lasers.
Engineering with quantum effects has promise as an industry. One can send photons with entangled states. The Chinese government has a satellite that can generate these photons and send them to base stations. If anyone reads them they can tell because the wave function collapsed too soon.
Quantum sensing promises to develop accelerometers and gyroscopes that are orders of magnitude more sensitive than what’s commercially available today.
Quantum engineering could become a new field. Much like electrical engineering was born 140 years ago, electronics was born roughly 70 years ago, computer science was born out of math and electrical engineering. It’s possible that the birth of quantum engineering will be considered to be some point in the next 5 years or last 5 years.
Lee’s favorite quantum state is the Bell state. It’s the equal probability state between 1 and 0, among other interesting properties. The Bell state encapsulates a lot of the quantum weirdness in one snippet of math.