In contrast to the human genome, which consists of 3 billion bases made of 4 nucleic acids organized in a one dimensional space, the human phenome contains an unknown number of elements with variation and dimensionality only partly understood. The scientific understanding of genes and genomic variation is restricted by a narrow range of methods to assess phenotypes allowing only certain anatomical and behavioral traits to be recorded, which is often done manually. Phenomics aims to get a more in-depth and unbiased assessment of phenotypic profiles at the whole organism level.
The field of phenomics recognizes a need for consensus on human- and machine-interpretable language to describe phenotypes. Lack of standardization and computability across phenotype data make sharing phenotypic data difficult and may result in missed opportunities for discoveries and a large amount of phenotype data are not publicly available. A computable format may involve the use of appropriate ontology terms for representing phenotypic descriptions in text or data sources.
In human phenomics, two of the aims are to understand how the environment makes people more or less susceptible to disease and to understand individual reactions to therapies. In 2003, at the time of completion of the Human Genome Project, the need for more precisely defined phenotypes and high-throughput systems to fully take advantage of genotyping studies was projected and the creation of an international Human Phenome Project was proposed. The theme for a satellite meeting at the 2012 Annual Meeting of the American Society of Human Genetics in San Francisco by The Human Variome Project was “Getting Ready for the Human Phenome Project”. The meeting was cosponsored by the Human Genome Variation Society. At that meeting it was noted that in comparison to phenome projects in model organisms such as mouse, rat and zebrafish which have compiled phenotypic data on the consequences of genetic mutations, similar scale efforts for humans lagged behind.
In humans, data on genotype-phenotype associations has been generated through genome-wide association studies (GWAS) or linking single nucleotide polymorphisms (SNPs) with disease phenotypes. To reduce the disease-centric bias in these approaches, an effort was made to look for associations in complete medical records and complete genome sequences, called Phenome Wide Association Studies (PheWAS). However human phenotype data has a strong clinical bias.
The introduction of genetic changes in animal model systems allow for unbiased interrogation of genotype-phenotype interactions. The Mouse Phenome Project was the first major effort in a vertebrate model to catalog baseline phenotypic data, which is housed at the Mouse Phenome Database at the Jackson Laboratory, Bar Harbor, ME. The Knockout Mouse Project is a National Institutes of Health (NIH) initiative which aims to generate a resource containing loss of function mutations for every gene in the mouse genome correlated with phenotypic data.
Most human genes (70%) have a counterpart (ortholog) in zebrafish, which combined with their short generation time, standard practises for genetic manipulation and suitability for live imaging make them cost-effective in biomedical research. The Zebrafish Phenome Project is underway and contribute to knowledge about phenotype-genotype associations and genetic diagnosis of human disease. The Chemical Phenomics Initiative, based on chemical genetics, is a high-throughput chemical screen for small molecules that modulate early embryonic development in zebrafish, carried out by the Hong lab at Vanderbilt University. Pharmacological targets for these small molecule developmental modulators are identified and made accessible to the scientific community through the chemotype-phenotype database on Chemical Phenomics interactive web portal. In zebrafish whole body micro-CT scanning has been used for skeletal phenomics studies.
As DNA sequencing became faster and cheaper, new knowledge about normal genetic variation in the human population allowed genetic variants once thought to cause disease to be reclassified as benign. The ranges and commonality of variations in human phenotypes if better understood could improve the accuracy and treatment of disease in genetic medicine. The Human Phenotype Ontology helps the sharing of phenotype data by standardising the vocabulary for phenotypes. For some types of phenotypic abnormalities, standardized measurements can be used to define the phenotype. To show a causal link between a genetic variant and an abnormal phenotype, it needs to be shown that the two are found together more often then expected by chance. Improvements in data about baseline population frequencies of phenotypes are needed for these calculations.
The International Human Phenome Project (Phase I) was launched in Shanghai in March 2018. The project will be led by Fudan University with collaboration with Shanghai Jiao Tong University, Shanghai Institute of Measurement and Testing Technology and Shanghai Institutes for Biological Sciences.
The following projects promote standardization and sharing of phenotypic data related to humans and model organisms:
- The Human Phenotype Ontology (HPO)
- The Human Variome Project
- PhenX Toolkit
- International Mouse Phenotyping Consortium (IMPC)
- National Phenome Centre
- International Phenome Centre Network
- UK Biobank
- The Personal Genome Project
- National Bio Resource Project (NBRP) Rat Phenome
- Inborn Errors of Metabolism Knowledgebase (IEMbase)
- Chemical Phenomics Initiative
- The Phenomics Discovery Intitiative (PDi)
- Consortium for Neuropsychiatric Phenomics (CNP)
- Mouse Phenome Database
- The Knockout Mouse Project
- Phenome Wide Association Studies (PheWAS)
- Chemical Phenomics Initiative
- Definiens (Tissue Phenomics Company)
- Plant Ontology
- Gene Ontology Consortium
Plant phenomics is used both to understand how crops respond to environmental changes and for crop improvement. Connections between plant genotype and phenotype were historically investigated by identifying a trait of interest and then using DNA markers and breeding to locate the gene responsible for the trait. Seeking the gene responsible for a phenotype is called a forward genetics approach. Reverse genetics approaches which mutate or alter genes first to find the phenotypic consequence of specific genetic changes became more commonly used with the development of mutagens, molecular genetics and bioinformatics. As the price of image data collection has gone down and the capability for computational image processing has increased, plant phenomics researchers are investigating relationships between genotype, phenotype and environment with satellite and drone images. One hurdle is in developing computational methods to extract useful information. Researchers at Iowa State University are using crowdsourcing to for image labeling used to train machine learning algorithms. The team used students and Amazon MTurkers for image labeling.
Researchers at University of Saskatchewan developed the open-source software platform, Deep Plant Phenomics, which uses deep convolutional neural networks for phenotyping plants. The platform was shown to be effective at leaf counting, mutant classification and age regression in top-down images of plant rosettes.
- National Plant Phenomics Centre (IBERS Gogerddan, Wales, UK)
- PHENOME, the French plant phenomic Infrastructure
- Australian Plant Phenomics Facility
- The European Infrastructure for Multi-scale Plant Phenomics and Simulation (EMPHASIS)
- International Plant Phenotyping Network
- North American Plant Phenotyping Network
- Qubit Phenomics
- Zegami Ltd
Computational approaches are being developed to gather, compare and process phenomics data. Machine learning methods are used for analysing images such as satellite images of plants, medical histology images and words describing medical conditions. For comparison of phenotypes across different organisms, formal ontologies are implemented that are accessible to automated reasoning. Phenotype ontologies are hierarchically-related phenotypic descriptions using controlled vocabulary that allows computation in individuals, populations and across multiple species. Ontologies are being developed in Web Ontology Language (OWL) and OBO Flatfile Format.
Documentaries, videos and podcasts
- Recursion PharmaceuticalsRecursion Pharmaceuticals is a biotechnology and data science company based in Salt Lake City, Utah, founded in 2013, that combines biology with artificial intelligence for drug discovery. Using human cell models of diseases, Recursion captures microscopic images to build biological datasets and computational techniques identify disease-associated.


Imagine a technology that could allow hackers to access everybody’s passwords, worldwide, in a matter of minutes; or with the right adjustments could create unbreakable encryption and information security. These are just a few potential consequences of breakthroughs in the field of quantum computing, which applies the unique laws of quantum mechanics to developing computers with remarkable capabilities.
Other benefits of quantum computing include speed and energy efficiency improvements, as well as increased computational capacity over current computers, potentially unlocking breakthroughs in fields from drug discovery to artificial intelligence, and space exploration to weather forecasting which were previously too complex for conventional computers .
To illustrate quantum computing, consider the following from Business Insider: “imagine you only have five minutes to find an “X” written on a page of a book in a library of 50 million books. It would be impossible. But if you were in 50 million parallel realities, and in each reality you could look through the pages of a different book, in one of those realities you would find the “X.” In this scenario a regular computer is you running around like a crazy person trying to look through as many books as possible in five minutes. A quantum computer is you split into 50 million yous, casually flipping through one book in each reality” .
Many physicists from Albert Einstein to Carl Sagan have agreed that the principles of quantum physics are so strange that they defy understanding. However, it is precisely these strange properties which are being harnessed to develop the next generation of computing.
Quantum computers are based on the physics of the small – the scale of individual electrons. At this scale, nature behaves differently than it does at our “human” scale. Examples include “superposition” (objects existing in multiple states simultaneously) and “entanglement” (intrinsically connected objects regardless of their distance apart), which can be manipulated to perform operations on data. Compared to modern digital computers that fundamentally store data in one of two states – known as “bits,” quantum bits or “qubits” can be in an infinite number of states at once .
With another breakthrough in quantum computing announced this week, the quantum computing revolution may be closer than many of us realize . Several companies have already launched various attempts to capitalize on this field, including Google, IBM and D-Wave [4,5,6].
D-Wave is the first company offering quantum computers, with basic versions having already been used by Google, Lockheed Martin, NASA and others. Founded in British Columbia, Canada in 1999, the company made a big bet on the development and feasibility of quantum computing technology. However, that bet paid off with its first functional quantum computer priced at $10 million, with a number of customers already engaged and further developments on the way [4,5,6].
Business and Organizational Model
D-Wave originally chose to outsource its research to other laboratories by funding research in exchange for rights to intellectual property . After securing the concept and design from 1999 to 2006 (D-Wave holds 100 US patents and over 60 scientific publications), the company embarked on engineering, commercialization and scale. The go-to-market model was based on joint collaboration with strategic customers in specific verticals including defense, web 2.0 and energy. The company intends to achieve a sustainable model by focusing on long-term growth and building multi-year relationships with customers, which includes professional and maintenance services, and offering multi-year subscription contracts to clients .
- Publicity. Scientists were critical of the early D-Wave computers, arguing that they were not actually quantum machines. Though D-Wave since disproved these claims, maintaining commercial momentum will require positive publicity to bolster their brand name and avoid any perception of deceit. This is especially important since their product is based on complicated physics which could lend itself to a lack of trust by consumers in the nascent stages of commercialization [6,9].
- Partnerships. D-Wave has already partnered with corporations, laboratories, universities and governments to foster implementation of its product, however it should further invest in this arena as these partnerships will fuel early adoption of this technology. Without aggressively pursuing these partnerships, the company also risks losing market share to competitors. Moreover, if D-Wave is able to expand its user-base, it will foster a sort of competition built around its product; organizations will not want to be left behind with outdated computers [6,9].
- New Research. D-Wave must continue to source capital and continue to innovate, as competitors will be motivated to enter the market with their own breakthroughs in quantum computing.
- Regulation. As a new technology, D-Wave is susceptible to new laws focused at its technology. If unforeseen repercussions arise from quantum computing, D-Wave will likely be the first organization affected. The company must be forward-thinking and take proactive strategic measures, such as working with regulators and exploring challenges.
 Dickerson, Kelly, “7 awesome ways quantum computers will change the world.” Business Insider. Web. 18 Nov. 2016. http://www.businessinsider.com/quantum-computers-will-change-the-world-2015-4
 “Quantum computing 101” University of Waterloo. Web. 18 Nov. 2016. https://uwaterloo.ca/institute-for-quantum-computing/quantum-computing-101
 Ranger, Steve, “Researchers claim quantum computing breakthrough, explain it using beer.” ZDNet. Web. 18 Nov. 2016. http://www.zdnet.com/article/researchers-claim-quantum-computing-breakthrough-explain-it-using-beer
 “Quantum A.I.” Research at Google. Web. 18 Nov. 2016. http://research.google.com/pubs/QuantumAI.html
 “A New Way of Thinking: The IBM Quantum Experience.” IBM Quantum Computing. Web. 18 Nov. 2016. http://www.research.ibm.com/quantum/
 D-Wave Systems Inc. Website. Web. 18 Nov. 2016. http://www.dwavesys.com/
 MacCormack, Alan D., Ajay Agrawal, and Rebecca Henderson. “D-Wave Systems: Building a Quantum Computer.” Harvard Business School Case 604-073, April 2004.
 “D-Wave Overview.” Web. 18 Nov. 2016. http://www.dwavesys.com/sites/default/files/D-Wave-Investor%20Presentation-Web100814-2.pdf
 Shah, Agam. “D-Wave will ship a 2,000-qubit quantum computer next year.” PC World. Web. 18 Nov. 2016. http://www.pcworld.com/article/3122452/hardware/d-wave-will-ship-a-2000-qubit-quantum-computer-next-year.html
Photo credit: http://quantumhealthjournal.com/ (Accessed Nov. 18) Quantum mechanics helps describes discrete locations and objects as spectra of probabilities, from which novel computing principles can arise. This picture represents probability distributions of electron locations around their atomic nucleus – the building block of quantum computing.


Towards the end of 2001, IBM made a breakthrough – they proved that three times five is fifteen. Not a big deal, one would say. But they haven’t heard the whole story yet: IBM did it using only seven atoms. That’s right – seven atoms. Not a full-fledged computer with a chip and input mechanism and trillions of atoms making all the components up. Only seven atoms did the trick. That, in short, is Quantum Computing – harnessing the power of nature.
Moore’s Law coming to an end
Moore’s famous law – computing power doubles every eighteen months. So by that logic, computers of 2050 will be able to process data at the speed of human thought – five hundred trillion bytes per second. This is enough to build machines with AI comparable to human intelligence. But there’s only one problem – Moore’s law won’t stand up till 2050. By 2020, we will have probably expanded it. There are only so many transistors we can fit on a chip. Eventually the distance between them will be too small for us to make any further improvements.
So where do we go once Moore’s Law comes to an end? IBM has recently been showing off Graphene chips. Remember that Moore’s Law corresponds to the use of Silicon. Graphene is a special form of Graphite that consists of a single layer of carbon atoms. IBM managed to reach speeds of 100 GHz using it last year.
Graphene chips are out?
But then last week, IBM dismissed the idea of replacing silicon in computers with graphene. They said that because of its small width, graphene does not have a power gap and hence cannot be completely switched off. So while it works well in a laboratory, it is unlikely to replace the transistors in a CPU. While they may complement silicon in hybrid circuits such as RF circuits, hopes of using them to continue Moore’s Law have been well and truly dashed.
What do we need Quantum Computing?
Everything mentioned above leads us to this – Quantum Computing. When Moore’s law comes to an end, we will no longer be worried about improving silicon. Hopefully, by then we will have moved onto something much better – using atoms for computing.
Suppose you want to simulate the behavior of a handful of atoms. With today’s technology, you would need nothing short of a super computer and a couple of days to perform the simulation. Yet in real life, nature can simulate their behavior using just that handful of atoms. That is something we plan to achieve using Quantum Computing.
Quantum Computing will increase computing power not linearly, but exponentially.
Why would we need so much power? For everything! Imagine having the power of the current breed of super computers on your desktop.
“But will it run Crysis?”
Oh! Yes it will! It will run thousands of instances of Crysis at the same time, if you so wish. But desktop implementation is far away.
Think of the super computers locked away in laboratories. If we replace the silicon in them, imagine how much better weather prediction will get. So will tracing the motion of stars and planets, predicting future natural disasters, simulation of events like earthquakes, etc.
Things that require days to compute today (like DNA sequences) will be completed in a matter of minutes.
Just like we didn’t know what computers would be capable of when we first invented them, we don’t know today what we might use Quantum computers in the future for. We might use them to create virtual worlds like the Matrix (imagine how you would be playing Second Life then!). We might use them to create accurate simulations. We might use them for mind reading. We might even use them for teleportation! Because we haven’t truly harnessed the power of Quantum Computing yet, we don’t know what it is capable of.
All the uses are not without potential security issues, however. When IBM computer 3*5 using seven atoms, even the CIA took note. If and when quantum computers become a reality, any code the CIA makes will be cracked in a matter of minutes, making password protection and encryption obsolete.
Fortunately, the same computers that can be used to break codes can also be used to make codes. However, this still wouldn’t work unless Quantum Computing made its way into homes. Brute force attacks will become so simple that we will have to completely rethink security. Passwords will no longer work.
This article from October 2009 outlines the current state of technology. While the technology is exciting, it is so complex that we don’t yet fully understand how it works. As a result, we are not sure how to extract the most out of it. However, with a major focus on the area today, scientists are figuring out quantum computing at a much faster pace than ever before.
A few days back, an international team of scientists created 10 billion qubits on silicon at once. A qubit is an entangled pair of atoms, such that changing the state of one instantly affects the state of another, no matter how separated they are in space.
One of the biggest problems with Quantum Computing, however, is getting the results out. The calculations are performed and stored in qubits. How do you extract the solutions from there? For this, we currently have to go back to our old methods of using silicon, eating up the time saved by using Quantum Computing in the first place.
Another huge problem is that they are easily affected by external disturbances. Even cosmic radiation can throw your calculations off track, since they can cause atoms to vibrate strangely. How to isolate quantum computers from noisiness is a big issue. Isolation is also needed because quantum information has a tendency to leak into the outside environment.
As things stand right now, Quantum Computing is still a far distance into the future. We might be lucky if we get to use them in our life time. But no matter how long it takes, one thing’s for sure- Quantum Computing is well on its way. And it is going to change the world forever.


Though the concept of the robot seems to be a modern and a relatively new idea, they have been around for years. The first recording in literature of a possible description of the robot is found in the Iliad in reference to a “a three-legged cauldron that had ears for handles”. Later on, in 1900, we were introduced to Tik-Tok in Frank Baum's Wizard of Oz. The word robot was first used in 1920 by the Czech writer Karel Čapek in his play R.U.R. (Rossum's Universal Robots). This would be the first dramatization of a robot under this name. However, robots would come to life and be used for practical purposes in 1962. General Motors was the first company to use a robot for industrial purposes.
Since then, robots have been used in many ways. They have come in all shapes and sizes. They have been used in the medical field, the armed forces, and in the space program.
Now as we face the 21st century, technology evolves more. A new kind of robot is being studied and researched. This robot is called the quantum robot.
The quantum robot is the idea of combining quantum theory with robot technology. In other words, it is a practical use of the combination of quantum computing and robot technology. Quantum computing involves using quantum systems and quantum states to do computations.
A robot is an automated machine that is capable of doing a set of complex tasks. In some applications of robots, the programming used to run the robots may be based on artificial intelligence. Artificial Intelligence is the ability of a computer system to operate in a manner similar to human intelligence. Think of artificial intelligence as if you were training a machine to act like a human. Essentially, quantum robots are complex quantum systems.They are mobile systems with on board quantum computers that interact with their environments. Several programs would be involved in the operation of the robot. These programs would be quantum searching algorithms and quantum reinforcement learning algorithms.
Quantum reinforcement learning is based on superposition of the quantum state and quantum parallelism. A quantum state is a system that is a set of quantum numbers. The four basic quantum numbers represent the energy level, angular momentum, spin, and magnetization. In the superposition of quantum states, the idea is to get one state to look like another.
Let's say I have two dogs. One dog knows how to fetch a bone (energy level), sit up (angular momentum), give a high five (spin), and shake hands (magnetization). Now, let's apply the superposition of quantum states. Since one dog has been trained and given the commands, the other dog must learn to mimic or copy what the first dog did. Each time a command is achieved, reinforcement is given. The reinforcement for the dog would be a bone (or no bone if the command is not achieved).
In quantum reinforcement learning, it is slightly different. The idea would be similar to an “If-Then” statement. An example would be if the quantum state has a certain energy level, then the angular momentum is certain value. This idea of “If-Then” statements in the quantum world leads to an idea which can be a topic of its own; Quantum Logic.
Quantum parallelism simply means that computations can happen at the same time. This allows for all of the quantum numbers of the quantum system to be measured at the same time. If there are multiple quantum systems then; by using the concept of parallelism, all systems can be measured at the same time.
Programs used for “quantum searching” are based on quantum random walks. Quantum random walks use probability amplitudes. A probability amplitude allows us to determine that there is more than one possible quantum state. In the classical world, if you type a word “Quantum” in the search engine, you get many results. You may have a tough time finding a needle in a haystack if you use just one word, but if you want to refine your search; let's say “Quantum Random Walks”, then it narrows the search. The same principle applies in quantum computing to get more refined results. However, you are not necessarily searching for words but you are finding information that may correlate to a quantum state.
What would be the advantages of the Quantum Robot over the Robot?
Quantum robots are more intricate in examining their environments and doing tasks as they apply quantum effects . Because of the complexity in quantum computing, the expectations of the quantum robots would be that they are faster, more accurate, and are able to multitask better than the standard robot.
The quantum robots may be able one day to give us better medical diagnoses and better data interpretation in other research fields such as defense research. In medicine, they may be able to detect pathological changes in the body by being injected through the bloodstream. In the space program, they may be able to examine the delicate environments on other planets. In the military, they may be able to detect changes in the magnetic and electric fields. They may be able to help us detect early warnings of disasters more efficiently.


First, some background:
- Quantum computers use qubits instead of bits. Classical computers use electrical or optical pulses that represent zeros and ones while quantum computers typically use subatomic particles such as electrons or photons. Different quantum computers use different strategies to create and manage qubits.
- Quantum computers harness the principles of quantum mechanics such as superpositions (where qubits can represent different combinations of 0 and 1 simultaneously) and entanglement (where the state of one qubit instantaneously affects another) to perform tasks faster than classical computers.
- While quantum computers will have applications in many fields from materials science to pharmaceuticals research, they will probably not totally replace classical computers (Giles).
- Currently, complex mathematical formulas are used to encrypt and decrypt data.
- Symmetric cryptography uses the same key for both encryption and decryption. Asymmetric, or public-key, cryptography uses two mathematically linked keys, “one shared publicly to let people encrypt messages for the key pair’s owner, and the other stored privately by the owner to decrypt messages.” (Denning)
- While symmetric cryptography is much faster and is thus used for communications and stored data, public-key cryptography is used for exchanging symmetric keys and digital authentication. Because almost all internet applications use a combination of the two, everything needs to be secure. (Denning)
- Quantum computers could break symmetric cryptography by simply trying all possible keys. While they would be much faster than classical computers and thus be able to realistically break keys, making keys longer would be a easy solution.
- Quantum computers pose a great threat to public-key cryptography.
- “The algorithms for public-key encryption that are popular today—which are called RSA, Diffie-Hellman, and elliptic curve—all make it possible to start with a public key and mathematically compute the private key without trying all the possibilities.” (Denning)
- Public-key cryptography is currently uncrackable when very long key pairs are used. Both classical and quantum computers don’t have the ability to factor large enough numbers or perform advanced math quickly enough to crack them. However, in the future, a sufficiently advanced quantum computer could easily break public-key encryption using a quantum computer. (Denning)
- There are options for new secure methods: In 2016 the U.S. National Institute of Standards and Technology evaluated 69 potential new methods for post–quantum cryptography, which has since been reduced to 26. Unfortunately, it will likely be years before any draft standards are published. (Giles)
- Supersingular isogeny key exchange
- Lattice-based cryptography is “relatively simple, efficient, and highly parallelizable.” Although the security of lattice-based systems has been proven to be secure in difficult scenarios, it is difficult to say for sure how secure it is. (Chen)
- Code-based cryptography includes all cryptosystems, symmetric or asymmetric, whose security relies, partially or totally, on the hardness of decoding in a linear error correcting code. (Sendrier)
- Multivariate polynomial cryptography is “based on the difficulty of solving systems of multivariate polynomials over finite fields.” (Chen)
- Hash-based signatures use hash functions. Although there are drawbacks, “their security, even against quantum attacks, is well understood.” (Chen)
- There are many other options being explored (Chen).
This all seems rather dire (and complicated). What should the response be?
What U.S. Governments and Corporations Should Do:
Game Theory shows that allied governments and corporations developing quantum technologies should collaborate. For example, Sara Bjerg Moller, a professor of International Relations, writes that one of NATO’s goals should be countering China (Moller). One good way to achieve this goal would be to work together to make sure China does not develop a sufficiently advanced quantum computer first. Another example of the importance of collaboration is U.S corporations. Although Google, IBM, Microsoft, and others, are all competing, it is in all of the corporations best interest to make sure a malicious group does not get there first, so that customers’ data is not compromised. The idea of collaboration to implement the post-quantum cryptography system is also really important because everyone will benefit from security. Sadly, governments and corporations being what they are, collaboration is unlikely.
What Researchers Should Do:
Unfortunately, game theory does not show if while picking the best and most efficient post-quantum cryptography technique is important, the highest priority should be implementing a workable system quickly. One of the interesting aspects of game theory is it does not always have an answer.
What You Can Do:
A lot of these ideas aren’t in the public’s conscious yet. Learn more! Talk to people you know! Ask your representatives and governments what they are doing to prepare. If this interests you, both cybersecurity and quantum computing are quickly growing fields that will need more researchers! My works cited page is a great place to find more resources.
I welcome feedback, thoughts, and questions in the comments!
Still Interested? Check out works cited for more info.


Quantum dots are tiny particles or nanocrystals of a semiconducting material with diameters in the range of 2-10 nanometers (10-50 atoms). They were first discovered in 1980. 1 They display unique electronic properties, intermediate between those of bulk semiconductors and discrete molecules, that are partly the result of the unusually high surface-to-volume ratios for these particles.2-4 The most apparent result of this is fluorescence, wherein the nanocrystals can produce distinctive colors determined by the size of the particles.
Due to their small size, the electrons in these particles are confined in a small space (quantum box), and when the radii of the semiconductor nanocrystal is smaller than the exciton Bohr radius (exciton Bohr radius is the average distance between the electron in the conduction band and the hole it leaves behind in the valence band), there is quantization of the energy levels according to Pauli’s exclusion principle (Figure 1)5,6. The discrete, quantized energy levels of these quantum particles relate them more closely to atoms than bulk materials and have resulted in them being nicknamed 'artificial atoms. Generally, as the size of the crystal decreases, the difference in energy between the highest valence band and the lowest conduction band increases. More energy is then needed to excite the dot, and concurrently, more energy is released when the crystal returns to its ground state, resulting in a color shift from red to blue in the emitted light. As a result of this phenomenon, these nanomaterials can emit any color of light from the same material simply by changing the dot size. Additionally, because of the high level of control possible over the size of the nanocrystals produced, these semiconducting structures can be tuned during manufacturing to emit any color of light.7
Quantum dots can be classified into different types based on their composition and structure.
Figure 1.Splitting of energy levels in quantum dots due to the quantum confinement effect, semiconductor band gap increases with decrease in size of the nanocrystal.
These nano dots can be single component materials with uniform internal compositions, such as chalcogenides (selenides, sulfides or tellurides) of metals like cadmium, lead or zinc, example, CdTe (Product No. 777951) or PbS (Product No. 747017). The photo- and electroluminescence properties of core-type nanocrystals can be fine-tuned by simply changing the crystallite size.
The luminescent properties of quantum dots arise from recombination of electron-hole pairs (exciton decay) through radiative pathways. However, the exciton decay can also occur through nonradiative methods, reducing the fluorescence quantum yield. One of the methods used to improve efficiency and brightness of semiconductor nanocrystals is growing shells of another higher band gap semiconducting material around them. These particles with small regions of one material embedded in another with a wider band gap are known as core-shell quantum dots (CSQDs) or core-shell semiconducting nanocrystals (CSSNCs). For example, quantum dots with CdSe in the core and ZnS in the shell (Product Nos. 748056, 790192) available from Sigma-Aldrich Materials Science exhibit greater than 50% quantum yield. Coating quantum dots with shells improves quantum yield by passivizing nonradiative recombination sites and also makes them more robust to processing conditions for various applications. This method has been widely explored as a way to adjust the photophysical properties of quantum dots.8-10
The ability to tune optical and electronic properties by changing the crystallite size has become a hallmark of quantum dots. However, tuning the properties by changing the crystallite size could cause problems in many applications with size restrictions. Multicomponent dots offer an alternative method to tune properties without changing crystallite size. Alloyed semiconductor nanodots with both homogeneous and gradient internal structures allow tuning of the optical and electronic properties by merely changing the composition and internal structure without changing the crystallite size. For example, alloyed quantum dots of the compositions CdSxSe1-x/ZnS of 6nm diameter emits light of different wavelengths by just changing the composition (Product Nos. 753742, 753793) (Figure 2). Alloyed semiconductor quantum dots formed by alloying together two semiconductors with different band gap energies exhibited interesting properties distinct not only from the properties of their bulk counterparts but also from those of their parent semiconductors. Thus, alloyed nanocrystals possess novel and additional composition-tunable properties aside from the properties that emerge due to quantum confinement effects.11
Figure 2.Photoluminescence of alloyed CdSxSe1-x/ZnS quantum dots of 6 nm diameter. The material emits different color of light by tuning the composition.
The unique size and composition tunable electronic property of these very small, semiconducting quantum dots make them very appealing for a variety of applications and new technologies.12
Quantum dots are particularly significant for optical applications owing to their bright, pure colors along with their ability to emit rainbow of colors coupled with their high efficiencies, longer lifetimes and high extinction coefficient. Examples include LEDs and solid state lighting, displays and photovoltaics.7,13,14
Being zero dimensional, quantum dots have a sharper density of states than higher-dimensional structures. Their small size also means that electrons do not have to travel as far as with larger particles, thus electronic devices can operate faster. Examples of applications taking advantage of these unique electronic properties include transistors, solar cells, ultrafast all-optical switches and logic gates, and quantum computing, among many others.13-15
The small size of dots allow them to go anywhere in the body making them suitable for different bio-medical applications like medical imaging, biosensors, etc. At present, fluorescence based biosensors depend on organic dyes with a broad spectral width, which limits their effectiveness to a small number of colors and shorter lifetimes to tag the agents. On the other hand, quantum dots can emit the whole spectrum, are brighter and have little degradation over time thus proving them superior to traditional organic dyes used in biomedical applications.16


Hold it right there: how (and why) to stop light in its tracks
We are taught in school that the speed of light is a universal constant. Yet we also know that light travels more slowly through materials such as water and glass. Recently, we have even discovered that light can actually be made to stand completely still.
In fact, it was first done a long time ago ... in a galaxy far, far away. In a scene from the latest Star Wars film, Kylo Ren stops a blaster pulse using The Force. The pulse is frozen, shimmering in mid-air. More recently, for our paper published in Nature Physics this week, we stopped a pulse of laser light using a rather different method, by trapping it in a cloud of cold rubidium atoms.
Rubidium and other similar atoms have been used previously to slow down and store light and even to trap it. These systems all work by absorbing and re-emitting laser light from the atoms in a controlled way.
But we found a new way to trap light, by using the light to write a particular "shape" into the atoms. When the light was re-emitted, it became trapped in the atoms. It turned out that once we had picked the right directions and frequencies for our lasers, the experiment was pretty straightforward. The hard part was figuring out the right frequencies and directions!
Why do this? We are interested in trapping light because our ultimate goal is to make individual light particles, or photons, interact with one another. By interacting directly, the photons will become entangled. By scaling this up to many interactions, involving many photons, we could theoretically create the intricate states of information necessary for powerful quantum computing.
Unfortunately, photons interact incredibly weakly with each other, but they can interact more strongly if they can be confined in a particular material long enough to enhance the interaction to a more useful level. In fact, these sorts of interactions have recently been demonstrated by multiple research groups around the world, often by using atom clouds to confine the light. But, as I'll explain below, our new stationary light system may have advantages when it comes to getting photons to interact.
Quantum computing is an exciting and rapidly evolving field of research, and our team is part of the Australian Research Council's Centre for Quantum Computation and Communication Technology. There are many different potential platforms for quantum computing. For example, the centre's UNSW team has demonstrated quantum computing operations using phosphorus atoms embedded in silicon chips.
But our group mainly studies light, not least because it is very likely that light will play some role in quantum computers. It offers a convenient way to send quantum information within or between computers because, unlike atoms or electric currents, it is not vulnerable to stray magnetic or electric fields. It may even be possible to perform quantum computation using light, and this is the idea that motivates our research into stationary light.
Our team has been able to store and retrieve pulses of light in the same system. We have also been able to show that quantum information encoded in these light pulses is preserved - meaning that it can form the basis of computing memory.
However, this is not sufficient to generate the sort of interaction we want, because the light is entirely absorbed into the atoms and it can no longer interact. Instead, we need to trap light in the memory, not just store it.
While researching how to trap light in the atomic memory, I discovered using a computer simulation that a particular kind of shape written into the atomic memory would produce stationary light. By retrieving the light in two directions at once, the light could actually be trapped in the memory. All the light being re-emitted throughout the memory would destructively interfere at the ends of the memory and not escape.
The simulations also predicted other interesting behaviour: if the wrong shape was written, some light would escape, but the memory would rapidly evolve to a shape where the light is trapped. This could be useful for stationary light by making it more robust, but it may also be useful for other optical processing.
We were able to demonstrate all of this behaviour experimentally using our atomic memory. Unlike Kylo Ren's frozen blaster pulse, it was not possible to see the stationary light directly (to see something, photons have to travel from the object to your eyes, and these photons were not going anywhere). Instead, the fact the behaviour of the system matched our predictions so precisely confirmed that the light was indeed stationary.
Light has previously been trapped in a similar system. What makes our system new and interesting is that we believe it is the most convincing demonstration so far, but also that the behaviour of our stationary light is radically different. We believe that this new behaviour, where the light travels more freely through the memory, could allow for stronger nonlinear interactions.
This experiment is only a single step on the long road to optical quantum computing. The next step will be to prove that photons can indeed interact with one another within our system. Looking much further down the road, we hope this will give rise to a device that can use some of our discoveries, among many others, to generate the intricate states of many entangled photons necessary for an optical quantum computer.


Nothing is perfect, not even the indifferent, calculating bits that make up the basis of computers. But for the first time, a collaborative team that includes University of Maryland (UMD) scientists has shown that an assembly of quantum computing pieces can be better than the shakiest individual components used to build it.
The research team that includes Christopher Monroe, a fellow of the Joint Quantum Institute and a College Park Professor of Physics, together with other UMD researchers and colleagues from Duke University, shared how they took this revolutionary step toward dependable, practical quantum computers in a paper published recently in the journal Nature.
In their experiment, the team joined together several qubits — the quantum version of bits that encrypt data in standard computers as zeros and ones — to work together as one unit. This “logical qubit” is established on a quantum error correction code that can spot and rectify an error that happens in one of the 13 individual qubits that make up the logical qubit “team.”
Furthermore, the design of the logical qubit is fault-tolerant — that is, can contain errors to diminish their negative effects.
The demonstration strengthens the great potential of quantum computers, which are supposedly capable of functioning beyond the range of standard or “classical” computers, in part because qubits are much more versatile than standard bits, and not restricted to merely being zero or one.
Yet, quantum faults have long hindered the effort to expand these futuristic technologies to superior levels of power; unlike transistors that encode data in standard computer chips, a qubit is vulnerable to errors from minute environmental disturbances like a temperature change or vibration that ruins its quantum state.
A group of qubits that function in unison can help surpass such limits, however, said Monroe. Monroe is also co-founder and chief scientist at IonQ, a quantum company in College Park that is established partly on the technology he created as a UMD scientist.
Qubits composed of identical atomic ions are natively very clean by themselves. However, at some point, when many qubits and operations are required, errors must be reduced further, and it is simpler to add more qubits and encode information differently. The beauty of error correction codes for atomic ions is they can be very efficient and can be flexibly switched on through software controls.
Christopher Monroe, Fellow, Joint Quantum Institute
For the first time, a logical qubit has been demonstrated to be more dependable than the most error-prone step necessary to make it. The experiment showed that the team could verify that it correctly formed the logical qubit in a preferred quantum state 99.4% of the time, compared to the estimated 98.9% success rate of the six quantum processes (known as quantum operations) that they employed to create it.
That may not seem like a significant difference, but it is a vital step in the quest to design much bigger quantum computers.
If the six quantum operations were assembly line workers, each concentrated on one task, the joint error rate of the workers would result in the line only creating beneficial products 93.6% of the time, much lower than the 99.4% efficacy rate when the “workers” cooperate to reduce the chance of quantum errors compounding and tarnishing the outcome.
Although it may appear wasteful to employ so many separate qubits and steps simply to make something work as a single qubit, the distinctive computational functions of quantum computers could make logical qubits a minor cost to bear.
If quantum computers can be made reliable, they will be robust tools capable of computations projected to transform sectors including security, healthcare and finance.
The results were realized using Monroe’s ion-trap system at UMD, which employs up to 32 individual charged atoms — ions — that are cooled with lasers and suspended over electrodes on a chip. The ions can then be employed as qubits through additional laser tweaks.
We have 32 laser beams. And the atoms are like ducks in a row; each with its own fully controllable laser beam. I think of it like the atoms form a linear string and we're plucking it like a guitar string. We're plucking it with lasers that we turn on and off in a programmable way. And that's the computer; that's our central processing unit.
Christopher Monroe, Fellow, Joint Quantum Institute
By effectively developing a fault-tolerant logical qubit with this system, the scientists have demonstrated that meticulous, creative designs have the potential to unleash quantum computing from the limitation of the unavoidable errors of the existing state of the art.
What's amazing about fault tolerance, is it's a recipe for how to take small unreliable parts and turn them into a very reliable device. And fault-tolerant quantum error correction will enable us to make very reliable quantum computers from faulty quantum parts.
Kenneth Brown, Study Co-Author and Professor of Electrical and Computer Engineering, Duke University
Besides Monroe and Brown, the paper’s co-authors are JQI graduate student Laird Egan; JQI graduate students Andrew Risinger, Daiwei Zhu, and Debopriyo Biswas; JQI research scientist Marko Cetina; Duke postdoctoral researchers Crystal Noel and Michael Newman; Duke University physics graduate student Dripto M. Debroy; and Georgia Institute of Technology graduate student Muyuan Li.


Supercomputers have a high level of computing performance compared to a general purpose computer. In this post, we cover all details of supercomputers like history, performance, application etc. We will also see top 3 supercomputers and the National Supercomputing Mission.
What is a supercomputer?
- A computer with a high level of computing performance compared to a general purpose computer and performance measured in FLOPS (floating point operations per second).
- Great speed and great memory are the two prerequisites of a super computer.
- The performance is generally evaluated in petaflops (1 followed by 15 zeros).
- Memory is averaged around 250000 times of the normal computer we use on a daily basis.
- Housed in large clean rooms with high air flow to permit cooling.
- Used to solve problems that are too complex and huge for standard computers.
History of Supercomputers in the World
- Most of the computers on the market today are smarter and faster than the very first supercomputers and hopefully, today’s supercomputer would turn into future computers by repeating the history of innovation.
- The first supercomputer was built in 1957 for the United States Department of Defense by Seymour Cray in Control Data Corporation (CDC) in 1957.
- CDC 1604 was one of the first computers to replace vacuum tubes with transistors.
- In 1964, Cray’s CDC 6600 replaced Stretch as the fastest computer on earth with 3 million floating-point operations per second (FLOPS).
- The term supercomputer was coined to describe CDC 6600.
- Earlier supercomputers used to have very few processors but as the technology evolved and vector processing was turned into parallel processing, use of processors multiplied manifold resulting into supra fast supercomputers of the current decade.
History of Supercomputer in India
- As the saying goes “need is the mother of all inventions”, India started its journey towards supercomputers because it was denied the import of Cray supercomputers from the United States of America due to arms embargo imposed on India after Nuclear tests in the 1970s.
- They were of the opinion that India might use the same for the development of military rather than civilian purposes since supercomputers came under dual-use technology group.
- Ideation phase was started in the 1980s.
- The first indigenous supercomputer was developed indigenously in 1991 by Centre for Development of Advanced Computing which was called as PARAM 8000.
- Russian assistant in the development was paramount.
- PARAM 8000 was replicated and installed at ICAD Moscow in 1991 under Russian collaboration.
- In 2007, India held top 10 spots for speeds of supercomputers.
- As of July 2016, India has 9 supercomputers with speeds in top 500 but not any in top 10.
How powerful are supercomputers as compared to a computer?
- The performance of ordinary computers is generally quoted in MIPS (million instructions per second).
- MIPS is about the fundamental programming commands (read, write, store, and so on) the processor can manage.
- Therefore computers are compared based on the number of MIPS they can handle which is typically rated in gigahertz as the processor speed.
- Supercomputers are rated a different way because they are dealing with the scientific calculations.
- They are measured according to how many floating point operations per second (FLOPS) they can do.
- Since supercomputers were first developed, their performance has been measured in successively greater numbers of FLOPS, as the table below illustrates:
World’s top 3 supercomputers
- Sunway TaihuLight – developed in China with the computing power of a 93 petaflop/s.
- The Tianhe-2 (MilkyWay-2) – from China. This supercomputer is capable of 33.8 petaflop/s.
- Titan – from the US. Computing capacity is 17.5 petaflop/s.
What is the next generation supercomputing?
- Optical computing calculations with the near speed of light by using optical devices and connections in place of transistors. Latest developments in this field have already taken place with the optical equivalent of transistors being switched on using photons and not electrons. Since photons travel at speed of light, therefore, calculations may be done at sub-light speed.
- DNA computing calculations by recombining DNA in a parallel environment. Numerous possibilities are tried at the same time; the most optimal solution will be “the strongest to survive.”
- Quantum computing not in practical use yet only conceptual proofing done but think of it as calculations being done before you have thought of them. Work is done in the blink of an eye since time is of no essence here.
What are the Applications of a Supercomputer?
- Academic research: For observing and simulating the phenomena which are too big, too small, too fast, or too slow to observe in laboratories. For example, astrophysicists use supercomputers as “time machines” to explore the past and the future of our universe. Another important area is quantum mechanics.
- Weather and climate modeling to forecast with better accuracy by analyzing multiple factors and their interrelationships.
- Medicine discovery for e.g. How a protein folds information leads to the discovery of new drugs.
- Monsoon Forecasting using dynamic Models.
- Big data mining to strengthen and better mobilization of digital India mission.
- Oil and gas exploration, therefore, ensuring energy security of India.
- Airplane and spacecraft aerodynamics research and development, therefore better safety standards and smoother connectivity thereby helping in ease of transportation.
- Simulation of nuclear fission and fusion processes, therefore imparting better nuclear infrastructure models and helping in energy security of the nation.
- Molecular dynamics: supercomputer simulations allow scientists to dock two molecules together to study their interaction which may lead to the development of innovative materials for future generation technologies.
- In 1994, A supercomputer was used to alert the scientists about the collision of a comet with Jupiter, providing them time to prepare to observe and record the event for useful analysis and its application in predicting future comet collision with the earth.
What are the initiatives taken by the Government of India?
- In the 12th five-year plan, the government of India (GOI) had committed that $2.5bn would be sanctioned for the research in the supercomputing field.
- In 2015, GOI approved 7-year supercomputing program known as National Supercomputing Mission which aims to create a cluster of 73 supercomputers connecting various academic and research institutions across India with $730mn investment.
Some facts for Prelims
- There are no exaflop (higher than petaflops) computing supercomputers in the world and the first product is expected around 2019-20.
- India is also preparing to launch its exaflop supercomputers by 2020.
- China’s, Sunway TaihuLight is the fastest supercomputer (93 Pflops) and China has more supercomputers than the USA as of July 2016.
Possible Sample Questions for Mains
- What are supercomputers? What is its status in India? How does it help in the development of India and the world?
- Supercomputers have more strategic significance than scientific. Illustrate.
Sample Questions for Prelims
Question: With reference to supercomputers, petaflops are related to?
- A – The latest model of sSupercomputers developed by China.
- B – The latest model of supercomputers developed by the USA.
- C – The performance of supercomputers.
- D – Floppy disks which are used on normal desktop computers.
Answer: (Option C) The performance of supercomputers.
Learning Zone: The performance is generally evaluated in petaflops (1 followed by 15 zeros) and some supercomputers may even perform quadrillions flops.
Article by: Nishant Raj. The author is an IIT Kharagpur Alumnus.


Quantum photonic interconnect and entanglement distribution between two integrated Si photonic chips. Credit: arXiv:1508.03214 [quant-ph] Explore further For modern electronic devices to work, there must be some channels for the different parts to use to convey information between them—such channels are usually either wire carrying electricity or fiber carrying photons and are called interconnects. But as researchers shrink down the parts, the interconnects more and more represent a bottleneck. Worse, as scientists conduct research into creating a truly quantum computer, the problem of creating interconnects for them has become a serious issue. Now, in this new effort, the research team is claiming to have found a solution—one where a separate entanglement stage is used to preserve the original entanglement needed as part of normal operations—demonstrating a way to connect two photonic chips.To allow for interconnection, the researchers ran two sources of photons along one of the chips, on channels that overlapped—when the photons met in the overlap area, they became entangled and that entanglement was then carried along different paths in the chip. They next ran the photons through a device that converted that path entanglement into a whole new type of entanglement, one that involved polarization, which also caused the creation of new entangled photons. Those newly entangled polarized photons were then passed into an optical fiber that ran between the two chips. The whole process was then reversed in the second chip, where the polarized photons were converted back to path entangled photons, which then behaved exactly like the photons in the first chip. The team conducted multiple different types of tests to prove that entanglement was preserved throughout the interconnection process.The team acknowledges that the process is still too inefficient to be implemented into real devices, but believe further refinement will lead to a usable solution. But, they have shown that it is possible to interconnect quantum devices, which should come as a relief to those working on building a quantum computer. © 2015 Phys.org Journal information: arXiv New method of quantum entanglement vastly increases how much information can be carried in a photon This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only. More information: Quantum Photonic Interconnect, arXiv:1508.03214 [quant-ph] arxiv.org/abs/1508.03214AbstractIntegrated photonics has enabled much progress towards quantum technologies. However, many applications, e.g. quantum communication, sensing, and distributed and cloud quantum computing, require coherent photonic interconnection between separate sub-systems, with high-fidelity distribution and manipulation of entanglement between multiple devices being one of the most stringent requirements of the interconnected system. Coherently interconnecting separate chips is challenging due to the fragility of these quantum states and the demanding challenges of transmitting photons in at least two media within a single coherent system. Here, we report a quantum photonic interconnect demonstrating high-fidelity entanglement distribution and manipulation between two separate chips, implemented using state-of-the-art silicon photonics. Entangled states are generated and manipulated on-chip, and distributed between the chips by interconverting between path-encoding and polarisation-encoding. We use integrated state analysers to confirm a Bell-type violation of S=2.638+-0.039 between two chips. With improvements in loss, this quantum interconnect will provide new levels of flexible systems and architectures for quantum technologies.via Arxiv Blog (Phys.org)—An international team of researchers has found a way to interconnect two quantum devices, allowing photons to move between the two, all while preserving entanglement. In their paper they have uploaded to the preprint server arXiv, the team describes their process and their hopes for tweaking it to make it more efficient. Citation: A way has been found to interconnect quantum devices including preserving entanglement (2015, August 21) retrieved 18 August 2019 from https://phys.org/news/2015-08-interconnect-quantum-devices-entanglement.html
Kolkata: Following the direction of Chief Minister Mamata Banerjee, the state Transport department is going to start a state-wide drive to check the fitness of public transport vehicles.According to a senior official of the state Transport department, checking of goods vehicles is carried out round-the-year.”Now, we are going to initiate a process to carry out periodical fitness checking of public transport vehicles,” the official said, adding that initially emphasis will be given on checking fitness of buses and mini-buses that ply on different routes. It has been stated that there are two reasons behind starting fitness check of public transport vehicles. First of all, it will help in reducing the number of road accidents and will also ensure that commuters get better service, particularly as the bus fares have recently gone up. It may be mentioned that the Chief Minister has called for necessary steps to ensure periodical fitness checking of vehicles to avoid accidents. Also Read – Rain batters Kolkata, cripples normal lifeSince stress will be initially given on checking the fitness of buses, the official said that they will be checking whether a passenger is facing any problem while travelling in the vehicles. Citing some examples, the official added that they will be checking the condition of tyres being used in the public transport vehicles.It may be mentioned that use of good quality tyres in buses is mandatory to check accidents. According to experts, the chances of skidding goes up when sudden brake is applied if the condition of tyre is poor or if the vehicle is fitted with rethreaded tyres. Mainly in monsoon, many accidents take place due to the use of rethreaded tyres, as vehicles do not stop despite applying brakes. Also Read – Speeding Jaguar crashes into Mercedes car in Kolkata, 2 pedestrians killedBesides checking the quality of tyres, the officials will also check the overall condition of a bus to find out whether water drips from its ceiling and causes inconvenience to passengers. They will also check the condition of seats.”The checking will be carried out at different points and it will be done following the pick and choose method. Necessary steps will be taken if they find that the vehicles are not maintained properly,” the official said, adding that their endeavour is to ensure better amenities for passengers.The bus owners, in whose vehicles any fault is found, will be directed to repair the same, before plying them again.This comes at the time when the state government has taken up the state-wide Safe Drive Save Life campaign to bring down the rate of accidents.
In what comes as a rare tributary endeavor of Khadi and Village Industries Commission (KVIC) to Rashtrapita on his 150th Birth Anniversary Year, Vice President Muppavarapu Venkaiah Naidu will unveil Mahatma Gandhi’s ‘Grand Wall Mural’ at New Delhi Municipal Corporation (NDMC) Main Building on January 31, 2019.This 150 square meters clay mural is made of ‘Kulhads’ from the hands of 150 village potters across India, who assembled to make it at Morbi in Gujarat. Also Read – Add new books to your shelfEnthused with the classical efforts shown by the artisans, KVIC Chairman Vinai Kumar Saxena said that KVIC had decided to mark the occasion with practical display of Gandhian thoughts on village industries, at a time when the whole nation is celebrating Gandhiji’s 150th birth anniversary in different styles. “With the co-operation of these 150 highly-skilled potters from all over the country, we have made this wall mural, using their kulhads,” he said, adding, “They had brought clay of their respective regions which has been mixed to produce the kulhads for the mural. Kulhads of 75 mm diameter and 90 mm height were made on electric pottery wheels given by KVIC.” “The potters have finished the kulhads as per tradition and design requirements. Altogether 5,000 kulhads were produced, of which the best 3,870 kulhads – making them all-weather proof by baking at high temperatures and glazing – were used in the final design.” Ahmedabad-based designer terracotta and ceramic art studio, Clay Club Innovations, had designed the artwork. Expressing his happiness on this tributary endeavour of KVIC, the Minister of State for MSME Giriraj Singh, who will be the Guest of Honour on this occasion, said, “It will really be a proud moment for the nation when KVIC’s grand mural will prominently be displayed in the heart of the national Capital – showcasing the combined ‘sweaty’ efforts of village potters across the nation.”


The year 2019 was full of surprises, wonders, and even setbacks. From Greta Thunberg championing climate activism to quantum supremacy claims by Google, we saw massive movements and moments in this field. But one that stood out was the first-ever portrait of a black hole. Scientific community around the world rejoiced at the stunning image of this enigmatic phenomenon, and most of the scientific outlets have named it the best achievement in science of the year.
This is undoubtedly a great end to a decade. A decade that brought us unprecedented advancements in science and technology and has put us onto a path of even greater scientific enlightenment. The image of the black hole united the scientific community as everyone sat together, talked and marveled at the beauty and complexity of this universe.
Before this image, many theories were given about the black hole’s actual shape. Researchers constructed various images, but due to lack of ample data, those were not quite right. This conundrum was also explored in Hollywood movies, and Interstellar even managed to come a bit close with its depiction of a black hole. Nevertheless, we now have an accurate visual treat of what a black hole really looks like.
Achieving the feat
Bringing the image to life was not an easy task. Even in this age of technology and modern equipment, it took years to process the data by a big team of talented scientists. Some theories presented earlier called for recording the shadows that surround the glowing area of a black hole. For that, a vast network of telescopes was created around the world that ultimately came to be known as the Event Horizon Telescope or the EHT. To be able to visualize distant images in a high resolution and detail, a telescope should have a large aperture or diameter. This way, more light is gathered and can be used in image construction.
A technique called Very Long Baseline Interferometry (VLBI) was further honed by scientists and was used to create a network of telescopes that can aim at an object of interest at the same time. This network can then act together as one big telescope. To locate spacecraft and missions in outer space, and to capture photos of various things in the universe, this technique is preferably used. EHT’s aperture is substantial and is equal to the distance between the two stations at the South Pole and in Spain. This was cleverly arranged as the resulting setup ended up being almost the same length as the diameter of the Earth. The arrangement and spacing of the telescopes are also crucial in image resolution, and the farther they are, the better the quality gets.
For taking the image of the black hole, the team of scientists decided to test the VLBI technique and computer programs and algorithms on two targets, each with its complexities and wonders.
One of these was Sagittarius A* – the closest supermassive black hole to our planet. Located at the very center of our galaxy at a distance of 26,000 light-years away, this appears to be the biggest in size when seen from the Earth. But its existence in the Milky Way also posed a problem for scientists, who figured that they would have to clean out all the background noise and pollution in the data, and a complicated process was needed to filter it all out. Still, it offered an exciting opportunity to the researchers who ultimately chose it as a target, despite such issues.
The other target was the black hole M87*, which is located in the center of the galaxy Messier 87 at 53 million light-years away. It is massive, and to get an idea of its size, the fact should be noted that it contains a whopping 6.5 billion solar masses! It was also an exciting and intriguing choice for the researchers as it is an active black hole meaning that matter is continually falling in and out of it.
The particles also jet out of M87* at very high velocities (almost at the speed of light). Being that far away was yet another challenge in taking its picture. Katie Bouman, the computer scientist with the Event Horizon Telescope team who became the star and another highlight of this feat, very aptly described it as similar to taking the photo of an orange on the surface of the Moon.
Originally the EHT had eight locations around the world but in the later years, more telescopes were added to help analyze and refine the data. For the collection of the data, there was a need for having suitable weather for telescope viewing at each location and it took almost ten days to observe it all.
Valid calibration and synchronization of the telescope was an essential task which ultimately enabled EHT to have a resolution that was 4,000 times better than that of the Hubble Space Telescope. A considerable amount of data was obtained by the team, which was then transported to the primary location where it could be studied easily with high internet speed. It was in this central area, where the scientists managed to combine the data using various programs and algorithms and developed the first-ever image of the silhouette of the event horizon of M87*. The other target’s image is also in the process of being developed. NASA also contributed to this strenuous task, and several spacecraft were used to observe the black hole with varying wavelengths of light.
The genius minds behind the scenes
The team who made this wonder possible from impossible also deserves immense appreciation for their hard work. These researchers were recently honored with the Breakthrough Prize in Fundamental Physics for their efforts. The team was led by Shep Doeleman at the Harvard-Smithsonian Center for Astrophysics. He told in an interview that “For many years, I would tell people that we were going to image a black hole, and they would say, ‘Well, we’ll believe it when we see it.’ But when you finally come with robust evidence, when you make a breakthrough like this, then you have the satisfaction of really giving birth to a new field.”
As mentioned above, another scientist that almost became a household name in the field of science was Katie Bouman, who garnered worldwide attention for working on the algorithm that helped to make the final image of the black hole. She became an inspiration for many people, especially women working in STEM. She started working on the algorithm as a graduate student at the Massachusetts Institute of Technology or MIT. In a caption to her Facebook post, she wrote, “Watching in disbelief as the first image I ever made of a black hole was in the process of being reconstructed.” She was hailed and appreciated around the world for her groundbreaking work along with her team.
Paving the way for scientific glory
Taking this image is no ordinary achievement. It is a big step in unraveling the mysteries of the universe. It can help us to test predicted theories and make observations about spacetime and celestial objects that have staggered humans since almost the beginning of the time. From working out and filling the gaps in Einstein’s theory of relativity to improving Hawking’s views on quantum mechanics, such type of data and knowledge are essential tools for figuring stuff out.
Einstein’s theory of general relativity was not really been proven for the black hole and other similar paradoxes. This project offers a more precise calculation of the mass of a black hole. The radius of M87 *’s event horizon was accurately measured, and a method of mass estimation was validated. General relativity equations can be used to provide an estimate of the size and shape of a black hole, which calls for it to be roughly circular contrasting other theories. The developed image showed that it indeed has a circular silhouette, thus proving the theory. This data provided information about formation and behavior, and some elements, such as the ejection of particles at the speed of light, are now offering new research interests for scientists.
As EHT continues to provide more data, new questions can now be answered, and studies can be done at an accelerated pace. Other areas can also get benefit from it, and it has also successfully ignited the fuel of passion and curiosity about the universe that has enabled scientists and researchers to come this far and will continue to take us to infinity and beyond!
Note Asterisk (*) is used to represent a black hole.


- Eclipses, Equinoxes, and Solstices and Earth Perihelion and Aphelion
- Space Exploration
- Human spaceflight launches and returns, 2011
The study of graphene—a two-dimensional lattice of carbon atoms on an insulating substrate—produced results that may lead to a new generation of electronic devices, since electrons can travel in graphene 100 times faster than in silicon. Yanqing Wu and co-workers at the IBM Thomas J. Watson Research Center, Yorktown Heights, N.Y., studied graphene transistors that had cut-off frequencies as high as 155 GHz and that, unlike conventional devices, worked well at temperatures as low as 4.3 K (−268.9 °C, or −451.9 °F). Ming Liu and colleagues at the NSF Nanoscale Science and Engineering Center, Berkeley, Calif., demonstrated a high-speed broadband electro-optical modulator with high efficiency and an active device area of only 25 μm2. Such a device could lead to new designs of optical communications on chips. Vinay Gupta and colleagues from the National Physical Laboratory, New Delhi, made luminescent graphene quantum dots blended with organic polymers for use in solar cells and light-emitting diodes, which could offer better performance at lower cost than other polymer-based organic materials. By combining graphene with extremely small metal wires called plasmonic nanostructures, T.J. Echtermeyer, of the University of Cambridge, and co-workers made graphene-based photodetectors that were 20 times more efficient than those made in previous experiments.
Other two-dimensional systems were studied. A.F. Santander-Syro’s group at Université de Paris-Sud, Orsay, France, showed that there was a two-dimensional electron gas at the surface of the material SrTiO3.
One possible way for future computers to store information would be to encode data in the spin of electrons; such a computer has been called “spintronic.” Kuntal Roy and colleagues at Virginia Commonwealth University made a great step to producing a spintronic device by making a small spintronic switch in which very small amounts of energy would cause a piezoelectric material to move and thus change the spins of electrons in a thin magnetic layer. Devices using such switches could be powered by only very slight movements.
Two new types of laser appeared in 2011. Yao Xiao and colleagues at the department of optical instrumentation, Zhejiang University, Hangzhou, China, reported lasing action at 738 nm (nanometres), using a folded wire 200 nm in diameter. The configuration made possible a tunable single-mode nanowire laser. Malte Gather and Seok-Hyun Yun at Harvard Medical School created a “living laser” by using biological material. Green fluorescent protein that had been inserted into human embryo kidney cells was used in a tiny optical cavity to produce laser light. This technique could be used to study processes in a living cell.
In a different region of the electromagnetic spectrum, J.R. Hird, C.G. Camara, and S.J. Putterman at the department of physics and astronomy, University of California, Los Angeles, investigated the triboelectric effect, in which electric currents are generated by friction. When the team pulled apart silicon and a metal-coated epoxy, a current generated by the friction was found to produce a beam of X-rays. This method could lead to a new generation of simple and cheap sources for X-ray imaging.
Lasers and optical devices for high-speed communications and information processing were being studied in many laboratories, with an emphasis on efficiency and reproducibility. Bryan Ellis and co-workers at Stanford University developed an electrically pumped quantum dot laser that produced continuous wave operation with the lowest current threshold yet observed. Matthew T. Rakher and colleagues at National Institute of Standards and Technology, Gaithersburg, Md., devised a system for simultaneous wavelength translation and amplitude modulation for single photons, using the “blending” in a crystal of photons from two separate laser sources. Georgios Ctistis and colleagues at the University of Twente, Enschede, Neth., built a switch that changed state in just one-trillionth of a second (10−12 s).
Quantum information systems involve photons that are “entangled”—perfectly correlated over long distances. For storage and transmission of such photons, practical quantum memories are required for storing and recalling quantum states on demand with high efficiency and low noise. For transmission occurring over long distances, memory repeaters are required for receiving input data and retransmitting.
In 2011 a number of groups demonstrated designs for such devices. M. Hosseini and colleagues at the Australian National University in Canberra reconstructed quantum states that had been stored in the ground states of rubidium vapour with up to 98% fidelity. Christoph Clausen and co-workers at the University of Geneva demonstrated entanglement between a photon and a physical system. One photon from an entangled pair was stored in a Nd:Y2SiO5 crystal and then later released, but it still retained its entanglement with the unstored photon.
Holger P. Specht and co-workers at the Max Planck Institute for Quantum Optics, Garching, demonstrated a system in which a quantum bit, or qubit (a photon whose polarization states contain information), was absorbed by a single rubidium atom trapped inside an optical cavity. The rubidium atom later emitted a photon containing the original polarized information. Thus, the rubidium atom served as a quantum computer memory.
In a very different approach, Christian Ospelkaus of Leibniz University, Hannover, Ger., and colleagues used a waveguide integrated on a microchip to produce the first microwave quantum gate—that is, a logic gate for a quantum computer. Two ions were trapped just above the chip’s surface. Multiple pulses of microwave radiation entangled the two ions, which acted as a quantum gate. N. Timoney and colleagues at the University of Siegen, Ger., trapped individual ions and applied microwave pulses to them to decouple them from outside noise and thus make an undisturbed quantum processor. Such developments could aid the production of large ion-trap quantum computers in the foreseeable future.


August 15, 2000 -- At a technical conference today at Stanford University, IBM-Almaden researcher Isaac Chuang described his team's experiments that demonstrated the world's most advanced quantum computer and the tremendous potential such devices have to solve problems that conventional computers cannot handle.
"Quantum computing begins where Moore's Law ends -- about the year 2020, when circuit features are predicted to be the size of atoms and molecules," says Isaac L. Chuang, who led the team of scientists from IBM Research, Stanford University and the University of Calgary. "Indeed, the basic elements of quantum computers are atoms and molecules."
Quantum computers get their power by taking advantage of certain quantum physics properties of atoms or nuclei that allow them to work together as quantum bits, or "qubits," to be the computer's processor and memory. By interacting with each other while being isolated from the external environment, theorists have predicted -- and this new result confirms -- that qubits could perform certain calculations exponentially faster than conventional computers.
The new quantum computer contains five qubits -- five fluorine atoms within a molecule specially designed so the fluorine nuclei's "spins" can interact with each other as qubits, be programmed by radiofrequency pulses and be detected by nuclear magnetic resonance instruments similar to those commonly used in hospitals and chemistry labs.
Using the molecule, Chuang's team solved in one step a mathematical problem for which conventional computers require repeated cycles. The problem is called "order-finding" -- finding the period of a particular function -- which is typical of many basic mathematical problems that underlie important applications such as cryptography.
While the potential for quantum computing is huge and recent progress is encouraging, the challenges remain daunting. IBM's five-qubit quantum computer is a research instrument. Commercial quantum computers are still many years away, since they must have at least several dozen qubits before difficult real-world problems can be solved.
"This result gives us a great deal of confidence in understanding how quantum computing can evolve into a future technology," Chuang says. "It reinforces the growing realization that quantum computers may someday be able to live up to their potential of solving in remarkably short times problems that are so complex that the most powerful supercomputers can't calculate the answers even if they worked on them for millions of years."
Chuang says the first applications are likely to be as a co-processor for specific functions, such as database lookup and finding the solution to a difficult mathematical problem. Accelerating word processing or Web surfing would not be well-suited to a quantum computer's capabilities.
Chuang presented his team's latest result today at Stanford University at the Hot Chips 2000 conference, which is organized by the Institute of Electrical and Electronics Engineers' (IEEE) Computer Society. His co-authors are Gregory Breyta and Costantino S. Yannoni of IBM-Almaden, Stanford University graduate students Lieven M.K .Vandersypen and Matthias Steffen, and theoretical computer scientist Richard Cleve of the University of Calgary. The team has also submitted a technical report of their experiment to the scientific journal, Physical Review Letters.
History of Quantum ComputingWhen quantum computers were first proposed in the 1970s and 1980s (by theorists such as the late Richard Feynmann of California Institute of Technology, Pasadena, Calif.; Paul Benioff of Argonne National Laboratory in Illinois; David Deutsch of Oxford U. in England., and Charles Bennett of IBM's T.J. Watson Research Center, Yorktown Heights, N.Y.), many scientists doubted that they could ever be made practical. But in 1994, Peter Shor of AT&T Research described a specific quantum algorithm for factoring large numbers exponentially faster than conventional computers -- fast enough to break the security of many public-key cryptosystems. Shor's algorithm opened the doors to much more effort aimed at realizing the quantum computers' potential. Significant progress has been made by numerous research groups around the world.
Chuang is currently among the world's leading quantum computing experimentalists. He also led the teams that demonstrated the world's first 2-qubit quantum computer (in 1998 at University of California Berkeley) and 3-qubit quantum computer (1999 at IBM-Almaden). The order-finding result announced today is the most complex algorithm yet to be demonstrated by a quantum computer.
Note: Earlier this year, scientists at Los Alamos National Laboratories announced they had achieved quantum coherence in a seven-qubit molecule. While this is a necessary condition for achieving a quantum computer, they have not yet used the molecule as a seven-qubit quantum computer to solve a problem or to implement a quantum algorithm.
How a Quantum Computer Works
A quantum particle, such as an electron or atomic nucleus, can exist in two states at the same time -- say, with its spin in the up and down states. This constitutes a quantum bit, or qubit. When the spin is up, the atom can be read as a 1, and the spin down can be read as a 0. This corresponds with the digital 1s and 0s that make up the language of traditional computers. The spin of an atom up or down is the same as turning a transistor on and off, both represent data in terms of 1s and 0s.
Qubits differ from traditional digital computer bits, however, because an atom or nucleus can be in a state of "superposition," representing simultaneously both 0 and 1 and everything in between. Moreover, without interference from the external environment, the spins can be "entangled" in such a way that effectively wires together a quantum computer's qubits. Two entangled atoms act in concert with each other -- when one is in the up position, the other is guaranteed to be in the down position.
The combination of superposition and entanglement permit a quantum computer to have enormous power, allowing it to perform calculations in a massively parallel, non-linear manner exponentially faster than a conventional computer. For certain types of calculations -- such as complex algorithms for cryptography or searching -- a quantum computer can perform billions of calculations in a single step. So, instead of solving the problem by adding all the numbers in order, a quantum computer would add all the numbers at the same time.
To input and read the data in a quantum computer, Chuang's team uses a nuclear magnetic resonance machine, which uses a giant magnet and is similar to the medical devices commonly used to image human soft tissues. A tiny test-tube filled with the special molecule is placed inside the machine and the scientists use radio-frequency pulses as software to alter atomic spins in the particular way that enables the nuclei to perform calculations.
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In physics, a quantum (plural: quanta) is the minimum amount of any physical entity involved in an interaction. Behind this, one finds the fundamental notion that a physical property may be "quantized," referred to as "the hypothesis of quantization". This means that the magnitude can take on only certain discrete values.
A photon is a single quantum of light, and is referred to as a "light quantum". The energy of an electron bound to an atom is quantized, which results in the stability of atoms, and hence of matter in general.
As incorporated into the theory of quantum mechanics, this is regarded by physicists as part of the fundamental framework for understanding and describing nature at the smallest length-scales.
Etymology and discovery
The word "quantum" comes from the Latin "quantus", meaning "how much". "Quanta", short for "quanta of electricity" (electrons) was used in a 1902 article on the photoelectric effect by Philipp Lenard, who credited Hermann von Helmholtz for using the word in the area of electricity. However, the word quantum in general was well known before 1900. It was often used by physicians, such as in the term quantum satis. Both Helmholtz and Julius von Mayer were physicians as well as physicists. Helmholtz used "quantum" with reference to heat in his article on Mayer's work, and indeed, the word "quantum" can be found in the formulation of the first law of thermodynamics by Mayer in his letter dated July 24, 1841. Max Planck used "quanta" to mean "quanta of matter and electricity", gas, and heat. In 1905, in response to Planck's work and the experimental work of Lenard (who explained his results by using the term "quanta of electricity"), Albert Einstein suggested that radiation existed in spatially localized packets which he called "quanta of light" ("Lightquanta").
The concept of quantization of radiation was discovered in 1900 by Max Planck, who had been trying to understand the emission of radiation from heated objects, known as black-body radiation. By assuming that energy can only be absorbed or released in tiny, differential, discrete packets he called "bundles" or "energy elements", Planck accounted for the fact that certain objects change colour when heated. On December 14, 1900, Planck reported his revolutionary findings to the German Physical Society, and introduced the idea of quantization for the first time as a part of his research on black-body radiation. As a result of his experiments, Planck deduced the numerical value of h, known as the Planck constant, and could also report a more precise value for the Avogadro–Loschmidt number, the number of real molecules in a mole and the unit of electrical charge, to the German Physical Society. After his theory was validated, Planck was awarded the Nobel Prize in Physics in 1918 for his discovery.
Beyond electromagnetic radiation
While quantization was first discovered in electromagnetic radiation, it describes a fundamental aspect of energy not just restricted to photons. In the attempt to bring experiment into agreement with theory, Max Planck postulated that electromagnetic energy is absorbed or emitted in discrete packets, or quanta.
- Elementary particle
- Introduction to quantum mechanics
- Magnetic flux quantum
- Photon polarization
- Quantal analysis
- Quantization (physics)
- Quantum cellular automata
- Quantum channel
- Quantum coherence
- Quantum chromodynamics
- Quantum computer
- Quantum cryptography
- Quantum dot
- Quantum electronics
- Quantum entanglement
- Quantum immortality
- Quantum lithography
- Quantum mechanics
- Quantum number
- Quantum sensor
- Quantum state
- Subatomic particle
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