Researchers at IBM have created an elusive molecule by knocking around atoms using a needle-like microscope tip. The flat, triangular fragment of a mesh of carbon atoms, called triangulene1, is too unstable to be made by conventional chemical synthesis, and could find use in electronics.
This isn't the first time that atomic manipulation has been used to create unstable molecules that couldn’t be made conventionally — but this one is especially desirable. “Triangulene is the first molecule that we’ve made that chemists have tried hard, and failed, to make already,” says Leo Gross, who led the IBM team at the firm’s laboratories in Zurich, Switzerland.
The creation of triangulene demonstrates a new type of chemical synthesis, says Philip Moriarty, a nanoscientist who specializes in molecular manipulation at the University of Nottingham, UK. In conventional synthesis, chemists react molecules together to build up larger structures. Here, by contrast, atoms on individual molecules were physically manipulated using a microscope.
But making molecules one at a time will be useful only in particular situations. And the method is unlikely to work for those with complicated shapes or structures that make it hard to identify or target individual atoms.
Triangulene is similar to a fragment of graphene, the atom-thick material in which carbon atoms are joined in a hexagonal mesh. The new molecule is made up of six hexagons of carbon joined along their edges to form a triangle, with hydrogen atoms around the sides (see ‘Radical triangle’). Two of the outer carbon atoms contain unpaired electrons that can’t pair up to make a stable bond.
Such a molecule is highly unstable because the unpaired electrons tend to react with anything around them. “As soon as you synthesize it, it will oxidize,” says Niko Pavliek, a member of the IBM team. So far, the closest conventional synthesis has come to making molecules of this sort involves buffering the reactive edges with bulky hydrocarbon appendages2.
The IBM team turned to a scanning probe microscope, which has a needle-sharp tip that ‘feels’ a material’s shape. The technique is usually used to image molecules, by measuring attractive forces between the tip and sample, or the electric currents that pass between them. The IBM team has demonstrated3 that, if the tip has a small molecule such as carbon monoxide attached to it, force microscopy can provide images of such high resolution that they resemble the ball-and-stick diagrams of chemistry textbooks.
Gross’s team has already shown how the microscope can be used to direct the course of chemical reactions and make unstable 'intermediate' molecules4. To produce triangulene, the team began with a precursor molecule called dihydrotriangulene, which lacks the reactive unpaired electrons. The precursors were synthesized by chemists at the University of Warwick in Coventry, UK.
The researchers deposited these molecules on a surface — salt, solid xenon and copper are all suitable — and inspected them under the microscope.They then used two successive voltage pulses from the tip, carefully positioned above the molecules, to blast off two hydrogen atoms and create the unpaired electrons. The work is published in Nature Nanotechnology1.
The team then imaged the products with the microscope, first picking up a carbon monoxide molecule to acquire the high resolution. The images had the shape and symmetry predicted for triangulene. Under the high-vacuum, low-temperature conditions of the experiments, the molecules remained stable for as long as the researchers looked.
“To my knowledge, this is the first synthesis of unsubstituted triangulene,” says chemist Takeji Takui of Osaka City University in Japan, who has previously synthesized triangulene-type molecules2.
Moriarty calls the work elegant, but is surprised that triangulene remained stable on a copper surface, where he might have expected it to react with the metal.In one set of experiments, says Pavliek, the molecule was still sitting on the copper four days after the team made it.
The researchers also probed triangulene’s magnetic properties. They found that, as they had expected, the two unpaired electrons have aligned spins — the quantum-mechanical property that gives electrons a magnetic orientation.
This property could make triangulene useful in electronics, they say. Takui agrees, and foresees applications in quantum computing, quantum information processing and a field known as spintronics, in which devices manipulate electron spins to encode and process information.
Making molecules one at a time might not seem very promising, but Gross points out that current quantum computers, such as the Quantum Experience developed at IBM, use only a handful of quantum bits, or qubits, each of which could correspond to a single molecule. Even if you need to make 100 such molecules “by hand”, he says, “it would be worth going through that manual labour”.
And although it’s not clear how easily the approach could be applied to molecules that aren’t flat, Gross says that such atom manipulation can be performed for 3D molecules to some extent.
Even with triangulene and related graphene-like fragments, “there’s a lot of exciting science still to be done”, says Moriarty. The IBM team “continues to set a high bar for the rest of us”, he adds.
This article is reproduced with permission and was first published on February 13, 2017.


In the last six decades, computers have become faster, compact, and cheaper. However, for engineers, the options have almost saturated for how small the silicon transistors can be made and how fast they can transfer electricity via devices to form digital ones and zeros.
Such a restriction has led Jelena Vuckovic, a Stanford electrical engineering Professor, to turn towards quantum computing, which is dependent on light and not electricity. Quantum computers operate by distancing the spinning electrons inside an innovative kind of semiconductor material. Once the electron is struck by a laser, it emits one or more quanta (or particles) of light to display the manner in which the electron spins. These spin states replace the ones and zeros of conventional computing.
Vuckovic, one of the leading scientists in the field, stated that quantum computing is perfect for analyzing biological systems, performing cryptography, or data mining — or even solving any challenge with a number of variables.
When people talk about finding a needle in a haystack, that’s where quantum computing comes in.
According to Marina Radulaski, a postdoctoral fellow in Vuckovic’s laboratory, the problem-solving ability of quantum computers arises from the complexity of the interactions between laser and electron fundamental to the concept.
With electronics you have zeros and ones. But when the laser hits the electron in a quantum system, it creates many possible spin states, and that greater range of possibilities forms the basis for more complex computing.
Acquiring information related to the interactions between electrons and light is difficult. Few of the major technology companies around the world are endeavoring to construct massive quantum computers that are dependent on materials that are super-cooled to near absolute zero, which is the theoretical temperature at which the movement of atoms is restricted.
Vuckovic’s two decades of own research has focused on one facet of the problem, namely, developing innovative quantum computer chips that will be the building blocks of prospective systems.
To fully realize the promise of quantum computing we will have to develop technologies that can operate in normal environments. The materials we are exploring bring us closer toward finding tomorrow’s quantum processor.
The obstacle to be overcome by Vuckovic and her colleagues is to create materials with the ability to trap a single, isolated electron. The research team has worked alongside international collaborators and has recently investigated three disparate ways to overcome the problem. One way is to enable operations at room temperature, which is a crucial step if quantum computing is to be developed into a practical tool.
For all three approaches, the researchers began by using semiconductor crystals, which are materials that have a regular atomic lattice similar to the girders of a skyscraper. When the lattice is slightly modified, a structure can be developed in which the atomic forces applied by the material have the ability to trap a spinning electron.
We are trying to develop the basic working unit of a quantum chip, the equivalent of the transistor on a silicon chip.
One method of developing such a laser-electron interaction chamber is by means of a structure called as a quantum dot. In physical terms, the quantum dot is a small quantity of indium arsenide enclosed inside a gallium arsenide crystal. The atomic characteristics of the two materials are known to confine a spinning electron.
In a latest paper published in the journal Nature Physics, Kevin Fischer (who is a graduate student at Vuckovic’s laboratory) has reported the ways in which laser-electron processes can be used within such a quantum dot to regulate the input and output of light. When more laser power is applied to the quantum dot, it can be forced to emit precisely two photons in the place of one. According to the researchers, the quantum dot has practical benefits when compared to other major quantum computing platforms. Yet, it mandates cryogenic cooling, and hence might not prove handy for general-purpose computing. However, it can be used for developing tamper-proof communication networks.
Vuckovic employed a varied approach to electron capture in two other papers, where she modified a single crystal to confine light in the so-called color center.
In a latest paper in the journal NanoLetters, Vuckovic and her colleagues have analyzed color centers in diamond. Naturally, the crystalline lattice in diamond is made of carbon atoms. Jingyuan Linda Zhang (who is a graduate student in Vuckovic’s laboratory) reported the manner in which a 16-member research group substituted certain carbon atoms with silicon atoms. The single modification led to the formation of color centers that could efficiently confine spinning electrons inside the crystalline lattice in diamond.
However, similar to the quantum dot, most of the diamond color center experiments mandate cryogenic cooling. Despite the fact that it is an enhancement over other techniques that mandated an elaborate cooling, Vuckovic aspired to achieve more.
Therefore, she collaborated with another international team of researchers to analyze a third material, namely, silicon carbide. Silicon carbide is generally called as carborundum, and is a hard, transparent crystal used for manufacturing brake pads, clutch plates, and bulletproof vests. Earlier studies have demonstrated that silicon carbide can be altered to form color centers at ambient temperature. However, this potential has not been made adequately efficacious to synthesize a quantum chip.
Vuckovic and her colleagues removed specific silicon atoms from the silicon carbide lattice to form highly efficacious color centers. They further produced nanowire structures around the color centers to enhance photon extraction. Radulaski was the first author of that study, which was reported in another paper published in NanoLetters. According to Radulaski, the net outcomes, such as efficacious color center, working at ambient temperature, in a material well known in the industry, were highly advantageous.
We think we’ve demonstrated a practical approach to making a quantum chip.
However, this field is just emerging and electron confinement is not so easy. Not even the scientists are confident on the technique, or techniques, that will be effective.
We don’t know yet which approach is best, so we continue to experiment.


For a long time, the development of quantum computers was concerned with theoretical and hardware aspects. But as the focus shifts towards programming, software and security issues, the classical computer sciences are coming back into play.
Physicists had long nurtured the ambition to build a quantum computer. In the early 1980s, one of the most famous among them, Richard Feynman (1918 -1988), questioned whether it would ever be possible to efficiently compute and simulate quantum physics phenomena using a conventional computer. He argued that digital computers couldn’t compute fast enough to calculate and simulate the quantum effects that typically occur within atoms and molecules and between elementary particles - at least not within a reasonable period of time.
Initially, he proposed building a quantum computer based not on digital coding but rather on a direct imitation of quantum systems. His core idea, which continues to inspire the development of quantum computers to this day, was that certain properties of quantum mechanics could be harnessed for computation. Specifically, this would mean taking advantage of two quantum states of particles: superposition and entanglement.
The principle of superposition, for example, can be exploited by quantum computers to carry out faster calculations. While digital computers use binary bits that can only take on the states of one or zero, quantum computers use quantum bits, or qubits, to process information. Qubits can be one or zero, and they can also be both one and zero at once, a state we call superposition. This crucial difference enables a huge leap in computing speed for certain computational problems.
In future, quantum computers promise to perform ultra-efficient calculations that normal computers cannot solve in a reasonable period of time, a milestone sometimes referred to as quantum supremacy. Although scientists have yet to find conclusive proof of the existence of quantum supremacy, recent technical advances have been impressive. In 2019, Google claimed to have achieved quantum supremacy for a specific computational problem for the first time, having built a quantum computer that required only 200 seconds to solve a problem that would have taken a conventional computer 10,000 years.
Encryption could be cracked
Right now, quantum computers are too small and error-prone to pose any serious threat to today’s digital computers, which are capable of performing billions of computations per second. Even Google’s quantum computer was only able to prove its supremacy in a single, specific task. Nonetheless, quantum technologies have now reached a stage where their development lies in the hands of more than just physicists. Today, many computer scientists are "quantum curious", according to ETH Computer Science Professor Kenneth Paterson. He conducts research in the field of cryptography and works on ways of securely processing, transferring and storing information. "We’ve been ’quantum aware’ in my area of research ever since quantum computing started to become a bigger issue in cryptography about ten years ago," says Paterson. "As soon as someone builds a quantum computer that is sufficiently large-scale and reliable, the current encryption framework of the internet will cease to be secure, because quantum computing could be used to crack that encryption."
The encryption and security protocols that run behind the scenes whenever we log on to social media, make an online purchase, use online banking or send an email are all based on integer factorisation and related problems that are vulnerable to Shor’s algorithm. Integer factorisation is the process of breaking down a large composite integer into its prime factors. This requires huge computing power, which is why there is still no algorithm - that is, no calculating procedure - that a digital computer can use to efficiently solve a factorisation problem. Back in 1994, however, mathematician Peter Shor created an algorithm specially designed for quantum computing, which can find the prime factors of composite integers significantly faster than classical algorithms. Shor’s ideas can be used to crack the other forms of public key cryptography in use today.
Today’s quantum computers are too small and error-prone to run Shor’s algorithm. In principle, however, it is clear that any quantum computer that is powerful and reliable enough to do so would be able to perform factorisation within a reasonable period of time. The moment this situation occurs, factoring-based cryptography and related techniques currently in widespread use will no longer be secure. Not all of cryptography will be affected, of course; for example, quantum computing won’t seriously affect the security of encryption methods that rely solely on secret-key cryptography. But public-key cryptography - which currently forms the basis for securing over 90 percent of web traffic - will definitely be at risk.
According to Paterson, a quantum computer would need millions of quantum bits to crack a security key. Scientists at ETH Zurich are currently running quantum computers with up to 17 qubits. On the development side, researchers are on the brink of reaching a new phase of mid-sized quantum computing systems with 50 to 100 qubits, though these are still susceptible to errors. "But we might see a sudden breakthrough in the power of quantum computers, and it could take at least ten years to modify today’s public key cryptography. That’s why we’re getting ready now," says Paterson. His group has co-developed a new quantum-safe algorithm that is being evaluated in an on-going worldwide competition to select new, quantum-secure algorithms.
People sometimes ask Benjamin Bichsel whether he feels his research will have been in vain should large-scale, reliable quantum computers eventually turn out to be unfeasible. "I think that’s the wrong question," he says. "But I do wonder what we’ll do if quantum computers end up working brilliantly and we don’t have a clue how to programme them efficiently!" Bichsel works in the research group led by computer science professor Martin Vechev, whose group developed the first intuitive high-level programming language for quantum computing in 2020.
It will take special programming languages to properly exploit the potential of quantum computers. "Quantum programming languages are essential to translate ideas into instructions that can be executed by a quantum computer," wrote Microsoft researchers in 2020 in the science journal Nature. The authors included Bettina Heim and Matthias Troyer, who had previously worked as researchers at the ETH Institute for Theoretical Physics.
Today’s quantum programming languages are tied closely to specific hardware. These "hardware description languages" focus on the behaviour of circuits and how to optimise them. In contrast, the Silq programming language developed by Martin Vechev’s group abstracts from the technical details.
Over a year has passed since Silq was launched; as the first high-level quantum programming language, it has already won acclaim for its elegance and internal coherence. Martin Vechev and his team have also earned praise for their innovative contribution towards reducing errors in quantum computing. In a further article about Silq, Nature explicitly refers to the "uncomputation" feature that enables Silq to automatically reset temporary values "rather than forcing programmers to do this tedious work manually".
A computer processes a task in several intermediate steps, creating intermediate results or "temporary values" in the process. In classical computers, these values are erased automatically to free up memory. This task is a lot more complex in the case of quantum computers, however, since the principle of entanglement means that previously calculated values may interact with current ones and jeopardise the calculation process. That makes the ability to automatically clean up temporary values a key part of quantum computing.
A holistic view of computing
The question of whether Silq can hold its own against the quantum programming languages developed by technology giants Microsoft, IBM and Google - Q#, Qiskit and Cirq, respectively - is still very much up in the air. But, in the meantime, Vechev’s team have also succeeded in transferring automatic uncomputation to Qiskit. "It’s very encouraging to see that we can transfer key Silq concepts to other languages - especially since automatic uncomputation improves the efficiency of quantum computing with Qiskit," says Martin Vechev.
In the long run, there will be less of a focus on computer scientists writing languages and software for hardware developed by physicists. Instead, the emphasis will shift to developing programming languages hand in hand with quantum algorithms, quantum hardware, quantum software, quantum applications and workflows. "If we genuinely want to make quantum computing a reality, we will need to make this new approach part of a fully fledged computer system in which multiple components combine to solve specific problems efficiently," says Paterson.
This text appeared in the 21/03 issue of the ETH magazine Globe.
Kenneth Paterson is Professor of Computer Science at the Institute of Information Security, where he leads the Applied Cryptography Group.
Martin Vechev is a professor at the Institute for Programming Languages and Systems and heads the Secure, Reliable, and Intelligent Systems Lab (SRI) research group.


You may not realize it, but you probably have valuable information stored in your computer. And if you don’t keep them safe, others can access and misuse them. This is where encryption comes in handy. Encryption is a way of scrambling information so that it cannot be easily read by anyone except for the person with the key to decrypt it. It’s a good idea to encrypt any information that is very sensitive or of high value before uploading online. Here are some ways to encrypt your files using asymmetric encryption:
Encryption is the process of converting information to make it unreadable without some form of authorization. A popular encryption method is asymmetric encryption, which is done using two keys: a public key and a private key. The public key allows anyone to encrypt data they send to you, but your private key is what lets you decrypt the message. This means that as long as your private key remains safe, no one can read messages that are encrypted with your public key without breaking the code. To help you get started with this process, here are some steps for how to encrypt your files using asymmetric encryption.
Data encryption is that the main process of converting readable information in to a form that can’t be read by someone without access to a secret key. Data encryption offers protection from privacy and security breaches. It also helps to ensure compliance with regulatory standards.
There are many ways to encrypt your files, but there is one type of data encryption which is more complex and harder to crack than others: asymmetric encryption. Asymmetric encryption uses a pair of keys, one public and one private, and allows for messages to be encrypted using the public key and decrypted using the private key. This blog post will teach you about how asymmetric encryption works and will give you tips on how to use it.
It is a fact that what you don’t know about encryption could hurt you. What if your personal data, or your most sensitive information was stolen? The unfortunate truth is that all data on the internet is vulnerable to being hacked and intercepted.
What can you do to protect yourself? There are many security options out there, but one of the best ways to keep your files secure is by using asymmetric encryption. This guide will teach you how to encrypt your files with this method so that no one else can access them without your permission.
Encrypting your files is important for privacy. As a result, you should encrypt any information you would not want people to see and store it in a secure location. For example, if you have confidential data in an Excel document, you can encrypt the file by using asymmetric encryption.
This article will teach you how to do this process with a few simple steps outlined below:
-Download and install the software McAfee Endpoint Encryption.
-Launch the software.
-On the welcome screen, click Next.
-Select Automatically activate product.
-Click on Next.
-Enter your email address and company name (optional).
-Click Activate Now!
One of the most important things you can do to keep your files safe is to encrypt them. This is especially true if you have a laptop or other device that you take with you everywhere. If someone steals your device, they could potentially access all of your files and see personal information like passwords and credit card numbers.
Encrypting your files requires a special type of software called an encryption program. Many people use these programs to protect their data and personal items from hackers or thieves. Here are some of the best encryption programs available for free download today.
Data breaches are getting more and more of the common. With the invention of quantum computing, hackers now have an easier time breaking into data. Encryption is one way to protect your files from prying eyes. It can also be used to sign important documents securely. The idea is pretty simple: you want to create a message that only you can read by using a secret key. There are two styles of encryptions, one is symmetric and second one is asymmetric encryption. Symmetric encryption uses the same key for both encrypting and decrypting text messages, meaning that if someone were to get their hands on that key, they’d have access to all your information. Asymmetric encryption uses two keys—a public key and a private key—which means that if
Encryption is the process of encoding a message or information in such a way that only authorized parties can read it. It’s also used to secure data and protect sensitive information. This article will show you how to encrypt your files using asymmetric encryption!
If you want to keep your files safe, make sure you follow these steps: (1) Gather all the files you want to encrypt (2) Encrypt them with an asymmetric key (3) Decrypt them with the same asymmetric key.
This article is designed for people who are interested in the technical side of encryption. If you just want a few tips on how to protect your personal photos from prying eyes, skip this article.
If you work with sensitive data, it is important to protect your files from prying eyes. One way to do this is by using asymmetric encryption. Asymmetric encryption uses a pair of keys to encrypt and decrypt files. One key encrypts the data while the other key decrypts it. This means that only authorized people will be able to read the encrypted files and no one else can see them. In this article, we’ll take a look at how asymmetric encryption works and how you can use it to encode your files in easily understandable steps.
One of the most important tasks a computer user can perform is securing their files. But, how do you know what the best way to encrypt your files is? Asymmetric encryption is one of the safest and most secure methods of encrypting your files. It’s easy to use, and it’s not expensive to set up. This blog post will teach you everything you need to know about asymmetric encryption so that you can keep your data safe.


In the previous tutorial https://acsharmablog.com/2018/07/02/quantum-computing-for-newbies/ ,we saw the evolution of quantum computing and find out that every particle at subatomic level behaves like a wave ,so if a single particle can carries all the information which is encoded in entire spectrum of wave ,then it’s amazing ,then it seriously looks like that quantum computing can give us exponential speed up on traditional computing.
Now the next set of questions arises –
- How to measure the wave function of a particle
- How to control the wave function of a particle
- How to measure the superimposed wave function of two particles
To understand all this let’s revisit the double slit experiment once more .
As we have see before ,that when researchers send one electron in double slit experiment ,it creates a diffraction pattern on the other wall .But that sound’s weird because light creates diffraction pattern on the other wall ,because some light waves are blocked by the wall ,but some light waves passes through the two holes simultaneously and after passing from the these two holes interact with each other and these two light waves either cancel out each other(destructive interference) ,where the phase of the light waves will be opposite or creates a more stronger intensity light wave (constructive interference)where phase of these light waves will be same ,because of this reason we show this bright and dark spot or striped patterns on the other wall.
So this diffraction pattern was produced by interference of two light waves ,but in double slit experiment of electron we are sending only one electron at a time ,so if it is still creating the diffraction pattern ,it means it is passing from both the slits simultaneously that’s weird huh.
Anyway to measure this weird phenomenon ,scientist put the detector on both the slits ,so if one electron passes through both the slits simultaneously ,then detectors should be able to detect it
Now boom!!!!! ,that didn’t happen ,as soon as scientist put the detectors on both the slits ,electron started behaving like other particles like(tennis ball) ,diffraction or striped pattern disappeared ,instead now electron created brightest points on the portion of wall which was just behind the slit or in other words ,same phenomenon which was experienced with tennis balls .Read this https://acsharmablog.com/2018/07/02/quantum-computing-for-newbies/ first to understand it better.
Very good video on this – Quantum Mechanics: Animation explaining quantum physics
Wow ,so now when we try to measure the wave function of electron ,its wave function collapsed ,that’s really spooky
But doesn’t it makes this all the more complicated
Because to know the information carried by a particle ,we need to know its wave function ,but we can’t do that ,because electron exhibits wave like properties only when we don’t measure it
Then how we will utilize this wave like behavior of electron for exponential speed up on traditional computers.
As mentioned in previous blog ,Wave function of a matter wave is defined by below equation
where V=V(r,t)=r and t are the position vector and time respectively,
Ђ=h/2π where h is momentum
So basically to define the wave function of a matter wave we need momentum and position at different time steps
And how do we measure the position of an object ,we check it time to time and if object is displaced from position x to position y in time t ,then we know its velocity ,and once we know its velocity we can calculate its momentum as well .
But in case of sub atomic level ,its not that easy ,position and momentum of an electron or photon or any particle at sub atomic level are conjugate with each other ,means we can’t measure both position and momentum of a particle with complete precision .How much inaccuracy will be there in this measurement is denoted by heisgenberg uncertainty principal.
As per that principal ,the product of variance of measurement in position ϭx and variance in measurement of momentum ϭy should be greater than planck’s constant ђ/2
But this looks weird ,why we can’t measure the position and momentum both precisely at subatomic level ?We can do it very precisely for bigger objects like trains,cars etc.
To understand this ,first let’s see what’s the very basic step in calculating position and momentum of bigger object’s –
To measure position and momentum of these object ,we need to see these objects and how we will see them ,we will throw some light on it ,that’s where the catch is 😊
A light ray contains multitude of photons ,so if we will throw light on a single electron ,to see it clearly ,it will displace it right ? ,its like that – to measure the position of a small stone ,we are throwing a very big stone ,this big stone will displace the smaller stone ,so if we want to correctly measure the position of smaller stone ,we need to throw a smaller stone right ?
In case of light ,smaller stone means ,we need to throw a light ,which comprises only one photon ,now light with one photon means very dim light ,in this light you can’t see the position of electron very clearly ,you will get to know that electron present in this region ,but you won’t know ,that where exactly it present , and if you want to measure the position correctly ,you have a throw a light with more number of photons ,but this light will displace the electron from its position a little bit ,so because of this displacement ,we won’t be able to calculate the momentum correctly .
That’s why this uncertainty constraint ,so basically we can measure the position and momentum of a particle at sub atomic level ,but with this uncertainty threshold ,and because position and momentum is error prone ,then the wave function which is dependent on this ,will also be error prone .
I hope all of you now must have some understanding of heisenberg uncertainty principle.To understand the concept visually ,Please refer this- https://www.youtube.com/watch?v=qwt6wUUD2QI
I hope till now you all have understood the wave function of an electron ,uncertainty associated with measurement of position and momentum of subatomic particle ,and the biggest thing ,if we will try to measure an electron its wave function collapses ,it starts behaving like a normal particle .
So if we want to use electron wave function ,to store spectrum of information simultaneously ,what do we need ?
- We should be able to measure the wave function without collapsing it
- We should know that how much error prone this measurement is (because of heisenberg uncertanity) and what are the counter measures to correct this error
- And the most important question ,how we are going to make a qubit
In next tutorial ,we will explore possible answers of these striking questions.


The Great Questions of Philosophy and Physics
01: Does Physics Make Philosophy Superfluous?
Trace the growth of physics from philosophy, as questions about the nature of reality got rigorous answers starting in the Scientific Revolution. Then see how the philosophy of physics was energized by a movement called logical positivism in the early 20th century in response to Einstein’s theory of relativity. Though logical positivism failed, it spurred new philosophical ideas and approaches.
02: Why Mathematics Works So Well with Physics
Physics is a mathematical science. But why should manipulating numbers give insight into how the world works? This question was famously posed by physicist Eugene Wigner in his 1960 paper, “The Unreasonable Effectiveness of Mathematics in the Natural Sciences.” Explore proposed answers, including Max Tegmark’s assertion that the world is, in fact, a mathematical system.
03: Can Physics Explain Reality?
If the point of physics is to explain reality, then what counts as an explanation? Starting here, Professor Gimbel goes deeper to probe what makes some explanations scientific and whether physics actually explains anything. Along the way, he explores Bertrand Russell’s rejection of the notion of cause, Carl Hempel’s account of explanation, and Nancy Cartwright’s skepticism about scientific truth.
04: The Reality of Einstein’s Space
What’s left when you take all the matter and energy out of space? Either something or nothing. Newton believed the former; his rival, Leibniz, believed the latter. Assess arguments for both views, and then see how Einstein was influenced by Leibniz’s relational picture of space to invent his special theory of relativity. Einstein’s further work on relativity led him to a startlingly new conception of space.
05: The Nature of Einstein’s Time
Consider the weirdness of time: The laws of physics are time reversable, but we never see time running backwards. Theorists have proposed that the direction of time is connected to the order of the early universe and even that time is an illusion. See how Einstein deepened the mystery with his theory of relativity, which predicts time dilation and the surprising possibility of time travel.
06: The Beginning of Time
Professor Gimbel continues his exploration of time by winding back the clock. Was there a beginning to time? Einstein’s initial equations of general relativity predicted a dynamic universe, one that might have expanded from an initial moment. Einstein discarded this idea, but since then evidence has mounted for a “Big Bang.” Is it sensible to ask what caused the Big Bang and what happened before?
07: Are Atoms Real?
Compare proof for the reality of atoms with evidence for the existence of Santa Claus. Both are problematic hypotheses! Trace the history of atomic theory and the philosophical resistance to it. End with Bas van Fraassen’s idea of “constructive empiricism,” which holds that successful theories ought only to be empirically adequate since we can never know with certainty what is real.
08: Quantum States: Neither True nor False?
Enter the quantum world, where traditional philosophical logic breaks down. First, explore the roots of quantum theory and how scientists gradually uncovered its surpassing strangeness. Clear up the meaning of the Heisenberg uncertainty principle, which is a metaphysical claim, not an epistemological one. Finally, delve into John von Neumann’s revolutionary quantum logic, working out an example.
09: Waves, Particles, and Quantum Entanglement
Quantum mechanics rests on an apparent category mistake: Light can’t be both a wave and a particle, yet that’s what theory and experiments show. Analyze this puzzle from the realist and empiricist points of view. Then explore philosopher Arthur Fine’s “natural ontological attitude,” which reconciles realism and antirealism by demonstrating how they rely on different conceptions of truth.
10: Wanted Dead and Alive: Schrödinger's Cat
The most famous paradox of quantum theory is the thought experiment showing that a cat under certain experimental conditions must be both dead and alive. Explore four proposed solutions to this conundrum, known as the measurement problem: the hidden-variable view, the Copenhagen interpretation, the idea that the human mind “collapses” a quantum state, and the many-worlds interpretation.
11: The Dream of Grand Unification
After the dust settled from the quantum revolution, physics was left with two fundamental theories: the standard model of particle physics for quantum phenomena and general relativity for gravitational interactions. Follow the quest for a grand unified theory that incorporates both. Armed with Karl Popper’s demarcation criteria, see how unifying ideas such as string theory fall short.
12: The Physics of God
The laws of physics have been invoked on both sides of the debate over the existence of God. Professor Gimbel closes the course by tracing the history of this dispute, from Newton’s belief in a Creator to today’s discussion of the “fine-tuning” of nature’s constants and whether God is responsible. Such big questions in physics inevitably bring us back to the roots of physics: philosophy.


What the heck is a quantum network?
Today's supercomputers could one day provide ultra-secure encryption.
While it’s currently possible to send encrypted messages with apps like Signal, no system is completely unhackable. But one day, encryption could be much, much harder to crack—thanks to networks that take advantage of quantum mechanics, the esoteric branch of physics that governs the universe at the tiniest scales.
You’re almost certainly reading this story on an electronic device that operates, at its most basic level, with bits built from silicon-based transistors. In the non-quantum world, what scientists call the “classical” world, each of those bits holds a single number: a zero or a one.
Quantum devices use their own quantum bits, or “qubits” (pronounced like “Q-bits”), which play by the rules of quantum mechanics. That allows qubits to act in weird and wondrous ways. A qubit can, for instance, hold both zero and one at the same time.
A quantum network can transmit these curious qubits: for example, photons, which scientists can send through the fiber-optic lines that underpin the classical internet.
These networks, still in the experimental stage, serve to link quantum devices together. “Now that quantum computers are really starting to be built, people are really starting to think more seriously about networking them,” says Christoph Simon, a researcher specializing in quantum optics at the University of Calgary.
It’s already hard to build a quantum computer, and it’s even harder to make quantum computers bigger. “So one way to scale up the processing power would be to entangle several networked quantum computers to create a single ‘super’ quantum computer,” says Oliver Slattery, a physicist at the National Institute of Standards and Technology.
But the original (and best-known) use of quantum networks is to create connections that are—in theory—far more inscrutable than anything on the highly fallible classical internet.
These hyper-secure connections take advantage of a principle called quantum entanglement. Simply put, you can create particles that are “entangled.” If you then observe the state of one of them, you’ll affect the state of its entangled partner, no matter how far away that other particle is.
You can use that to encrypt information. Suppose you want to send a message to your spy friend in the next city over. The both of you would each receive one of a pair of entangled photons. Measuring those photons’ states would give both you and your colleague a unique key, which you could use to encrypt a message, and which your friend could in turn use to decrypt it.
If somebody tried to tap in for the key, that very act would influence the photons, and you’d know. “You can’t eavesdrop and make measurements on the channel without people being able to detect that,” says Nathalie de Leon, a professor of electrical and computer engineering at Princeton University. “Also, you can’t just intercept and copy the information.”
You can’t copy a qubit thanks to another quantum quirk called the “no-cloning principle.” But that very principle is also a quantum network’s fatal flaw. If you send a qubit down a line, then it can only go so far before it fades. In the classical internet, you can simply forward that information along. But that won’t fly in the quantum world, because you can’t copy a qubit.
As a result, current quantum networks can only send qubits a few kilometers away. That means if you send qubits through fiber right now, you can’t do it at a scale larger than a city.
“Being able to do anything at longer distances requires fundamentally new technologies,” says de Leon. There are shortcuts, but those aren’t necessarily secure. They’re like relaying your message via middlemen—and you can’t always trust middlemen.
It’s also possible to avoid fiber entirely and send a qubit across what researchers call “free space”—literally the open air. It’s like flashing a light from one mountaintop to another. You need to physically see the other side, making it impractical for most cases. And it’s prone to atmospheric interference.
But it does work in the vacuum of space. That’s what allowed the Chinese satellite QUESS to “teleport” a qubit from orbit to the ground in 2017. It’s slow and not especially efficient, but the scientists behind QUESS (and the Chinese government) hope that the technology could form the basis for a quantum satellite network.
As impressive as the accomplishment is, de Leon says it builds on existing work. “It was a very important demonstration … and I think we do learn a lot as a community,” she says. “But everything that they did, you could have written down ten years ago, fifteen years ago.”
Still, that’s where some scientists are turning their attention, building ground stations to receive qubits from space. QUESS soon won’t be alone: Another satellite, QEYSSat, will be stewarded by a number of scientists from Canadian institutions, including Christoph Simon.
“We are in the process of determining what’s possible and reasonable,” says Simon. “Frankly, we are thinking about the next [satellite].”
So could all these links eventually evolve into a “quantum internet”? After all, today’s classical internet began as a fledgling network of connections spindled between labs and universities.
There’s a fair distance to go before that can happen, and more than a few technical conundrums to overcome along the way. Quantum computers need to run at ultra-cold temperatures, for instance, barely above absolute zero. But most fiber-optic cables don’t run at ultra-cold temperatures. So any linkage between the two needs to overcome the temperature difference.
But perhaps the biggest challenge is that nobody agrees on what to actually build a quantum network from. Today’s quantum networks largely use relatively simplistic equipment. Moving forward, scientists are trying to build more sophisticated nodes that could use quantum trickery, get around the no-cloning principle, and make longer quantum networks.
“We haven’t … identified the thing that’s like the silicon-based transistor,” says de Leon.
Some researchers want to read qubits by trapping them in rubidium vapor. Others want to do something similar with a cage of magnets and lasers. De Leon’s group wants to use something (literally) brilliant: diamonds. A type of imperfection in diamonds called the “nitrogen-vacancy center” can act as a sort of quantum memory.
“The basic unit is still up for grabs,” says de Leon.
Until fundamental issues like these are sorted out, then quantum networks will, for the most part, remain lab-bound. And as curious as quantum networks might be, it’s unlikely they’ll fully replace the Internet anytime soon.
“It is almost certain that classical networks will need to run alongside quantum networks to make them usable in a practical sense,” says Slattery.


Before quantum computing and self-driving cars, a different kind of cutting edge was sweeping the world: metal smithing. To ancient people living over 6,000 years ago, mining raw metal from the Earth and carefully melting it to craft into currency, tools, and even ornate ritualistic objects, was the height of innovation.
In a recent study in the Journal of Archaeological Science: Reports, scientists describe an archaeological site that may have been the first place in the world to host this technology's secret sauce — a furnace.
Chemical analysis of remnants at an ancient copper-smelting site in Israel points to a two-stage crafting process for metal objects. Not only that, but the site appears to have used copper ore from mines located over 60 miles away.
The combined evidence of an elaborate supply network and the specialized, multi-step process is a testament to the importance of this ancient, cutting-edge technology, the researchers say.
Melting metal is no easy feat. Lead researcher on the study and professor of archeology at Tel Aviv University Erez Ben-Yosef said in a statement the reality is an incredibly delicate and precise process that requires serious skill.
"It's important to understand that the refining of copper was the high-tech of that period. There was no technology more sophisticated than that in the whole of the ancient world," Ben-Yosef says. "Tossing lumps of ore into a fire will get you nowhere. You need certain knowledge for building special furnaces that can reach very high temperatures while maintaining low levels of oxygen."
Older studies suggest people living some 6,000 years ago in what is now the Middle East used clay crucibles — which resemble vases — for smelting copper ore. But when archaeologists excavated this site in 2017, they found evidence of a different kind of technology: a small furnace made of tin and clay.
"This provides very early evidence for the use of furnaces in metallurgy and it raises the possibility that the furnace was invented in this region," said Ben-Yosef.
Reconstructing history — The archeologists first conducted a chemical analysis on uncovered remnants of the site's metal works using a portable X-ray instrument. After studying 14 crucible and 18 furnace fragments, as well as metallurgy's glass-like byproduct, 'slag,' the team retraced these ancient innovator's steps to imagine what their process would've looked like.
In the study, they describe there was likely a two-step metal-smithing process that began with melting ore in a clay-lined, pit furnace, and then scraping it into a smaller crucible to be remelted. Finally it would be poured into a sand-based mold in the ground to cool and form transportable lumps.
The irregularity of these final forms and the lack of other casting remnants led researchers to believe that this site was not constructing objects themselves, but instead processing the metal for other communities to use.
In addition to the copper found, the team also found reoccurring signatures of phosphorous, which they think may have come from burnt bones. While there isn't enough evidence to know for sure, the researchers write that it's possible an animal sacrifice was made during the smelting process as a form of organic fuel.
The analyses also reveal the site used an ore found more than 60 miles away, in what is now the Jordan Valley. In future centuries, these smelting sites and mines would move closer together for practical and economic reasons, but the researchers write that the more ancient, long-distance network uncovered here is further evidence that the process of smelting these metals was highly specialized and safe-guarded by each community — like a secret family recipe, or how a tech company protects its intellectual property with NDAs.
"At the beginning of the metallurgical revolution, the secret of metalworking was kept by guilds of experts. All over the world, we see metalworkers' quarters within Chalcolithic settlements, like the neighborhood we found in Beer Sheva," explains Ben-Yosef.
A first... or not? — The evidence suggests this Israeli site may be one of the first in the ancient world to begin using a furnace for copper smelting. But the technology may have been invented and used around the same time in neighboring regions, Ben-Yosef says. Nevertheless, the discovery cements a place in history for this community as an ancient, "technological powerhouse," he adds.
"[T]here is no doubt that ancient Beer Sheva played an important role in advancing the global metal revolution," he says.
Abstract: Recent discoveries at Horvat Beter (Beersheva, Israel) shed new light on the earliest phase of Southern Levantine metallurgy (second half of the 5th millennium BCE). Multiple fragments of furnaces, crucibles and slag were excavated, and found to represent an extensive copper smelting workshop located within a distinct quarter of a settlement. Typological and chemical analyses revealed a two-stage technology (furnace-based primary smelting followed by melting/refining in crucibles), and lead isotope analysis indicated that the ore originated exclusively from Wadi Faynan (MBS Formation), more than 100 km away. These observations strengthen previous suggestions that metallurgy in this region started with furnace-based technology (possibly not locally invented). Furthermore, the absence of any artifact related to the contemporary industry of copper-based alloys indicates a high degree of craft specialization, and together with other regional observations testifies to the important role of metallurgy in the society of the Beer-sheba Valley during this formative time.


Elliptic Curve Cryptography
Cryptography has been used for many centuries now. One of the most well-known and a rather old cryptographic cipher is the Caesar cipher.
There are two main different types of encryption - symmetric encryption, which uses one key to both encrypt and decrypt (e.g. AES), and asymmetric encryption, which uses two different keys (e.g. RSA). These are often called a public and private key, where the private key is not to be disclosed. RSA uses integer factorization cryptography based on algebraic number theory, while elliptic curve cryptography (ECC) uses integer factorization cryptography based on elliptic curves. In this post, we will delve deeper into ECC and discuss an application of it, Elliptic-Curve Diffie-Hellman (ECDH).
Elliptic Curve Cryptography is a choice for public-key-cryptography, based on elliptic curves over finite fields.
What are elliptic curves?
Let K be a field with characteristic not equal to 2 or 3. Let a, b ∈ K with 4a3 + 27b2 ≠ 0. Then the elliptic curve over K is represented by the Weierstrass equation:
We are interested in the case K = Fp where p is prime. The set of points of such an elliptic curve over K is the collection of ordered pairs (x,y) with coordinates in K and x, y satisfying the equation (1) plus an additional point called the point at infinity or zero point.
Lets have look at an example of a random elliptic curve in the following Figure.
An elliptic curve E has the crucial property that we can define as the addition of two points on the elliptic curve so that we obtain a third point also being on the curve. This happens in the following way: Let P and Q be Points on E. To add these two points together, we pass a line through them. Through reflection of the intersection of the line with E, we get a third point R = (P+Q). The idea behind the described group operation is that the three points P, Q, -R lie on a common line with the same slope and the points, which are the reflection of each other over the z-axis are considered to add up to be zero (see the previous Figure). EC addition is described very nicely in the original Lenstra paper "Factoring integers with elliptic curves". A very nice tool to experiment with the mathematical properties of elliptic curves can also be found here. With the addition of two points we can define the multiplication kP with k a positive integer and P a point obtained through add P k times to itself, as example 2P = P + P.
Note that there are a multitude of curves available, and picking one can make a difference. Some of the published curves include Curve25519, Curve448, P-256, P-384, and P-521, with P-256 being the most popular one, followed by Curve25519 (which promises to be faster than P-256). There is also the major difference that Curve25519 (and its selection of parameters) is documented openly, while P-256, which is published by NIST, is not. This is likely one of the reasons that SSH has adopted Curve25519 as its curve.
But how does Elliptic Curve Cryptography work exactly?
Using the operations defined in the previous paragraph, a key exchange method based on Elliptic Curves can be devised. Specifically, we will take a look at Elliptic-Curve Diffie-Hellman (ECDH) next. Note that, similarly to standard Diffie-Hellman, while ECDH protects against passive attacks such as eavesdropping, it does not protect against active ones such as a Man-in-the-Middle attack. To devise a secure key exchange, additional measures such as authentication are required. Now, the following steps are needed to securely exchange a key between two parties Alice and Bob (with Cathy being an adversary that eavesdrops) without having a pre-shared key:
Alice, Bob and Cathy agree on a public elliptic curve and a public fixed curve point G.
Alice picks a private random integer α. From now on, α is her private key.
Now Alice computes her public key. That is the curve point A = αG. She publishes her public key.
Bob picks a private random integer β. β is his private key.
Now Bob computes his public key. His public key is the curve point B = βG. He also publishes his public key.
Cathy does the same way.
Now we suppose Alice wants to send Bob a message.
Alice can simply compute P = αB = α(βG) and uses P as the private key for the conversation.
Bob can simply compute P =βA = β(αG) and uses P as the private key for the conversation.
You see αB = α(βG) = β(αG) = βA, so just Alice and Bob know the private key P for their conversation.
Suppose Cathy wants to read the conversation between Alice and Bob.
She knows the elliptic curve, the point G, the order of G and the public keys A and B from Alice and Bob. What she does not know are the private keys - she would have to compute the private key P to do so.
The Figure below illustrates the steps above and serves as a graphical aid.
The security of this method (against passive attacks) is believed to be adequate for current computers (Haakegard et al.), however, it is also believed that Quantum Computing could render ECC insecure (Roetteler et al.).
Advantages of ECC
For one, key bit length plays an important role - the key lengths of ECC keys are much smaller than the ones of RSA, given the same security level is required. ECC also arguably offers a largely better performance, with ECC-512 (comparable to RSA-15360) being up to 400 times as fast as RSA for both encryption and decryption, as per Lauter.
In summary, ECC is a very interesting method - as a matter of fact, Github uses ECC keys in their documentation examples (e.g. when generating SSH keys) due to its performance.
*Figures are not representative of a product, and were made by the author.
Lenstra, "Factoring integers with elliptic curves"
Lauter, "The advantages of elliptic curve cryptography for wireless security"
"Faktorisierung großer Zahlen"
Haakegard et al., "The Elliptic Curve Diffie-Hellman (ECDH)"
Roetteler et al., "Quantum resource estimates for computing elliptic curve discrete logarithms"


What Is Neuromorphic Computing?
There are a number of types and styles of artificial intelligence, but there's a key difference between the branch of programming that looks for interesting solutions to pertinent problems and the branch of science seeking to model and simulate the functions of the human brain. Neuromorphic computing, which includes the production and use of neural networks, deals with proving the efficacy of any concept of how the brain performs its functions -- not just reaching decisions, but memorizing information and even deducing facts.
Both literally and practically, "neuromorphic" means "taking the form of the brain." The keyword here is "form," mainly because so much of AI research deals with simulating or at least mimicking, the function of the brain. The engineering of a neuromorphic device involves the development of components whose functions are analogous to parts of the brain, or at least to what such parts are believed to do. These components are not brain-shaped, of course, yet like the valves of an artificial heart, they do fulfill the roles of their organic counterparts. Some architectures go so far as to model the brain's perceived plasticity (its ability to modify its own form to suit its function) by provisioning new components based on the needs of the tasks they're currently running.
The first generation of AI was rules-based and emulated classical logic to draw reasoned conclusions within a specific, narrowly defined problem domain. It was well suited to monitoring processes and improving efficiency, for example. The second, current generation is largely concerned with sensing and perception, such as using deep-learning networks to analyze the contents of a video frame.
A coming next generation will extend AI into areas that correspond to human cognition, such as interpretation and autonomous adaptation. This is critical to overcoming the so-called “brittleness” of AI solutions based on neural network training and inference, which depend on literal, deterministic views of events that lack context and commonsense understanding. Next-generation AI must be able to address novel situations and abstraction to automate ordinary human activities.
Neuromorphic Computing Research Focus
The key challenges in neuromorphic research are matching a human's flexibility, and the ability to learn from unstructured stimuli with the energy efficiency of the human brain. The computational building blocks within neuromorphic computing systems are logically analogous to neurons. Spiking neural networks (SNNs) are a novel model for arranging those elements to emulate natural neural networks that exist in biological brains.
Each “neuron” in the SNN can fire independently of the others, and doing so, it sends pulsed signals to other neurons in the network that directly change the electrical states of those neurons. By encoding information within the signals themselves and their timing, SNNs simulate natural learning processes by dynamically remapping the synapses between artificial neurons in response to stimuli.
While building such a device may inform us about how the mind works, or at least reveal certain ways in which it doesn't, the actual goal of such an endeavor is to produce a machine that can "learn" from its inputs in ways that a digital computer component may not be able to. The payoff could be an entirely new class of machine capable of being "trained" to recognize patterns using far, far fewer inputs than a digital neural network would require.
"One of the most appealing attributes of these neural networks is their portability to low-power neuromorphic hardware," reads a September 2018 IBM neuromorphic patent application [PDF], "which can be deployed in mobile devices and native sensors that can operate at extremely low power requirements in real-time. Neuromorphic computing demonstrates an unprecedented low-power computation substrate that can be used in many applications."
Although Google has been a leader in recent years, of both research and production of hardware called tensor processors (TPU) dedicated specifically to neural network-based applications, the neuromorphic branch is an altogether different beast. Specifically, it's not about the evaluation of any set of data in terms of discrete numeric values, such as scales from 1 to 10, or percentage grades from 0 to 100. Its practitioners have a goal in mind other than to solve an equation, or simply to produce more software. They seek to produce a cognition machine -- one that may lead credence to, if not altogether prove, a rational theory for how the human mind may work. They're not out to capture the king in six moves. They're in this to build mechanisms.
The Future Of Neuromorphic Computing
At any one time in history, there is a theoretical limit to the processing power of a supercomputer -- a point after which increasing the workload yields no more, or no better, results. That limit has been shoved forward in fits and starts with advances in microprocessors, including by the introduction of GPUs (formerly just graphics processors) and Google's design for TPUs. But there may be a limit to the limit's extension, as Moore's Law only works when physics gives you room to scale smaller.
Neuromorphic engineering points to the possibility, if not yet probability, of a massive leap forward in performance, by way of radical alteration of what it means to infer information from data. Like quantum computing, it relies upon a force of nature we don't yet comprehend: In this case, the informational power of noise. If all the research pays off, supercomputers, as we perceive them today, maybe rendered entirely obsolete in a few short years, replaced by servers with synthetic, self-assembling neurons that can be tucked into hallway closets, freeing up the space consumed by mega-scale data centers for, say, solar power generators.
Examples of neuromorphic engineering projects
Today, there are several academic and commercial experiments underway to produce working, reproducible neuromorphic models, including:
- SpiNNaker [pictured above] is a low-grade supercomputer developed by engineers with Germany's Jülich Research Centre's Institute of Neuroscience and Medicine, working with the UK's Advanced Processor Technologies Group at the University of Manchester. Its job is to simulate the functions so-called cortical microcircuits, albeit on a slower time scale than they would presumably function when manufactured. In August 2018, Spinnaker conducted what is believed to be the largest neural network simulation to date, involving about 80,000 neurons connected by some 300 million synapses.
- Intel is experimenting with what it describes as a neuromorphic chip architecture, called Loihi (lo · EE · hee). Intel has been reluctant to share images that would reveal elements of Loihi's architecture, though based on what information we do have, Loihi would be producible using a form of the same 14 nm lithography techniques Intel and others employ today. First announced in September 2017, and officially premiered the following January at CES 2018 by then-CEO Brian Krzanich, Loihi's microcode include statements designed specifically for training a neural net. It's designed to implement a spiking neural network (SNN), whose model adds more brain-like characteristics.
- IBM maintains a Neuromorphic Devices and Architectures Project involved with new experiments in analog computation. In a research paper, the IBM team demonstrated how its non-volatile phase-change memory (PCM) accelerated the feedback or backpropagation algorithm associated with neural nets. These researchers are now at work determining whether PCM can be utilized in modeling synthetic synapses, replacing the static RAM-based arrays used in its earlier TrueNorth and NeuroGrid designs (which were not neuromorphic).


The ISS houses many ground-breaking experiments which can only be performed in space, and the ice chest-sized Cold Atom Laboratory (CAL) is officially the ‘coolest’ suite of instruments, studying hyper-cold atoms and exploring physics at the atomic scale. Here, Emma Holling discusses the creation of Bose-Einstein Condensate in the Cold Atom Laboratory and the future applications of CAL’s research.
The Cold Atom Laboratory is a facility on the International Space Station (ISS) which utilises the unique microgravity environment in space. In July 2018, scientists used the lab to produce Bose-Einstein condensate in orbit for the first time, and in 2020 the lab was upgraded for further research.
As its name suggests, the Cold Atom Laboratory cools atoms down to around 200 nano-Kelvin by three key processes. First, a magneto-optical trap holds atoms in place, while radiation and pressure from lasers slows them down. Six lasers are focussed on the atoms, meaning whichever way the atom shifts, it will have a stream of photons exerting a force to slow it down.
While this process cools atoms to a fraction of a degree above kelvin, for Bose-Einstein Condensates to form, further cooling is required. This comes in the form of evaporative cooling, which holds the atoms so they vibrate in place, allowing the highest energy atoms to be removed – almost like siphoning off the hottest atoms. The final stage is adiabatic expansion, where the remaining atoms are allowed to expand out by reducing the strength of the magnetic field holding them.
Bose-Einstein condensate is an intriguing state of matter where supercooled bosons are in a single quantum state with the same low energy level. The group of bosons acts in a wave-like fashion (owing to the correlation of the particles), with the same wave function meaning the matter waves are coherent; with multiple condensates it is possible to observe interference, generating a clear series of minima and maxima. Perhaps more importantly, quantum effects can be observed on a much larger scale than usual, a vital discovery we will explore later.
Interference patterns observed in Bose-Einstein condensate. Credit: Lachmann et al, Nat Commun, 2021
Credit: McGraw-Hill Concise Encyclopedia of Physics
One quantum effect is quantum tunnelling. Part of the condensate is able to overcome physical barriers, something impossible were the atoms only to obey classical mechanics. Linked to this is the Josephson-Effect, which is when an electric current is able to flow from one part of a superconductor to another passing through an insulator. The current is able to do this due to a ‘weak link’ between the macroscopic quantum objects, giving a fraction of the condensate the ability to tunnel through, but not allowing a particle in the classical sense to as the weak link’s barrier is too high.
While Bose-Einstein condensate was proposed nearly a century ago, it took 80 years for a Nobel Prize winning project to form one. Creation can be performed on Earth, but gravity means that the atoms fall out of place almost immediately. However, on the ISS, the microgravity environment allows scientists to observe the condensate for over a second rather than fractions of one - as the freefall of the atoms is indefinitely long unlike on Earth where there are gravity constraints, causing the condensate to be shifted within its formation chamber. Bose-Einstein condensate can also be created in seconds, giving physicists more opportunities to perform repeats and adjust their experiments.
Further research into the quantum effects demonstrated by Bose-Einstein Condensate is vital to the progression of quantum computing, and so too is the development of the Cold Atom Laboratory. Quantum computers use qubits for calculations, and investigations into different systems for their creation are currently underway. One of the biggest challenges is maintaining coherence, as quantum states are very sensitive to their environments.
Today, information is stored on computers using macroscopic objects, and while it was initially thought that this would be impossible for quantum computers, owing to quantum effects often disappearing for larger objects, Bose-Einstein condensate may defy this thinking. As quantum effects can be seen on a macro level in Bose Einstein, it may be possible to encode the condensate and then use it in quantum computing to create qubits. The next step is to discover the feasibility of encoding the condensate, and if successful this could open up a world of possibility for technological advancement.
To allow for advanced research, in 2019 the laboratory was upgraded to include an interferometer which significantly increases the Cold Atom Lab’s abilities. Astronauts Christina Koch and Jessica Meir removed the old Science Module and upgraded it by connecting 11 fibre optic cables to the new module. The wire cores were thinner than a human hair, and if snapped or scratched, this could have ended the mission. The incredible astronauts completed this precision mission successfully.
Now with the interferometer, not only can atoms be supercooled and observed on a microscopic scale, but the waves can be split and recombined, allowing for cutting-edge research into the fundamental physics of the universe.
To do this the Bose-Einstein condensate is irradiated, separating the atoms and then allowing them to come back together and superpose. The interference pattern is clearly visible, owing to the coherence of the waves, and with some adjustment there is hope that physicists will be able to measure the effects of gravitational waves to a high level of precision by measuring interesting disruptions to the condensate.
About the author
Emma Holling is a UK student passionate about helping people see the value of the space industry and their place within it. When she’s not studying for a physics degree, Emma works with schools; produces a variety of online content (including webinars and podcasts); and is an Outreach Ambassador with New Voices in Space.
This is the third monthly article in our ‘New Voices in Space’ series authored by young scientists and engineers involved in the space business. The first two articles are Taking quantum into space by Sonali Mohaptra and NASA’s women of inspiration by Mansi Joshi.


It is a peculiar thing to see, but more and more commonly terms of art make their way into the mainstream media. It seems that every week a new article about a vulnerability, cyberattack, or data breach makes its way into public discourse. One phrase used to give confidence in a strong encryption scheme is “256-bit encryption”, but what does this mean?
- What is Encryption?
- What is a Key Size?
- How Strong is 256-bit Encryption?
- But what if the hardware gets better?
What is Encryption?
Encryption is the practice of taking a message, referred to as “plain-text” and applying a series of transformations to produce “cipher-text”. This cipher-text is only readable by someone who can reverse this process, turning the cipher-text back into plain-text. Cipher-text is very portable – it can be safely sent via an insecure channel such as the internet without worrying about the contents of the message being intercepted by prying eyes. Broadly speaking, encryption falls into two major buckets: symmetric encryption, and asymmetric encryption.
Symmetric encryption refers to an encryption algorithm which relies on both parties being privy to the same encryption key – it is used both to encrypt and decrypt the message.
Asymmetric encryption on the other hand utilizes public/private keypairs. In this kind of encryption, both parties have a public key and a private key which are intrinsically, mathematically linked: that which can be encrypted via the public key can be reversed via the private key (and vice versa). Symmetric cryptography is much faster, but requires two parties to have communicated the key in advance via another channel. Asymmetric cryptography is slower, but can be performed without a prior exchange of information. SSL/TLS, the protocol most responsible for securing the internet, uses a mix of symmetric and asymmetric cryptography in order to get the best of both worlds.
What is a “Key Size”?
In cryptography, “key size” refers to the length of the secret key used to encrypt and decrypt information. If I asked you to pick a number 1 thru 4 (integers only!), you’d have a 25% chance of getting it right on your first try. If you got to pick 4 numbers, you’d have 100% chance of getting the right answer. In this way, if an attacker tries every possible key, they will eventually land upon the right one. This is referred to as a “brute force attack”. In order for something to be reasonably secure then, trying every possible key must be infeasible with modern hardware. But what is a bit? Instead of using our base-10 number system, computers rely on binary numbers because electrically, they operate on the presence or absence of current. These 1’s and 0’s are referred to as “bits”, and the number of them in your key is what defines your key-size. With a symmetric encryption key 256 bits long (2 to the 256th power possible combinations!), on current hardware it would take literally millions of years.
How Strong is 256-bit Encryption?
Given that it would take millions of years to try all possible combinations of an AES 256-bit key, what other attacks exist against modern encryption schemes? DES was rendered insecure to a 56-bit key size. As it turns out, not all AES is created equal! AES defines 5 different “modes”, some of which have suffered from rampant implementation flaws over the years. There is nothing preventing something we do not yet know from rendering many implementations of AES insecure in the same manner! Additionally, while infinitesimally unlikely, it is statistically possible for an attacker to guess the right key on the very first try! This is all to say that information security is relative, and it is fluid. AES 256-bit encryption represents the strongest symmetric encryption achievable today, but that is not a guarantee that this won’t change.
But what if the hardware gets better?
It will! over time, hardware improves. Some of you may be familiar with “Moore’s Law”, the tendency for the number of transistors packed into an integrated circuit to double every two years as we more and more clever with our manufacturing capabilities. Much in the same way, the computers of 2021 are staggeringly better than the computers of even as recent as 2005. Should this pace continue, it’s feasible that we eventually need to move away from current encryption methods. Many believe that quantum computing will rapidly accelerate this need. Quantum computers, unlike regular computers, are very good at reversing factorization of large prime numbers (the fact that this is so difficult is the very basis of RSA key generation). Luckily, alternative encryption algorithms already exist. But wait? You might ask, with baited breath. Why not move to those alternatives today? Why not pick keys so obscenely large that we never have to have this conversation again? Ultimately, the longer your key size, the more hardware time is needed to perform the encryption and decryption, and the more power the operation consumes. Ultimately, advances in battery technology have the potential to catapult us forward into uncharted terrain in terms of encryption. As constraints change, and as technology improves, optimizing for security and performance may look very different ten years from now, much like it looked very different ten years ago.
The next time you see them mention encryption in the news, try to figure out if they’re talking about symmetric or asymmetric cryptography. (If you’re wondering what length TLS keys are generally considered to be strong, asymmetric cryptography tends to require much larger key sizes such as 2048 or 4096 bit keys!). Try to determine how many attempts it would take to brute-force the key space. Consider what industry is being discussed and the tradeoffs the company must have made to arrive at the chosen key size (a bank is likely to use a larger key size and value future-proofing its security over performance.)


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


The ubiquitous classical digital computer encodes data in bits (a portmanteau of binary and digits) in either a 0 or 1 state. On the other hand, while a quantum computer also uses 0/1 data representation, these qubits (from quantum and bits), qubit states 0 and 1 can be simultaneously in what is known as a superposition – and a quantum computer can also make use of entanglement. For these reasons, quantum computers can potentially solve problems whose complexity is too resource-intensive for classical computation. That being said, quantum computers are very difficult to construct. Recently, however, scientists at University of Wisconsin, Madison have fabricated a qubit in a silicon double-quantum dot in which the qubit basis states are the singlet state and the spin-zero triplet state of two electrons. (A double quantum dot links two quantum dots – semiconductor nanostructures that confine the motion of conduction band electrons, valence band holes, or excitons in all three spatial directions.) Moreover, the researchers have for the first time integrated a proximal micromagnet, allowing them to create a large local magnetic field difference between the two sides of the quantum dot – thereby greatly increasing their ability to manipulate the qubit without injecting noise that would induce superposition decoherence.
Prof. Susan Coppersmith and Prof. Mark Eriksson discuss the paper they and their co-authors published in Proceedings of the National Academy of Sciences with Phys.org, noting that overall goal of the research program is to develop quantum bits for a quantum computer using technology that is similar to that used for current classical computers. “The advantages of this strategy arise for two main reasons.” Coppersmith tells Phys.org. “First, enormous investments have been made to develop large-scale classical electronics, and one hopes that this investment can be leveraged to facilitate scale-up of quantum electronics. Second, the similarity in technology facilitates integration of quantum and classical processors.” Integration is important, Eriksson adds, because a large-scale classical computer will almost certainly be necessary to control the operation of a quantum computer.
An early step towards this goal is to fabricate high-fidelity individual qubits. This paper focuses on the so-called singlet-triplet qubit, which was first fabricated in gallium arsenide (GaAs) devices. “The operation of a singlet-triplet qubit in GaAs is complicated by strong coupling between the electron spins and nuclear spins, Eriksson explains. “Silicon has much weaker coupling between the electron spins and nuclear spins, and most of the nuclei in silicon have spin zero, so the electron spins in silicon can stay coherent much longer than in GaAs.” In fact, measurements of a singlet-triplet qubit in natural silicon indeed yield much longer coherence times than in GaAs, but because the qubit operations themselves rely on having a magnetic field difference between the dots – a difference that also arises from the nuclei themselves – the qubit operations in that work were much slower than in GaAs. “Our work shows that using an integrated micromagnet enables faster gate operations by imposing a larger magnetic field difference between the quantum dots,” Coppersmith points out, “and it does so without introducing measurable additional decoherence, which improves the overall performance of the qubit.”
Specifically, the paper states that the integrated micromagnet provides a promising path toward fast manipulation in materials with small concentrations of nuclear spins, including both natural silicon (Si) and isotopically enriched 28Si. “Nuclear spins in GaAs and other materials, such as InSb (Indium Antimonide), reduce qubit coherence – but this strong coupling also enables fast manipulation,” Eriksson tells Phys.org. “However, if the decoherence effects are reduced by using a material with weaker coupling to nuclear spins, it’s necessary to find another way to create a large magnetic field difference between the quantum dots – and the integrated micromagnet enables this.”
“One big challenge was fabricating a suitable device, that being a double quantum dot in which a micromagnet is incorporated,” Coppersmith continues. Devices with incorporated micromagnets had previously been investigated in GaAs in a slightly different context, but the fabrication procedure in the University of Wisconsin devices differs from that used in the GaAs devices, requiring novel processes to be developed. “A further challenge arose because the micromagnetic field was somewhat different than what was expected based on measurements of cobalt films and our numerical calculations,” notes Eriksson. “Therefore, to perform the experiments we had to use the properties of the qubit itself to figure out what the actual fields on the quantum dots were.” By so doing, the researchers found that the field from the micromagnet depended on the applied uniform field, which enabled them to investigate the qubit properties for two magnitudes of the micromagnet field.
Interestingly, the paper states that the scientists’ fabrication techniques being similar for both quantum dot-based qubits and donor-based qubits in semiconductors suggests that micromagnets should also be applicable to donor-based spin qubits. “The micromagnet in the device that we measured is created by depositing the metal cobalt by Electron Beam Physical Vapor Deposition (EBPVD), onto the top of the sample,” Coppersmith says. “Therefore, applying the technique to other semiconducting qubit architectures in which the qubits are defined by evaporated metal top gates is rather straightforward.” (EBPVD uses an electron beam to bombard a target and convert some of its atoms into a gas, which then precipitate and coat all surfaces in the vacuum chamber.) In practice, however, some of the gates on these devices will be made of non-magnetic materials – typically aluminum or gold –resulting in a small number of cobalt gates.
The researchers also describe the unique characteristics of a large-scale quantum computer based on their approach: Once high-quality single qubits and two-qubit gates are achieved, then because the technology is close to that already used in classical electronics and the qubit size (< 1µ) is small, scaling up to devices with large numbers of qubits could be feasible. This plausible path to large numbers of qubits has sparked significant interest in electrically-gated qubits in semiconductors.
“The next steps in our research are to increase both the magnitude of the field difference between the quantum dots, and the number of qubits by increasing the number of quantum dots,” Coppersmith tells Phys.org. “Both steps are being implemented in new devices that have been designed and are currently being fabricated. We’re also working on other qubit implementations in silicon quantum dots1,2, all of which use electrical initialization, manipulation and readout, and therefore have the potential advantages of integrability and scalability.” Moreover, Eriksson points out that being able to control local magnetic fields in a nanoelectronic device could be very useful for spintronics.
Learn more here http://www.pnas.org/content/early/2014/07/31/1412230111


Nanoscience may involve manipulation of the smallest materials, but it could have a large impact on the biggest of all issues, climate change. This month, we look at two new battery systems, both aimed at improving on the slow-charging (and sometimes unsafe) lithium-ion batteries that now power our world. We also examine a new technology to compost plastic that leaves nothing—not even microplastics—behind, then take a peek at a living wall (or roof) printed with living algae. Finally, we finish up with an interesting way to solve a quantum computing problem—by emulating a black hole.
Cheaper aluminum batteries shine
A new aluminum-based battery achieves 10,000 error-free recharging cycles while costing less than the conventional lithium-ion batteries now used in everything from smartphones to electric vehicles. The new battery builds on research by Lynden Archer, a member of the Kavli Institute at Cornell for Nanoscale Science and dean of the school’s engineering department. He is studying low-cost batteries that are safer and more environmentally friendly than lithium-ion, which is slow to charge and prone to catching fire. His team built the new battery’s anode and cathode from two readily available elements, aluminum and carbon. Aluminum is trivalent (having three electrons in its outmost shell vs. one for lithium) and lightweight, so it can store more energy per unit mass than many other metals. While aluminum reactions can cause short circuits by reacting with other battery materials, Archer’s group found they could stop this by depositing aluminum on carbon fibers to form a bond strong enough to prevent it from moving to other parts of the battery.
Solid-state battery moves closer to reality
Factorial Energy has unveiled a 40-ampere-hour solid-state battery cell for electric vehicles and other applications. The startup was co-founded by Héctor Abruña, a member of the Kavli Institute at Cornell, and Cornell chemistry Ph.D. graduate Siyu Huang. The new battery is based on an electrolyte that is stable enough for batteries to run high voltages and energy densities without bursting into flames or growing lithium dendrites that damage and degrade lithium-metal anodes. The company claims its battery boosts driving range 20 to 50 percent without sacrificing service life. The company recently added two auto industry heavyweights, Joe Taylor, former chairman/CEO of Panasonic North America, and Dieter Zetsche, former chair of Daimler and head of Mercedes-Benz, to its team, and says several global automotive companies are testing the battery.
Truly compostable plastics
The problem with most “compostable” plastics is that they do not break down in composting facilities or landfills. They are so dense, there is no way for microbes to worm their way in to digest the polymers. Instead, they remain intact or turn into microplastics that do not break down any further and pose environmental risks. Now, Ting Xu, a member of the Kavli Energy NanoScience Institute at UC Berkeley, has found a way around the problem. She has developed a technique to “cocoon” enzymes that eat plastic and incorporate them into the plastic material itself. This is no small feat, since shaping those plastics takes place at 338* F, hot enough to destroy any enzyme. Once incorporated, the enzymes are inactive until exposed to water and moderate heat. It takes just one week to degrade 80 percent of the plastic at room temperature, and less time at higher temperatures. Xu’s technique is a boon for composters and could lead to new ways to incorporate active biomolecules into materials for sensing, decontamination, and self-healing materials.
3D printing an algae roof
Image a roof or wall made of living, photosynthetic materials tough enough to use in real-life settings. That is exactly what Marie-Eve Aubin-Tam, a member of the Kavli Institute of Nanoscience at Delft Technical University, has built by 3D printing living algae into a cellulose matrix. Cellulose is a strong yet flexible material excreted by bacteria that retains its shape even when twisted or crushed. She and her team printed living microalgae onto the cellulose, resulting in a photosynthetic system that can turn carbon dioxide and water into sugar and feed itself for several weeks. One day, she speculates, the living elements of the material could sense and respond to cues in the environment. This work showcases a new way of thinking about materials and could spark new conversations between scientists and designers.
Black holes improve quantum computing
If you have been keeping up with quantum computing, you probably know about qubits. They are quantum bits of information that hold the value of zero, one, or a superposition of zero and one at the same time. With each new qubit entangled, quantum computers gain power and algorithm-crunching ability. Now, meet qutrits. These quantum bits can hold the value of zero, one, two, or a superposition of all three at the same time. That means the size of your computer scales much quicker, so fewer bits do more work. But qutrits are prone to decoherence (when they fall out of entanglement). Irfan Siddiqi, a member of the Kavli Energy NanoScience Institute, may have found a way around the problem—by storing information as if it were in a black hole, where information is scrambled but not destroyed. It is a fascinating exploration of how to use the most advanced science to do the most advanced engineering.