FREEDOM AND SAFETY
Quantum computers are making all the headlines these days, but quantum communication technology may actually be closer to practical implementation. In a bid to hasten its arrival, researchers have now mapped out the path to a quantum internet.
The building blocks for these emerging technologies are more or less the same. They both use qubits to encode information-the quantum equivalent to computer bits that can simultaneously be both 1 and 0 thanks to the phenomena of superposition. And they both rely on entanglement to inextricably link the quantum states of these qubits so that acting on one affects the other.
But while building quantum computers capable of outperforming conventional ones on useful problems will require very large networks of qubits, you only need a handful to build useful communication networks.
And we’re already well on the way. In a review article in Science, researchers from the University of Delft in the Netherlands outlined six phases of development towards a global network of quantum-connected quantum computers and point out that we’re already on the bottom rung of that ladder.
“We are now at an exciting moment in time, akin to the eve of the classical internet,” the researchers wrote. “Recent technological progress now suggests that we may see the first small-scale implementations of quantum networks within the next five years.”
The main advantages of a quantum communication network over a conventional one are speed and security. Entanglement makes it possible to communicate instantly across arbitrarily large distances in principle. No matter how far apart you put two entangled qubits, acting on one will have an instant and measurable impact on the other.
It’s also essentially impossible to eavesdrop on a quantum conversation. Under quantum mechanics, if you read the quantum state of an object it changes that quantum state, which means the act of intercepting any message encoded in quantum states will immediately change the content of the message.
But the same property that makes quantum communication intrinsically secure also poses a major challenge. It means qubits can’t be copied or amplified, two essential ingredients of classical communication systems.
Nonetheless, working quantum “trusted repeater networks” are already in operation, which the researchers identify as the first step on the way to a full quantum internet. These networks feature nodes that can encode and decode qubits, which are then sent across optical cables or potentially beamed down from space by a satellite.
But because quantum signals degrade the further they travel, it’s necessary to pass messages from node to node to cover longer distances. Each of these handovers is secure, but if two distant nodes need to communicate, then all the nodes in between know the content of the message, and so must be trusted if the message is to remain secure.
To reach the next stage we will need to develop reliable quantum repeaters, the researchers said. This is a device that is able to establish entangled qubits with each node and then rely on quantum teleportation to effectively swap entanglements around so that the two nodes are entangled. A network connected by these kinds of repeaters would allow any node to securely communicate with any other without having to trust any of the intermediaries.
At both these stages, the principle use would be quantum key distribution, which allows two nodes to securely share an encryption key in a way that can’t be eavesdropped on, which can then be used to decode encrypted messages sent via conventional communication channels.
The process of entangling distant qubits is hit and miss at the minute, though, so the next stage will be to create a network that’s able to create entanglements on demand. The main advantage of this kind of “entanglement distribution network” is that it will make the network device-independent,according to the researchers.
After that, the development of quantum memory will allow much more complicated communication protocols that require quantum information to be stored while further communication goes on. This is a major challenge, though, because quantum states rapidly degrade through a process called decoherence. Most technology proposals only hold their states for seconds or fractions of a second, which poses problems for a network whose communication times are longer than that.
But if it could be realized, it would make it possible for simple quantum nodes to send computations to a quantum computer on the network, potentially creating a kind of quantum cloud. It could also make it possible to do things like synchronize distant telescopes to create a single “super telescope.”
Ultimately, the goal is to create a network of fully-connected quantum computers. The first phase of that will be a “few-qubit fault-tolerant network,” in which the quantum computers at each node will not yet be large enough to out-do standard computers. Nonetheless, the fact that they incorporate fault tolerance will mean they will carry out relatively complex computation and store quantum data for significant amounts of time.
And the final stage will come when these quantum computers finally surpass their conventional cousins, making it possible to create distributed networks of computers capable of carrying out calculations that were previously impossible, and instantly and securely share them around the world.
The authors noted that there’s a long road ahead. We need better ways of encoding, storing, and transmitting quantum information, and perhaps even more importantly, we need to build quantum equivalents of our internet communication protocols, something almost entirely lacking today.
But they’re bullish that the first multimode quantum networks will be appearing in the next few years, which will make it possible to test all these ideas and hopefully turbocharge development of a true quantum internet.


For many of us, the fast-evolving pace of technology means we are increasingly surrounded by one black box after another. We may have a rudimentary idea of how things work, but not enough to do anything more than understand the instructions.
To really understand, you have to be able to open up the black box, actually see how it works and then put it back together. The latter point is one reason I leave my car engine well alone. The same goes for my laptop – I’m happy to leave that to the techies and the coders.
But even if I was computer savvy, how am I supposed to get my head around the quantum computer revolution heading our way, when it’s impossible to look inside while it’s running?
One of the many quirks of quantum computing is that it relies on the strange interaction of atoms and subatomic particles, and then there’s the small matter that the whole fragile quantum state collapses once you try and look at what’s actually going on.
Seeing quantum computing in action
“A quantum computer is the ultimate black box,” smiles quantum physicist Professor Lloyd Hollenberg who heads the University of Melbourne’s first ever course on quantum computer programming.
He’s smiling because even after just 15 minutes, he is pleased a dullard like me is starting to show some rudimentary understanding of how quantum computing works. And it’s all thanks to a quantum computer simulator he and his colleagues have developed that basically lets you operate a quantum computer... with the lid off.
“To see how a quantum computer works you want to crack it open, but in the process, you collapse the quantum state, so what do you do? Our simulator was designed to solve that problem, and in terms of its ease of use and what it tells you, it’s unique,” says Professor Hollenberg.
There are already opportunities for people to access online a few of the small-scale quantum computers that have so far been developed, but generally programmers will only get back the final output from their coding – they won’t be able to ‘see’ how it works.
It’s this ability to see inside that Professor Hollenberg says is crucial to help students learn by actually doing, and for professionals to debug their quantum code.
qubits and pieces
The simulator – the Quantum User Interface or QUI – is software that lets you click and drag logic instructions that operate on quantum bits (known as qubits) in order to write a program.
A remote cluster of computers at the University runs the program on a simulated quantum computer and sends back the results in real time so the user can inspect and visualise all aspects of the quantum computer’s state at every stage in the program.
A qubit is simply the quantum version of a classical computer ‘bit’ – the basic unit of computer data that exists in one of two states, which in programming we know as 0 or 1. This is the basis of all computer coding, and in a classical computer the 0s or 1s are usually represented by the different voltages that run through its transistor.
But in a quantum computer the bits, or qubits, are quantum objects, like an electron in an atom, which can be in one of two states that we can likewise label for convenience as 0 or 1.
What these quantum objects actually are varies across different quantum computer systems, but for the computer programmer that isn’t so important. What is important is the 0 and 1 generated by the quantum objects enables us to use the objects for coding.
Getting past the weird physics
What’s different about qubits is that because of the weird physics that exists at the atomic scale, each qubit can be in an unresolved ‘quantum superposition’ state of 1 and 0. When observed (which, remember, collapses the quantum state) each will have some probability of being 0 or 1, depending on how the quantum superposition was formed.
Qubits can also be made to influence each other through a property called entanglement so that if a qubit is resolved as 0, another might automatically be 1.
It is these peculiarities of quantum physics that promise to make quantum computers much more powerful than classical computers and, therefore, able to address difficult problems – like optimising complex routes or systems from weather forecasting to finance, designing new materials, or aiding machine learning.
Unlike a classical computer that laboriously computes all possibilities before finding the right answer, a quantum computer uses the wave-like properties of data structures and numbers to narrow down the probability of the correct answer for problems that, theoretically, our current computers have no hope of matching.
For the students accustomed to classic computer programming – it’s like learning from scratch.
“You have to think really differently with quantum programming,” says electrical engineering student Fenella McAndrew, who is doing the course.
“We’re basically going back to the start of the programming process, like working at the level of one circuit in conventional programming.”
And the rules of how numbers are processed in a quantum computer is, for the uninitiated, mind boggling.
“Teaching the material is a challenge, especially without a quantum mechanics background,” says Professor Hollenberg. “But there is a clear demand from students and professionals to learn more about the technology and get themselves ‘quantum ready’.”
It was this need to make quantum computing more accessible to people with no physics background that was the genesis of developing QUI. The system allows programmers to see each phase of the operation and exactly what the quantum computer is doing – in particular how the quantum data is being manipulated to produce the output of the program.
This is critical information for a would-be quantum programmer to understand.
For students grappling with quantum theory that even experts struggle to explain, it’s reassuring to get started using the QUI and actually see quantum computing at work.
“Quantum software design is such a new concept, and it feels more abstract than conventional computer coding, so being able to see what is happening when we design a function really helps,” says Daniel Johnston, a maths student taking the course.
SOLVING PROBLEMS IMMEDIATELY
Professor Hollenberg’s co-teacher, physicist Dr Charles Hill, says QUI means students are actually doing quantum computing themselves from the outset of the course.
“People learning quantum computing need to understand how bits of information are manipulated with the unique rules that are different from classic computing, and then write their programs in a way that solves the problem they are looking at.
“We’ve found that the QUI is easy and intuitive to use. It takes the beginner no more than five minutes to get started with the system and writing programs,” Dr Hill says.
According to Professor Hollenberg, further versions of QUI are now in the works.
“We are working on improvements and add-ons which will not only enhance the user experience, but also the uptake of the system more broadly in teaching and research,” he says.
Budding quantum software programmers, or indeed anyone interested in quantum computing, can view the introductory video and try the system at QUIspace.org.
In addition to Professor Hollenberg and Dr Hill, the QUI development team comprised Aidan Dang, Alex Zable and Matt Davis; IT experts Dr Melissa Makin and Dr Uli Felzmann; and research students Sam Tonetto, Gary Mooney and Greg White.
Banner Image: Peaks of probabilities associated with data in a quantum algorithm evolve through time in a complicated way. The example pictured is part of a simulation of a quantum computer finding the prime factors of a number using Shor’s Algorithm. Picture: Matthew Davis, Gregory White and Aidan Dang


Click the table of contents to start reading.
Learn Quantum Computing with Python and Q# demystifies quantum computing. Using Python and the new quantum programming language Q#, you’ll build your own quantum simulator and apply quantum programming techniques to real-world examples including cryptography and chemical analysis.
A great introduction to the exciting new world of quantum computing.
Part 1: Getting Started with Quantum
1 Introducing Quantum Computing
1.1 Who This Book is For
1.2 Who This Book is Not For
1.3 How this book is organized
1.4 Why does quantum computing matter?
1.5 What Can Quantum Computers Do?
1.6 What is a Quantum Computer?
1.6.1 How will we use quantum computers?
1.6.2 What can’t quantum computers do?
1.7 What is a Program?
1.7.1 What is a Quantum Program?
2 Qubits: The Building Blocks
2.1 Why do we need random numbers?
2.2 What are Classical Bits?
2.2.1 What Can We Do With Classical Bits?
2.2.2 Abstractions are our friend
2.3 Approaching Vectors
2.4 Seeing the Matrix for Ourselves
2.4.1 Party with inner products
2.5 Qubits: States and Operations
2.5.1 State of the qubit
2.5.2 The game of Operations
2.5.3 Measuring Qubits
2.5.4 Generalizing measurement: basis independence
2.5.5 Simulating qubits in code
2.6 Programming a Working QRNG
3 Sharing Secrets With Quantum Key Distribution
4 Nonlocal Games: Working With Multiple Qubits
5 Teleportation and Entanglement: Moving Quantum Data Around
Part 2: Programming Quantum Algorithms In Q#
6 Changing the odds: An introduction to Q#
6.1 Introducing the Quantum Development Kit
6.2 Functions and Operations in Q#
6.3 Passing Operations as Arguments
6.4 Playing Morgana’s Game in Q#
7 What is a Quantum Algorithm?
8 Quantum Sensing: Measuring At Very Small Scales
Part 3: Applied Quantum Computing
9 Computing Chemistry Problems With Quantum Computers
10 Searching Databases With Quantum Computers
11 Arithmetic With Quantum Computers
Appendix A: Installing Required Software
A.1 Installing a Python Environment
A.1.1 Installing Anaconda
A.1.2 Installing Python packages with Anaconda: QuTiP
A.2 Installing the Quantum Development Kit
A.2.1 Installing the .NET Core SDK
A.2.2 Installing the Project Templates
A.2.3 Installing the Visual Studio Code extension
A.2.4 Installing IQ# for Jupyter Notebook
A.2.5 Installing the
qsharp Python package
About the TechnologyQuantum computing is the next step in computing power and scalability, with the potential to impact everything from data science to information security. Using qubits, the fundamental unit of quantum information, quantum computers can solve problems beyond the scale of classical computing. Software packages like Microsoft's Quantum Development Kit and the Q# language are now emerging to give programmers a quick path to exploring quantum development for the first time.
About the bookLearn Quantum Computing with Python and Q# demystifies quantum computing. Using Microsoft’s Quantum Development Kit to abstract away the mathematical complexities, this book builds your understanding of quantum computers by actively developing for them. You’ll start by learning QC fundamentals by creating your own quantum simulator in Python. Soon you’ll move on to using the QDK and the new Q# language for writing and running algorithms very different to those found in classical computing. When you’re finished you’ll be able to apply quantum programming techniques to applications like quantum key distribution, and tackle real-world examples such as chemistry simulations and searching unsorted databases.
- The underlying mechanics of how quantum computers work
- How to simulate qubits in Python
- Q# and the Microsoft Quantum Developer Kit
- How to apply quantum algorithms to real-world examples
About the readerNo academic experience of quantum computing is required. A reader will need basic programming skills and some experience of linear algebra, calculus and complex numbers.
About the authors
Christopher Granade completed his PhD in physics (quantum information) at the University of Waterloo’s Institute for Quantum Computing, and now works in the Quantum Architectures and Computation (QuArC) group at Microsoft. He works in developing the standard libraries for Q# and is an expert in the statistical characterization of quantum devices from classical data. Previously, Christopher helped Scott Aaronson prepare lectures into his recent book, Quantum Computing Since Democritus.
Sarah Kaiser completed her PhD in physics (quantum information) at the University of Waterloo’s Institute for Quantum Computing. She has spent much of her career developing new quantum hardware in the lab, from satellites to hacking quantum cryptography hardware. Communicating what is so exciting about quantum is her passion, and she loves finding new demos and tools to help enable the quantum community to grow. When not at the keyboard, she loves kayaking and writing books about engineering for kids.
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by Alexandru Gheorghiu (University of Edinburgh) and Elham Kashefi (University of Edinburgh, CNRS)
Quantum computers promise to efficiently solve not only problems believed to be intractable for classical computers, but also problems for which verifying the solution is also intractable. How then, can one check whether quantum computers are indeed producing correct results? We propose a protocol to answer this question.
Quantum information theory has radically altered our perspective about quantum mechanics. Initially, research into quantum mechanics was devoted to explaining phenomena as they are observed in nature. But the focus then changed to designing and creating quantum systems for computation, information processing, communication, and cryptography among many other tasks. In particular, what became clear was that quantum interference - “the heart of quantum mechanics”, as Richard Feynman described it - can be harnessed for quantum computation. Algorithms running on a hypothetical quantum computer would be able to solve problems by creating an interference pattern of different computational branches. This can lead to an exponential saving in the amount of resources used by a quantum algorithm, when compared to the best known classical algorithms. The most famous example of this is Shor's algorithm for factoring numbers which is exponentially faster than the best known classical factoring algorithms.
But having a device which can solve problems exponentially faster than classical computers raises an interesting question: can a classical computer efficiently verify the results produced by this device? At first, one might be tempted to dismiss this question and say that as long as each component of a quantum computer has been tested and works correctly, there is no need to worry about the validity of the device's results. However, the point of verification is much more profound. Quantum computers would provide one of the most stringent tests of the laws of quantum mechanics. While numerous experiments involving quantum systems have already been performed to a remarkable precision, they all utilized relatively few degrees of freedom. But when many degrees of freedom are involved, and because predicting the outcome of the experiment requires exponential resources, it quickly becomes infeasible to calculate the possible results of the experiment without resorting to lax approximations. Verification of quantum computation would therefore allow for a new test of quantum mechanics, a test in the regime of high complexity.
There is another important reason for verifying quantum computations, having to do with cryptography. The first quantum computers are likely to be servers, to which clients can connect through the Internet. We can already see an instance of this with the recent 5-qubit and 16-qubit devices that IBM has made available to the general public [L1]. When larger devices become available, users will wish to delegate complex computations to them. However, in such a distributed environment, malicious agents might perform man-in-the-middle attacks or compromise the remote server. The clients would then need a means to check the validity of the server's responses. In fact, in this setting, users might also wish to keep their data hidden even from the quantum computer itself, as it might involve sensitive or classified information.
So can one verify quantum computations while also maintaining the secrecy of the client's input? The answer is yes. In fact, the client's ability to keep the input hidden is what makes verification possible. This was shown by Fitzsimons and Kashefi when they proposed a verification protocol based on a cryptographic primitive known as Universal Blind Quantum Computation (UBQC) [1,2]. In UBQC, a client that can prepare single qubits has the ability to delegate quantum computations to a server, in such a way that the server is oblivious to the computation being performed. To do verification, the client can then exploit this property by embedding tests in the computation, referred to as traps, which will fail if the server doesn't perform the correct computation. Of course, the problem with this approach is that the client needs to trust that the qubit preparation device works correctly and produces the specified states. But if, prior to the start of the protocol, a malicious agent corrupts the preparation device, the client could later be tricked into accepting incorrect results.
To address this issue, we, together with Dr. Petros Wallden, at the University of Edinburgh, proposed a verification protocol which is device-independent . In other words, the client need not trust any of the quantum devices in the protocol. This is achieved by using a powerful result of Reichardt, Unger and Vazirani, known as rigidity of non-local correlations . Non-local correlations are correlations between responses of non-communicating parties that cannot be reproduced classically, unless the parties are allowed to communicate. Such correlations can be produced, quantum mechanically, through a suitable strategy for measuring certain entangled states. The rigidity result is essentially a converse to this. It states that certain non-local correlations can only be produced by a particular, unique strategy. Observing such correlations between non-communicating devices then implies that the devices are behaving according to this fixed strategy. What is remarkable about this result is that it only requires examining the outputs of the devices, without assuming anything about their inner workings.
The protocol then works as follows: the client has an untrusted device for measuring single qubits and is also communicating classically with the quantum server. By examining the outputs of the two devices, it follows from the rigidity result that the client can check whether the two devices are sharing entanglement and performing measurements as instructed. If so, the client leverages this and uses the entanglement to remotely prepare single qubit states on the server's side. Finally, the client uses the trap-based scheme of Fitzsimons and Kashefi to delegate and verify an arbitrary quantum computation to the server.
Figure 1: Device-independent verification protocol. The client, or verifier, will instruct both the measurement device and the server to measure entangled qubits. The statistics of these measurements are then checked by the verifier. All communication with the quantum devices is classical.
Verification is an important milestone on the road to scalable quantum computing technology. As we have seen, verification protocols exist even for the most paranoid users. But even so, questions still remain regarding their optimality, their ability to tolerate noise and imperfections, as well as other issues. Addressing all these questions is a key challenge for both theorists and experimentalists and their resolution will shape the landscape of the emerging Quantum Internet.
 A. Broadbent, J.F. Fitzsimons, E. Kashefi: “Universal blind quantum computation”, in Proc. of FOCS ‘09, IEEE Computer Society (2009) 517 – 526.
 J.F. Fitzsimons, E. Kashefi: “Unconditionally verifiable blind quantum computation”, Phys. Rev. A 96 (2017) 012303.
 A. Gheorghiu, E. Kashefi, P. Wallden: “Robustness and device independence of verifiable blind quantum computing”, New Journal of Physics 17(8) (2015) 083040.
 B.W. Reichardt, F. Unger, U. Vazirani: Classical command of quantum systems. Nature 496(7446) (2013) 456.
Elham Kashefi, University of Edinburgh, UK and CNRS, France
University of Edinburgh, UK


Industrial Revolution had started in 1784 AD in Britain. It is marked as an end of medieval era and start of modern era. It also means end of Human hard-work and start of machine work. It affected almost every aspects of human life. Starting from Britain it spread in whole world. Until that time only wood was primary source of energy but coal took over and generation of energy and its requirement of it increased. A first Steam engine was developed that could pump water from below ground to enable mining of coal deep down. With steam engine development of steam trains, steam powered pumps and machine were invented. The first modern water powered cotton spinning mill factory was established by Richard Arkwright in 1774 in Comford in Derbyshire with around 200 workers (today it is UNESCO world heritage point). People started to migrate to live near newly formed industries, crowding the area for supply of labourers and this is how modern urban areas developed. Huge sum of money was invested in making canals, production of pig iron with production of coal was done, it is also known as Canal Mania. Development of residential places, canals, Railways, roads, etc. took place. Britain’s first railway line opened in 1825 built by Stockton and Danlington. In 1840s railways building was on its high peak which is also known as Railway Mania. This was all about Industrial Revolution 1.
After all advancement towards mechanical industrial revolution there was Technological revolution also known as Industrial Revolution 2. It started at the latter part of the 19th century. Due to industrial revolution there was mass production of cloth, goods, and surplus amount of agriculture products productions. It also changed the global trade. In this technological revolution there was new innovation in steel production, electricity and petroleum which made way for introduction of automobile and then planes. During this period manufacturing and production method were improved for example production of steel which replaced iron and it was strong and cheap price and which made possible to build rail lines at cheap price and it spread transportation, facilated the construction of ships, skyscrapers and larger bridges. In today’s world you won’t be able to imagine a world without electricity but till now it was a norm and the brilliant idea of electricity was invented. Edison made its first light bulb working on electricity and commercial first bulb generated in 1870s. And then Mosley Street and Saroy theatre set the stage for the first large scale power station. Second industrial revolution rolled around electricity and many component needed for it was too invented for example stepping down high voltage alternating current by Sebastian de Ferranti and with it he also enabled the assembly line and mass production. Now with technology and electricity in Second Industrial Revolution it made way for distant communication and hence fruitfully in 1875 Alexander Graham Bell invented the telephone and many years after Guglielmo Marconi in 1901 sent radio waves across the Atlantic Ocean for the first time in History. Many products on which are now life is depended was invented in this era like current paper machine, steam driven rotary printing press, etc. also appeared.
This revolution lasted for around 100 years and now it was time for Industrial Revolution 3 which was started around 1960s. We are surrounded by technology, electronics, Internet, etc. and now it has become part of our life but when all these technology got invented? Yes it was Industrial Revolution 3. We can also called it as Digital Revolution as it brought semiconductors, main frame computing, personal computer, Internet, etc. Now which was analogy now became digital example the old television which used to tune with the antenna now got replaced by an Internet connected tablet. With digitalization automation took place in industries, further reducing man’s hard work. Electronics and information technology began to automate production and take supply chain global. Everything which was paper based work beamed computerised. We can also say Industrial Revolution 3 gave birth to new field that was IT sector. It was also the point where people life style changed with Internet, phone, electronics, automation, etc. Now a days developing countries are still going through industrial revolution 3. In 1980s only 1% of the world’s information was in digital format and now in 2019 it is around 99% in digital format.
Now in 21st century we all are going through industrial revolution 4 which can also be said Artificial Intelligence revolution. Now we are breaking the walls between the digital, physical and biological spheres known as cyber physical systems. It’s breakthrough in a numerous of field including robotics, artificial intelligence, quantum computing, nanotechnology, biotechnology, Internet of things, 3D printing, autonomous vehicle and 5th generation wireless technology, advance genetically knowledge, etc. We all are in 4th wave of industrial Revolution.
We are all going to witness human success in science and technology. The change world has seen in these 200 years is unimaginable. No one ever believed that we would fly, travel 100 times faster than horse, connect with anyone anywhere anytime in fraction of seconds, we would ever touch moon, and know secrets of universe, nanotechnology, biotechnology, etc. in just 200 years. But this haphazard development had led to many problems like pollution, degradation of earth, creation of new problems like health, cyber security, etc. May be the next advancement in this revolution would be to make earth pollution free and to solve many problems, etc.


The first ever programmable computer was an electro-mechanical hybrid of a machine put together in his parents’ front room in the late 1930s by German wunderkind Konrad Zuse. By the first half of the 1940s a fully digital machine, the Atanasoff-Berry Computer, had been achieved by the two scientists whose names the model was given. The two were scientists working at the Iowa State University. It took another several decades before computers had become both efficient and cheap enough to become a norm in first workspaces and then homes.
Over the 25 years since the economy and much of the entertainment sector was digitalised, there has been a rapid acceleration in the pace of development. The latest technology in the world of computer hardware and software is able to achieve some quite remarkable feats. Even the smartphones we carry in our pockets contain more powerful processors than the laptops and PCs of ten years ago.
We’ve undoubtedly come a long, long way since the computers of Zuse, Berry and Atanassof. We’re on the verge of AI software algorithms taking control of driverless cars and creating virtual reality worlds some are forecast to earn their primary income in within a decade or so. But despite the wonders modern software developers and hardware engineers are achieving at a seemingly ever accelerating rate, today’s most powerful computers and programmes retain one key similarity to Zuse’s first computer. Computers are all still programmed using exactly the same basic building blocks of binary code.
Software code is, and always has been, represented by strings of ‘1’ and ‘0’. If you know where to look, the code that CERN scientists in Switzerland use to crunch the data produced by the Hadron Collider, the photos on your smartphone, the PowerPoint Presentation you made at work last week and your favourite computer game are all just endless streams of ‘0’ and ‘1’. These digits are represented by tiny circuits that switch from off to on, representing ‘0’ and ‘1’ depending on their off or on position. These circuits are called ‘bits’.
This binary system of bits has made the latest technology in the world possible and for most tasks we use computing for is more than enough. However, for the most complex tasks we are now trying to compute, this binary system is beginning to prove to be a bottleneck. Some problems, such as modelling traffic flows, weather systems or the human genome become exponentially harder with each variable added. Faster and faster classical processors allow algorithms to very quickly sift through all the possible variables in their quest to arrive at an answer or end result. However, the sheer scale of the potential variables in some cases, such as the examples provided, means that it takes too long for binary computers to arrive at an answer to be a practical solution.
It is hoped the quantum processors will solve that bottleneck.
So What is Quantum Computing?
Quantum mechanics is the fundamental theory of physics that explains nature by breaking it down to the smallest possible level – the energy inherent in atoms and sub-atomic particles. Quantum computing is made possible by scientists being able to use electrons as quantum bits, or qubits. Qubits are distinct from classical bits not only by their microscopic size but by their very nature. Electrons exist simultaneously in two states at once. Or rather, they exist between two states to varying degrees. It’s something that even the greatest minds in theoretical physics grapple with but in the simplest possible terms, by maintaining qubits at an extremely cold temperature and using magnetism to manipulate their polar field, they can simultaneously represent both a ‘1’ and ‘0’ at the same time.
Rather than the string sequence of ‘1’s and ‘0’s of binary code, one qubit, which is both ‘1’ and ‘0’ can speak to two more qubits, who also speak to two more qubits each, which almost instantaneously results in countless numbers of qubits working together to process information. This means, in theory, qubits are able to run an indeterminate set of processes simultaneously and at a spectacular speed. This exponentially increases the processing power of a theoretical quantum computer system.
At What Stage is Quantum Computing Technology?
So that’s the theory but at what stage of development is quantum computing technology practically? Computer processors that have been proven to employ quantum phenomena do already exist. The tech big boys such as IBM, Microsoft and Alphabet are all working intensely on quantum computing R&D and one company, D-Wave Systems, is manufacturing commercially available quantum computing hardware.
However, the technology is still in an early stage, is far from perfect and can’t, as yet, do anything that classical binary processors cannot. Nonetheless, it has already been demonstrated that these early quantum processors can solve problems that created specifically to take account of their current structure and limitations, far more quickly than classical processors. Microsoft’s Research Lab is predicting working quantum computers being widely available within a decade. Many other experts approximately agree, give or take a decade or so. Others are sceptical and think that quantum computing faces obstacles that mean it will never be genuinely practical.
The biggest problem that researcher working on quantum processors face is keeping enough electrons in qubit state for long enough. It takes a huge amount of power to maintain the temperature and magnetic manipulation necessary for a qubit to come into existence for even a fraction of a second. When an electron loses it qubit state the information held in it is also lost unless passed on in time. This means the same information has to be held simultaneously on multiple qubits simultaneously and passed through the network quickly. It’s a bit like a movie scene where the hero or heroine is running across a crumbling bridge and has to reach the other side of a ravine before it crumbles from beneath their feet.
The challenge is in creating processors that can maintain enough qubits in a connected state for long enough. D-Wave’s most recent working prototype 2000Q system is said to have made a ‘critical breakthrough’ in this respect.
One has been bought and installed in the Quantum AI Lab run in partnership by Alphabet, NASA and the Universities Space Research Association. IBM also believes its research lab will succeed in connecting 50 qubits in a processor before the end of the year. The company has also managed to increase the period of time qubits exist to 100 microseconds and expects this to be multiplied by ten to a millisecond within 5 years.
Within the next few years, quantum computing processors are expected to reach a ‘good enough’ approximation of the technology. ‘Good enough’ will mean a system that is based on quantum phenomena and, at least in certain applications, achieves processing speeds that classical binary processors cannot.
The Future for Quantum Computing
The ‘full’ quantum revolution is thought to still be many years away but plenty of encouraging breakthroughs are taking place. Key to increasing the stability of qubits may be recent achievements by a team of quantum scientists at Harvard University lead by Professor Kang-Kuen Ni.
The team have, for the first time, succeeded in creating a ‘bespoke molecule’. Optical tweezers were used to manipulate single sodium and caesium atoms in an alloy that represents the first man-made molecule. The molecule also just happens to represent the optimal electrical constitution to be maintained in qubit status.
We don’t yet really know how quickly developments in quantum computing technology may come about. It may be that, like many fields of science, the modern era will see us make leaps in years that previously took multiple decades.
As an almost completely new science, quantum computing may also take the decades to develop that classical binary computing took before progress started to speed up. We may also hit a bottleneck that means quantum computing turns out to be a dead end.
If we do get there though, and there are plenty of positive indicators that we may, quantum computing could be the most significant step yet towards humanity succeeding in stripping back the Veil of Maya to see the secrets of the universe revealed.


The Natural History Museum of Utah recently announced that a recently found dinosaur fossil of Lythronax argestes is a new branch of the tyrannosaur family tree. It weighed 2 tons, and was over 24 feet long. Lythronax evolved over 10 million years before other tyrannosaurs, changing our understanding of dinosaur evolution.
Lythronax was only one of a bunch of new dinosaur fossils discovered at Grand Staircase-Escalante National Monument.
Metabolism. In a cellular context, it means the chemical reactions that happen in cells that help keep it alive. These reactions are fairly complex, and for a long time we thought that they could only happen inside cells, which kind of leads to a chicken or the egg kind of paradox. Now, scientists have found that it is relatively simple to have metabolic reactions happen outside of cells.
RNA is used to make proteins. And you need these proteins to do things with RNA. But these experiments show that you don’t need RNA to get the metabolic reactions happening. They could have happened in the Earth’s early oceans.
By starting with what we think the Earth’s early oceans would have, along with the starting chemicals for metabolic reactions, then heating it to 50° to 70° C for 5 hours, they were able to produce 29 different metabolic reactions. These included glycolysis and the pentose phosphate pathway, which are needed for production of ATP.
This helps scientists understand abiogenesis, how life first started. It takes out the requirement for a cell to form with all of the necessary chemistry along with it out of whole cloth. The chemistry is capable of working before the first cell formed. The part we don’t understand yet is where the starting chemicals came from. We don’t know how they could have formed yet. But we’re getting closer.
Scientists studying the brain have managed to grow neurons on petri dishes for a while, but they don’t connect the way real neurons do because the ones in a dish grow in a fundamentally 2D environment, and regular brains are fundamentally 3D.
Now, researchers at Tufts University in Boston have made a 3D scaffold that allows neurons to connect more realistically. It has grey matter / white matter compartmentalization, which means that the structure is more similar to real brains. It can also last longer, up to two months in labs.
This new tissue can let scientists study brain biology in more detail. They can see what happens to nearby cells when there is trauma. They can also see the effects of administering drugs more easily.
Here’s a good Ted video on Quantum Mechanics, specifically, Quantum Entanglement.
However, there are some things that you should keep in mind. I know that someone will say “sure I can tell if the cat is alive or not without opening the box. Just listen for the bomb.”. In the original thought experiment, there is a vial of poisonous gas, a Geiger counter, and a radioactive source. If the Geiger counter detects radiation, it will break the vial (killing the cat). After one hour, there is a 50% chance that this will happen. The bomb version is easier to understand, but you have to realize that you can’t detect if the bomb has exploded or not.
Also, for the entanglement to work, you have to set things up very carefully to make the entanglement happen. You can’t just grab 2 atoms and have them be entangled.
NASA has recently tested a new type of drive that may be used in future spaceships. The Cannae Drive is unique in that it doesn’t use propellant. Since propellant (fuel) has mass, normal drives need to move the spacecraft and the propellant for future thrust. This leads to needing lots of mass, frequently as much as the payload.
But the Cannae Drive is different. It uses microwaves instead of propellant. By bouncing microwaves in a specially shaped container, they have managed to create a difference in radiation pressure, generating between 30-50 micronewtons. This is a very small amount of thrust. The only energy that is needed is electricity, which is readily available through solar panels.
This technology is in its infancy, and is a long way from being used in spacecraft.
I love this kind of thing because it appears to violate the Law of Conservation of Momentum (simpler). This means that we’re at the edge where our understanding of the way the universe works may be wrong. Our scientific understanding may have to change to account for this effect.
Toxoplasma gondii is a single celled parasite that lives in a cat’s intestine. While it prefers felines, it can live in humans and other animals. In fact, about 1/3 of humans are hosts to it. Normally this isn’t a problem, but it is for people with suppressed immune systems. The interesting thing is that a human body’s reaction to T. gondii is similar to its response to cancer tumors.
This leads to the idea that perhaps this parasite can be used as a cancer therapy. T. gondii stimulates the body’s immune system to fight cancer. While cancer can shut down the immune system, T. gondii stimulates it. Scientists have created a version of the parasite that can be grown in a lab, but can’t grow in animals/people. This may lead to an effective cancer drug that helps the body fight the disease.


Semiconductor Technology May Pave Way For Integrating Quantum Communications Into Standard Optical Channels
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Since the 1920s, scientists have theorized ways to exploit the properties of quantum systems for communication purposes. By utilizing the strange properties of quantum entities and phenomena, like superpositions and entanglement, quantum communication channels could create genuinely unbreakable encryption protocols and provide computing power vastly superior to traditional computing methods.
Now, a team of researchers has taken a significant step toward making the use of quantum communications a practical reality.
In an article published October 1st in Nature, a group of scientists from the University of Groningen in the Netherlands reports that they have developed a reliable method to create entangled pairs of quantum particles at wavelengths close to those that are used by standard telecom providers. The new method relies on exploiting the structural defects of the semiconductor silicon carbide in optical fibers to produce qubits of information that can be transmitted via ordinary communication channels.
Previous attempts at communicating using quantum systems have shown success, but most existing setups require custom hardware, as the produced qubits emit photons at wavelengths outside the range of that used standard optical channels. The discovery of this method is a significant step towards integrating quantum communications into everyday communication systems.
Qubits And Semiconductors
Standard digital computing systems store information in registers called “bits.” In digital computers, a bit can only take on one of two values; either a 1 or a 0 (hence the term binary). Quantum computing systems use “qubits,” a generalization of the classical bit. Qubits can take on three states, 1, 0, or a superposition of both 1 and 0. Essentially, a classical computer bit can exist in only one state at one time while a qubit can exist in multiple states at the same time. The most interesting property of a qubit is that it can store arbitrarily large amounts of classical information, so a quantum computer theoretically could perform tasks that would be impossible or take an extremely long time on a regular digital computer.
To produce a qubit, first, the system has to make an entangled pair of photons. Two entangled particles will have certain values correlated no matter how far apart they are separated, so a measurement on one will instantaneously give you information about the state of the other particle. So, in order for a quantum communication channel to work, there needs to be a reliable way to create entangled pairs of particles. Additionally, these superpositions must be sufficiently isolated from the surrounding environment, as any small disturbance can decohere the superposition into a classical state.
It is known that various transition-metal impurities in semiconductors will emit photons when struck by light. These impurities are known as “color-centers” and can affect the behavior of light. When light is shined on these impurities, excited electrons will jump to a higher energy state. When they fall back to their ground state, the electrons will emit the excess energy as a photon. For a material like silicon carbide with impurities of the metal molybdenum, the photons are emitted at an infrared wavelength close to that used in standard telecom communications channels.
With this information in mind, the team began constructing their system. By using a procedure known as “coherent population trapping,” they were able to create superpositions of electrons in the color-centers of samples of silicon carbide with molybdenum impurities. These superpositions of electrons represented the qubits of information as entangled electrons will always have their spin values correlate. Using magnetic fields, the team was able to align the superpositions in whatever direction they desired. According to Ph.D. student Carmem Gilardoni, one of the researchers credited on the paper, “If you apply a magnetic field, the spins align either parallel or anti-parallel to the magnetic field. The interesting thing is that as a result, the ground state for electrons with spin up or spin down is slightly different.” Shining light on these electrons will make them fall back into one of two ground states and emit an entangled pair of photons.
After some initial attempts, they managed to produce stable and long lasting superpositions at the color centers. The created superpositions showed optical life-cycles of 60 nanoseconds; long enough to extract useful information out of the quantum system. Most importantly, the entangled photons were emitted at a wavelength of ~1100 nm. Traditional optical communications channels use infrared wavelengths of ~1,300-1,500 nm and given the massive amount of knowledge of the effects of transition-metal impurities in semiconductors on the behavior of light, the team is confident that they can fine-tune their procedure to create photon pairs that are emitted at wavelengths that fall comfortably within those used in standard optical channels.
One potential use of this technology would be to create genuinely unbreakable encryptions on communication channels. Superpositions are finicky entities and any disturbance can destroy a superposition by collapsing it into a definite state. If a person attempts to tap into a quantum communication channel to listen in on someone’s conversations, their external interaction with the channel will cause the quantum state to collapse. The result is that it is impossible to eavesdrop on two people who are communicating using a quantum channel, as any attempt to tap into the channel from the outside will decohere the state and destroy the original information.
Other applications of quantum computing include an internet with unparalleled speeds and running simulations of quantum systems. Information stored in qubits can be accessed at dazzling speeds and a single qubit can hold infinitely more information than a classical bit. Though still in its infancy, the potential applications of this technology would completely change the face of modern technology as quantum communication channels could be easily integrated into existing communications hardware, ensuring that the benefits of quantum information systems would be readily available to as many people as possible.


There's a lot of hype floating around the general computer industry, hype centered around one specific technology that has the potential to change everything: quantum computers. Being our company's namesake, we'll admit to a bias in our bullishness around this tech, and over the course of this final chapter of our Future of Computers series, we hope to share with you just why that is.
At a basic level, a quantum computer offers an opportunity to manipulate information in a fundamentally different way. In fact, once this tech matures, these computers will not only solve mathematical problems faster than any computer currently in existence, but also any computer forecasted to exist over the next few decades (assuming Moore’s law holds true). In effect, similar to our discussion around supercomputers in our last chapter, future quantum computers will enable humanity to tackle ever larger questions that can help us gain a profoundly deeper understanding of the world around us.
What are quantum computers?
Hype aside, just how are quantum computers different than standard computers? And how do they work?
For visual learners, we recommend watching this fun, short video from the Kurzgesagt YouTube team about this topic:
Meanwhile, for our readers, we'll do our best to explain quantum computers without the need for a physics degree.
For starters, we need to recall that the basic unit of information computers process is a bit. These bits can have one of two values: 1 or 0, on or off, yes or no. If you combine enough of these bits together, you can then represent numbers of any size and do all manner of calculations on them, on after the other. The bigger or more powerful the computer chip, the bigger the numbers you can create and apply calculations, and the faster you can move from one calculation to another.
Quantum computers are different in two important ways.
First, is the advantage of “superposition.” While traditional computers operate with bits, quantum computers operate with qubits. The superposition effect qubits enable is that instead of being constrained to one of two possible values (1 or 0), a qubit can exist as a mixture of both. This feature allows quantum computers to operate more efficiently (faster) than traditional computers.
Second, is the advantage of “entanglement.” This phenomenon is a unique quantum physics behaviour that binds the destiny of a quantity of different particles, so that what happens to one will affect the others. When applied to quantum computers, this means they can manipulate all their qubits simultaneously—in other words, instead of doing a set of calculations one after another, a quantum computer could do them all at the same time.
The race to build the first quantum computer
This heading is somewhat of a misnomer. Leading companies like Microsoft, IBM and Google have already created the first experimental quantum computers, but these early prototypes feature less than two dozen qubits per chip. And while these early efforts are a great first step, tech companies and government research departments will need to build a quantum computer featuring at least 49 to 50 qubits for the hype to meet its theorized real-world potential.
To this end, there are a number of approaches being experimented with to achieve this 50 qubit milestone, but two stand above all comers.
In one camp, Google and IBM aim to develop a quantum computer by representing qubits as currents flowing through superconducting wires that are cooled to –273.15 degrees Celsius, or absolute zero. The presence or absence of current stands for a 1 or 0. The benefit of this approach is that these superconducting wires or circuits can be built out of silicon, a material semiconductor companies have decades of experience working with.
The second approach, led by Microsoft, involves trapped ions held in place in a vacuum chamber and manipulated by lasers. The oscillating charges function as qubits, which are then used to process the quantum computer’s operations.
How we will use quantum computers
Okay, putting the theory aside, let’s focus on the real world applications these quantum computers will have on the world and how companies and people engage with it.
Logistical and optimization problems. Among the most immediate and profitable uses for quantum computers will be optimization. For ride-sharing apps, like Uber, what's the fastest route to pick up and drop off as many customers as possible? For e-commerce giants, like Amazon, what's the most cost-effective way to deliver billions of packages during the holiday gift buying rush?
These simple questions involve number crunching hundreds to thousands of variables at once, a feat that modern supercomputers just can't handle; so instead, they compute a small percentage of those variables to help these companies manage their logistical needs in a less than optimal way. But with a quantum computer, it will slice through a mountain of variables without breaking a sweat.
Weather and climate modeling. Similar to the point above, the reason why the weather channel sometimes gets it wrong is because there are too many environmental variables for their supercomputers to process (that and sometimes poor weather data collection). But with a quantum computer, weather scientists can not only forecast near-term weather patterns perfectly, but they can also create more accurate long-term climate assessments to predict the effects of climate change.
Personalized medicine. Decoding your DNA and your unique microbiome is crucial for future doctors to prescribe drugs that are perfectly tailored to your body. While traditional supercomputers have made strides in decoding DNA cost-effectively, the microbiome is far beyond their reach—but not so for future quantum computers.
Quantum computers will also allow Big Pharma to better predict how different molecules react with their drugs, thereby significantly speeding up pharmaceutical development and lowering prices.
Space exploration. The space telescopes of today (and tomorrow) collect enormous amounts of astrological imagery data each day that tracks the movements of trillions of galaxies, stars, planets, and asteroids. Sadly, this is far too much data for today's supercomputers to sift through to make meaningful discoveries on a regular basis. But with a mature quantum computer combined with machine-learning, all this data can finally be processed efficiently, opening the door to the discovery of hundreds to thousands of new planets daily by the early-2030s.
Fundamental sciences. Similar to the points above, the raw computing power these quantum computers enable will allow scientists and engineers to devise new chemicals and materials, as well as better functioning engines and of course, cooler Christmas toys.
Machine learning. Using traditional computers, machine-learning algorithms need a giant amount of curated and labeled examples (big data) to learn new skills. With quantum computing, machine-learning software can begin to learn more like humans, whereby they can pick up new skills using less data, messier data, often with few instructions.
This application is also a topic of excitement among researchers in the artificial intelligence (AI) field, as this improved natural learning capacity could accelerate progress in AI research by decades. More on this in our Future of Artificial Intelligence series.
Encryption. Sadly, this is the application that has most researchers and intelligence agencies nervous. All current encryption services depend on creating passwords that would take a modern supercomputer thousands of years to crack; quantum computers could theoretically rip through these encryption keys in under an hour.
Banking, communication, national security services, the internet itself depends on reliable encryption to function. (Oh, and forget about the bitcoin as well, given its core dependence on encryption.) If these quantum computers work as advertised, all of these industries will be at risk, at worst endangering the entire world economy until we build quantum encryption to keep pace.
Real-time language translation. To end this chapter and this series on a less stressful note, quantum computers will also enable near-perfect, real-time language translation between any two languages, either over a Skype chat or through the use of an audio wearable or implant in your ear.
In 20 years, language will no longer be a barrier to business and everyday interactions. For example, a person who only speaks English can more confidently enter into business relationships with partners in foreign countries where English brands would have otherwise failed to penetrate, and when visiting said foreign countries, this person may even fall in love with a certain somebody who only happens to speak Cantonese.


A Giant Quantum Leap
Chinese scientists have established a quantum entanglement between particles 1200 kilometres apart, smashing the previous world record of 143 kilometres.
In early 2016, China announced a successful transmission of “entangled” photon pairs from space to the Earth, which proves that quantum entanglement exists at a large distance.
The result is a stepping stone to ultrasecure communication networks and, eventually, a space-based quantum internet.
The study was published as a cover story by the U.S. journal Science on Friday.
What is quantum entanglement?
Quantum entanglement occurs when a pair of photons interact physically in the opposite manner at a large distance.
The entanglement phenomenon also involves putting objects in the peculiar limbo of quantum superposition, in which an object’s quantum properties can occupy multiple states at once: both here and there, both dead and alive at the same time. And these multiple quantum states can be shared among multiple objects.
So if entangled objects are separated, their precarious quantum states should remain linked until one of them is measured or disturbed. That measurement instantly determines the state of the other object, no matter how far away.
It means the communication speed that is faster than light is possible, and one-day humans might also be able o communicate with one another over massive distances, and instantly. Such implication troubled Einstein, as it is in direct violation of his Relativity.
However, as Chinese have long observed, comprehended and described, yin-yang balance is achieved constantly and consistently in motion, otherwise, the reality would collapse, while the same taichi core is a holographic existence in each piece of and at all levels in the universe.
Quantum entanglement and quantum computer
The basics of a quantum computer include processing data with ‘qubits’ (quantum particles), not a common binary system, which allows photons (particles of qubit) to exist in multiple states at the same time, so instead of being stored as either 0 or 1, it can be both 0 and 1. This can bring a quantum leap in computing speed.
The unique, long distance and constant entanglement also point to the possibility of hack-proof communications.
Usually, hackers perform cyber attacks by intervening within the system through transmitted signals. Quantum computer can be a game changer because the entanglement doesn’t require transmission.
Further, two parties can exchange secret messages by sharing an encryption key encoded in the properties of entangled particles. Any eavesdropper would affect the entanglement and so be detected.
Efforts to prove quantum entanglement
Ever since the 1970s, physicists began testing the quantum entanglement effect. In 2015, a test, which involved measuring entangled electrons 1.3 kilometres apart, demonstrates that such a correlation is real.
Yet efforts to entangle quantum particles, such as photons, have been limited to about 100 km, mostly because the entanglement is lost when transmitted along optical fibres or through open space.
A Chinese satellite for quantum entanglement research
Last August the Chinese Academy of Sciences put an experimental satellite into orbit.
The satellite, with a design life of two years, was the world’s first satellite launched to do quantum experiments, which is officially known as Quantum Experiments at Space Scale (QUESS), also known as Mozi, after the Chinese philosopher 墨子 (470BC-391BC), believed to be the first person conducted optical experiments in the world.
Central to QUESS’s experiments is a laser beam mounted on the Micius satellite. The beam was split to generate pairs of photons that share a common quantum state, in this case, related to polarization. The entangled photons were funnelled into two onboard telescopes that fired them at separate stations on the ground: one in Delingha in northwest China’s Qinghai Province and another in Lijiang in southwest China’s Yunnan Province.
The team found that all pairs of the photons were still entangled. This proves that quantum communication at continental distances can be achieved.
The next step in developing a global network is reportedly to test quantum key distribution and to establish a quantum connection between China and a ground station in Vienna.
According to the Chinese research team, over the next five years, more satellites with capabilities identical to Micius are planned for launch.
China is not the only country to perform such experiments but is the first to go that far. Teams from Canada, Germany, Austria, Singapore and other countries also have plans for quantum space experiments.
COMMENTS FROM GOOGLE PLUS
Lawrence Kedz (Jun 19, 2017)
This one may take hours to merely establish an overview? I’ll try to find some time because science still tugs at my heart at times. But I had realized when I’d left, it was an all or nothing proposition. Thanks for the memories, and for showing me that I still believe I’ve made the right decision almost 20 years ago.
All Things Chinese
I’m truly truly excited by this breakthrough 😅
Quantum physics is a way to rescue modern science from becoming a religion, that places blind faith in our own selective, omissive, rigid and biased brain activity called “logical reasoning”, a single dimensional thinking path, and their external physical extensions.


Materials by design: Argonne researchers use genetic algorithms for better superconductors.
Owners of thoroughbred stallions carefully breed prizewinning horses over generations to eke out fractions of a second in million-dollar races. Materials scientists have taken a page from that playbook, turning to the power of evolution and artificial selection to develop superconductors that can transmit electric current as efficiently as possible.
Perhaps counterintuitively, most applied superconductors can operate at high magnetic fields because they contain defects. The number, size, shape and position of the defects within a superconductor work together to enhance the electric current carrying capacity in the presence of a magnetic field.
Too many defects, however, can lead to blocking the electric current pathway or a breakdown of the superconducting material, so scientists need to be selective in how they incorporate defects into a material.
"When people think of targeted evolution, they might think of people who breed dogs or horses. Ours is an example of materials by design, where the computer learns from prior generations the best possible arrangement of defects." -- Argonne materials scientist Andreas Glatz.
In a new study from the U.S. Department of Energy's (DOEArgonne National Laboratory, researchers used the power of artificial intelligence and high-performance supercomputers to introduce and assess the impact of different configurations of defects on the performance of a superconductor.
The researchers developed a computer algorithm that treated each defect like a biological gene. Different combinations of defects yielded superconductors able to carry different amounts of current. Once the algorithm identified a particularly advantageous set of defects, it re-initialized with that set of defects as a "seed," from which new combinations of defects would emerge.
"Each run of the simulation is equivalent to the formation of a new generation of defects that the algorithm seeks to optimize," said Argonne distinguished fellow and senior materials scientist Wai-Kwong Kwok, an author of the study. "Over time, the defect structures become progressively refined, as we intentionally select for defect structures that will allow for materials with the highest critical current."
The reason defects form such an essential part of a superconductor lies in their ability to trap and anchor magnetic vortices that form in the presence of a magnetic field. These vortices can move freely within a pure superconducting material when a current is applied. When they do so, they start to generate a resistance, negating the superconducting effect. Keeping vortices pinned, while still allowing current to travel through the material, represents a holy grail for scientists seeking to find ways to transmit electricity without loss in applied superconductors.
To find the right combination of defects to arrest the motion of the vortices, the researchers initialized their algorithm with defects of random shape and size. While the researchers knew this would be far from the optimal setup, it gave the model a set of neutral initial conditions from which to work. As the researchers ran through successive generations of the model, they saw the initial defects transform into a columnar shape and ultimately a periodic arrangement of planar defects.
"When people think of targeted evolution, they might think of people who breed dogs or horses," said Argonne materials scientist Andreas Glatz, the corresponding author of the study. "Ours is an example of materials by design, where the computer learns from prior generations the best possible arrangement of defects."
One potential drawback to the process of artificial defect selection lies in the fact that certain defect patterns can become entrenched in the model, leading to a kind of calcification of the genetic data. "In a certain sense, you can kind of think of it like inbreeding," Kwok said. "Conserving most information in our defect 'gene pool' between generations has both benefits and limitations as it does not allow for drastic systemwide transformations. However, our digital 'evolution' can be repeated with different initial seeds to avoid these problems."
In order to run their model, the researchers required high-performance computing facilities at Argonne and Oak Ridge National Laboratory. The Argonne Leadership Computing Facility and Oak Ridge Leadership Computing Facility are both DOE Office of Science User Facilities.
An article based on the study, "Targeted evolution of pinning landscapes for large superconducting critical currents," appeared in the May 21 edition of the Proceedings of the National Academy of Sciences. In addition to Kwok and Glatz, Argonne's Ivan Sadovskyy, Alexei Koshelev and Ulrich Welp also collaborated.
Funding for the research came from the DOE's Office of Science.
Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation's first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America's scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy's Office of Science.
The U.S. Department of Energy's Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit https:/
Chris Kramer | EurekAlert!
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Quantum computers use quantum bits or "qubits" to do their calculations - quantum states, that is, of atoms or electrons that can take on the logical values "0" and "1" at the same time. In order to wire up many such qubits to make a powerful quantum computer, one needs to couple them to each other over distances of millimetres or even several metres. One way of achieving this is by exploiting the charge displacement caused by an electromagnetic wave, which is the working principle of an antenna. Such a coupling, however, also exposes the qubit to disturbances due to unwanted electric fields, which severely limits the quality of the logical qubit operations.
A team of scientists working in several research groups at ETH Zurich, assisted by theoretical physicists at Sherbrooke University in Canada, have now demonstrated how this problem can be avoided. To do so, they found a way to couple a microwave photon to a spin qubit in a quantum dot.
Qubits with charge or spin
In quantum dots, electrons are first trapped in semiconductor structures measuring just a few nanometres that are cooled to less than one degree above the absolute zero of the temperature scale. The logical values 0 and 1 can now be realized in two different ways. One either defines a qubit in terms of the position of the electron on the right or left side of a double quantum dot, or else by the spin of the electron, which can point up or down.
The first case is called a charge qubit, which couples strongly to electromagnetic waves through the displacement of electric charge. A spin qubit, on the other hand, can be visualized as a tiny compass needle that points up or down. Much like a compass needle, a spin is also magnetic and, therefore, does not couple to electric but rather to magnetic fields. The coupling of a spin qubit to the magnetic part of electromagnetic waves, however, is much weaker than that of a charge qubit to the electric part.
Three spins for stronger coupling
This means that, on the one hand, a spin qubit is less susceptible to noise and keeps its coherence (on which the action of a quantum computer is based) for a longer period of time. On the other hand, it is considerably more difficult to couple spin qubits to each other over long distances using photons. The research group of ETH professor Klaus Ensslin uses a trick to make such a coupling possible nevertheless, as the post-doc Jonne Koski explains: "By realising the qubit with not just a single spin, but rather three of them, we can combine the advantages of a spin qubit with those of a charge qubit."
In practice, this is done by producing three quantum dots on a semiconductor chip that are close to each other and can be controlled by voltages that are applied through tiny wires. In each of the quantum dots, electrons with spins pointing up or down can be trapped. Additionally, one of the wires connects the spin trio to a microwave resonator. The voltages at the quantum dots are now adjusted in order to have a single electron in each quantum dot, with the spins of two of the electrons pointing in the same direction and the third spin pointing in the opposite direction.
Charge displacement through tunnelling
According to the rules of quantum mechanics, the electrons can also tunnel back and forth between the quantum dots with a certain probability. This means that two of the three electrons can temporarily happen to be in the same quantum dot, with one quantum dot remaining empty. In this constellation the electric charge is now unevenly distributed. This charge displacement, in turn, gives rise to an electric dipole that can couple strongly to the electric field of a microwave photon.
The scientists at ETH were able to clearly detect the strong coupling by measuring the resonance frequency of the microwave resonator. They observed how the resonance of the resonator split into two because of the coupling to the spin trio. From that data they could infer that the coherence of the spin qubit remained intact for more than 10 nanoseconds.
Spin trios for a quantum bus
The researchers are confident that it will soon be possible to realize a communication channel for quantum information between two spin qubits using this technology. "This will require us to put spin trios on either end of the microwave resonator and to show that the qubits are then coupled to each other through a microwave photon", says Andreas Landig, first author of the article and PhD student in Ensslin's group. This would be an important step towards a network of spatially distributed spin qubits. The researchers also emphasize that their method is very versatile and can straightforwardly be applied to other materials such as graphene.
This work was carried out in the framework of the National Centre of Competence in Research Quantum Science and Technology (NCCR QSIT). At ETH Zurich, scientists in the groups of Klaus Ensslin, Thomas Ihn, Werner Wegscheider and Andreas Wallraff were involved in the research.
Landig AJ, Koski JV, Scarlino P, Mendes UC, Blais A, Reichl C, Wegscheider W, Wallraff A, Ensslin K, Ihn T: Coherent spin-photon coupling using a resonant exchange qubit. Nature, 25 July 2018, doi: 10.1038/s41586-018-0365-y
Prof. Dr. Klaus Ensslin | EurekAlert!
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On March 22, 1909, US-American physicist Nathan Rosen was born. He is best known for his cooperation together with Albert Einstein and Boris Podolsky on the quantum-mechanical description of physical reality leading the the so-called Einstein-Podolsky-Rosen paradoxon, as well as his postulation of worm holes connecting distant areas in space. Although purely theoretic, his work also had an important impact on science fiction literature.
Nathan Rosen was born in New York City. He first studied electrical engineering (bachelor’s degree) and then physics (master’s degree 1929) at the Massachusetts Institute of Technology, where he received his doctorate in 1932 with the thesis “Calculation of Energies of Diatomic Molecules” under John C. Slater. During his time at the University, Rosen already published several papers on the explanation of an atomic nucleus‘ structure and on wave functions. He then became a National Research Fellow at the University of Michigan and Princeton University, where he studied theoretical molecular physics (model of the hydrogen molecule). However, he already wrote his master’s thesis on gravitational physics and contacted Albert Einstein at Princeton to get his opinion.
The Assistant of Einstein
Rosen started his assistance job to Albert Einstein in 1935, extending Einstein’s studies on wave functions, resulting in a publication together with Boris Podolsky. In the paper, the three scientists attempted to answer the question “Can quantum-mechanical description of physical reality be considered complete?“. The effects were then named the Einstein-Podolsky-Rosen paradox (EPR). The EPR paradox contains a thought experiment, attempting to reveal insufficiencies of quantum mechanics and indeed they at least proved the research on quantum mechanics at this state was incomplete.
The Einstein-Podolsky-Rosen Paradoxon
The essence of the paradox is that particles can interact in such a way that it is possible to measure both their position and their momentum more accurately than Heisenberg’s uncertainty principle allows , unless measuring one particle instantaneously affects the other to prevent this accuracy, which would involve information being transmitted faster than light as forbidden by the theory of relativity (“spooky action at a distance”). This consequence had not previously been noticed and seemed unreasonable at the time; the phenomenon involved is now known as quantum entanglement. According to quantum mechanics, under some conditions, a pair of quantum systems may be described by a single wave function, which encodes the probabilities of the outcomes of experiments that may be performed on the two systems, whether jointly or individually. The routine explanation of this effect was, at that time, provided by Heisenberg’s uncertainty principle. Physical quantities come in pairs called conjugate quantities. Examples of such conjugate pairs are (position, momentum), (time, energy), and (angular position, angular momentum). When one quantity was measured, and became determined, the conjugated quantity became indeterminate. Heisenberg explained this uncertainty as due to the quantization of the disturbance from measurement.
After working for Einstein, Rosen was suggested to continue his work in Israel. Both scientists began focusing on wormholes after discovering a mathematical method for wormholes able to connect certain areas in space. These Einstein-Rosen bridges were found by mating the mathematical solutions of black holes and white holes through using Einstein’s field equations from 1915. The Einstein-Rosen bridges, also called Schwarzschild wormholes were completely theoretical, but John A. Wheeler and Robert W. Fuller proved these wormholes in 1962 to be unstable. A wormhole can be visualized as a tunnel with two ends, each at separate points in spacetime (i.e., different locations and/or different points of time), or by a transcendental bijection of the spacetime continuum. Wormholes are consistent with the general theory of relativity, but whether wormholes actually exist remains to be seen. A wormhole could connect extremely long distances such as a billion light years or more, short distances such as a few meters, different universes, or different points in time.
According to general relativity, the gravitational collapse of a sufficiently compact mass forms a singular Schwarzschild black hole. In the Einstein–Cartan–Sciama–Kibble theory of gravity, however, it forms a regular Einstein–Rosen bridge. This theory extends general relativity by removing a constraint of the symmetry of the affine connection and regarding its antisymmetric part, the torsion tensor, as a dynamical variable. Torsion naturally accounts for the quantum-mechanical, intrinsic angular momentum (spin) of matter. The minimal coupling between torsion and Dirac spinors generates a repulsive spin–spin interaction that is significant in fermionic matter at extremely high densities. Such an interaction prevents the formation of a gravitational singularity. Instead, the collapsing matter reaches an enormous but finite density and rebounds, forming the other side of the bridge.
Wormholes in Science Fiction
However, wormholes not only fascinated scientists, also science fiction writers increased their interest in them. Numerous writers in literature, television and films used and still use wormholes to transport whole star ships or travel through time as in Star Trek’s movie from 2009 in which Spock  and Nero use (fictional) red matter to build artificial black holes and travel back in time. Contrary to physics, there are no limits in science fiction and even in Star Trek, a completely stable wormhole near the planet Bajor can be found, unique also in the Star Trek universe. A notable science fiction novel is also ‘The Forever War‘ by Joe Haldeman from 1974. In the plot, interstellar travel is possible through collapsars, another word for black holes. The plot is leaned on the theory by Einstein and Rosen, claiming that there may be bridges located in the black holes.
Rosen’s Later Life
Rosen later was professor of theoretical physics at the University of Kiev (on Einstein’s recommendation) and from 1941 at the University of North Carolina at Chapel Hill before going to Israel, where he was professor at the Technion in Haifa from 1953 and founder of the Institute of Theoretical Physics there. He was temporarily head of the Physics Department and the Faculty of Nuclear Engineering there. In 1977 he became Distinguished Professor at the Technion. He was emeritus in 1979, but continued to teach gravitational physics at the Technion (as Gerard Swope Professor Emeritus) until 1991. In Israel, he was also involved in building the engineering education at Ben Gurion University in Be’er Scheva (1969-1971 he was Dean of Engineering there).
Nathan Rosen died on December 18, 1995 in Haifa.
References and Further Reading:
-  Nathan Rosen at New York Times Online
-  Nathan Rosen Biography
-  Wormholes at NASA’s Website
-  Albert Einstein revolutionized Physics, SciHi Blog, March 14, 2018
-  James Chadwick and the Discovery of the Neutron, SciHi Blog, February 27, 2018
-  Sir Arthur Eddington – The Man who Proved Einstein’s General Relativity, SciHi Blog, November 22, 2012
-  The Annus Mirabilis in Physics – Albert Einstein and the Year 1905, SciHi Blog, June 30, 2012
-  Albert Abraham Michelson and the Famous Experiment that lead to Einstein’s Special Relativity Theory, SciHi Blog, December 19, 2012
-  Nathan Rosen at Wikidata
-  A life is like a garden – Leonard Nimoy, SciHi Blog, February 28, 2015.
-  Werner Heisenberg and the Uncertainty Principle, SciHi Blog, December 5, 2012.
-  Nathan Rosen at the Mathematics Genealogy Project
-  A. Einstein and N. Rosen, The Particle Problem in the General Theory of Relativity (PDF; 908 kB), Phys. Rev. 48, 73–77 (1935)
-  Timeline of Quantum Physics People, via DBpedia and Wikidata


The effort to build a fully functional quantum computer is one of the most exciting engineering challenges today. We hear of a new potential breakthrough almost every week that gets researchers closer to achieving this goal. But with every new breakthrough comes the question of how quantum technology will affect security.
There are certainly reasons for concern. If a quantum computer were to appear today, virtually all internet communication would become insecure. Even if the technology emerges some years from now, it will still be able to open the secret communications of today. No matter how you cut it, quantum computing will have a profound effect on today’s security infrastructure, and organizations of all kinds would be wise to consider the security implications before it’s too late.
Protocols and Primitives
Cryptographic protocols, such as Secure Sockets Layer (SSL), Transport Layer Security (TLS) and Hypertext Transfer Protocol Secure (HTTPS), ensure that communication between two parties is authenticated and private. The building blocks of these protocols are various cryptographic primitives, such as authentication schemes (e.g., keyed-hash message authentication code, or HMAC), block ciphers (e.g., advanced encryption standard, or AES), digital signatures (e.g., Digital Signature Algorithm, or DSA) and encryption schemes (e.g., RSA). For the most part, protocols are constructed from primitives in a modular way. Therefore, if the primitives satisfy their respective security properties, so will the protocols.
Cryptographic primitives can be divided into two classes: symmetric and asymmetric. The latter is often referred to as public key. Symmetric primitives assume the parties have a preshared secret key before beginning communication, whereas asymmetric primitives assume the parties begin with no common secret information.
Most protocols employ the hybrid approach. The communicating parties first use public key primitives to secretly exchange a string before it switches over to the much faster symmetric key primitives, using this common string as the secret key.
An Existential Threat?
Quantum computers will affect symmetric and asymmetric primitives differently. According to Grover’s algorithm, quantum computers are able to brute-force search all 2n possible n-bit keys in 2n/2 time, which would require us to double the key sizes to maintain the same level of security. For the most part, this is all quantum computers can do against the symmetric primitives in use today. While certainly notable, this by itself does not pose an existential threat to symmetric cryptography.
Public key primitives are a different story. Virtually all public key primitives used today require that either factoring large integers or finding discrete logarithms in finite groups is a hard problem. However, quantum computers can easily solve these problems using Shor’s algorithm.
In other words, the bad news is that quantum computers break public key primitives. The good news is that the only primitives that really need fixing are digital signatures and public key encryption, because these are enough to construct virtually every critical internet security protocol. Once these primitives are in place, all the important protocols can be made quantum-safe.
Constructing Quantum-Safe Primitives
As it turns out, public key encryption and digital signature schemes that we believe to be quantum-safe have existed since the late 1970s, even before people were aware of the potential problems quantum computing would pose to cryptography. The problem with these constructions, however, is that they are very inefficient. Keys and/or messages are on the order of megabytes.
One of the real breakthroughs in the field of quantum-safe cryptography in the past decade has been the development of new techniques that allow for extremely practical constructions. Lattice-based cryptography has become one of the most fruitful approaches for constructing primitives.
Lattice-based cryptography is rooted in linear algebra. Suppose that one is given a square, full-rank matrix A and a value b=Ax mod p where x is a vector with 0/1 coefficients and p is a small (e.g., 10-bit) prime. One is then tasked with finding x. This problem has a unique solution, x, which is actually quite easy to find using Gaussian elimination.
On the other hand, if one is given a slightly noisy version of Ax, that is Ax+e mod p, where e is some random vector with 0/1 coefficients, then for matrices of large-enough dimension (say, around 512), this problem becomes surprisingly difficult. This type of problem is related to both the subset sum and the learning parity with noise problems that have been widely studied since the 1980s and have not succumbed to any algorithmic attacks, either classical or quantum.
Much of the past decade’s research focused on increasing our understanding of different versions of the problem described above and building schemes based on their presumed hardness. In my view, this research line has been a great success. Performancewise, the most efficient lattice-based encryption and signature schemes are much faster than those based on RSA, and have key and output lengths of a similar size.
Researchers have also made exciting progress toward building cryptographic primitives, such as fully homomorphic encryption, which allows for evaluation of encrypted data whose only instantiations are based on the same linear-algebraic assumptions. Lattice-based cryptography provides fast, quantum-safe, fundamental primitives and allows for constructions of primitives that were previously thought impossible. This combination has established lattice-based cryptography as one of the most fascinating research fields with potential to become the backbone of real-world cryptography in the near future.
Today’s Solution to Tomorrow’s Problem
Can quantum-safe cryptography be used today? The short answer is yes. Lattice-based primitives are efficient and have already been successfully plugged into the TLS and Internet Key Exchange (IKE) protocols. But since quantum computers are not yet here, few security professionals are likely to abandon today’s cryptography for a different approach. After all, any new technology comes with growing pains, and IT professionals cannot afford to make even the smallest mistake when dealing with information security. A slow migration towards lattice cryptography is probably the best bet.
An organization looking to future-proof its data should first use lattice cryptography in tandem with traditional primitives. This approach should secure the organization’s data as long as at least one of these constructions is secure. This would remove all the risk of introducing a new technology. If implemented correctly, all communication will be quantum-safe. All it takes is a couple of extra kilobytes of data per communication session.
In short, quantum computers may be coming sooner than you think, so be ready!