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  • richardmitnick 4:46 pm on May 21, 2016 Permalink | Reply
    Tags: , , Quantum Computing, The Power of Entanglement: A Conversation with Fernando Brandão   

    From Caltech: “The Power of Entanglement: A Conversation with Fernando Brandão” 

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    Written by Lori Dajose

    Fernando Brandão. Credit: Courtesy of F. Brandão

    Computers are a ubiquitous part of modern technology, utilized in smartphones, cars, kitchen appliances, and more. But there are limits to their power. New faculty member Fernando Brandão, the Bren Professor of Theoretical Physics, studies how quantum computers may someday revolutionize computing and change the world’s cryptographic systems.

    What do you do?

    My research is in quantum information science, a field which seeks to merge two of the biggest discoveries of the last century: quantum mechanics and computer science. Particularly, I am interested in studying quantum entanglement.

    Entanglement is a special kind of correlations only found in quantum mechanics. We are all familiar with the concept of correlations. For example, the weather in Southern California is pretty well-correlated from one day to the next—if it is sunny today, it will likely be sunny tomorrow. Quantum systems can be correlated in an even stronger way. Entanglement was first seen as a weird feature of quantum mechanics—Einstein famously referred to it as a “spooky action at a distance.” But with the advancement of quantum information science, entanglement is now seen as a physical resource that can be used in information processing, such as in quantum cryptography and quantum computing. One part of my research is to develop methods to characterize and quantify entanglement. Another is to find new applications of entanglement, both in quantum information science and in other areas of physics.

    What is a quantum computer?

    At the most basic level, computers are made up of millions of simple switches called transistors. Transistors have two states—on or off—which can be represented as the zeroes or ones that make up binary code. With a quantum computer, its basic building blocks (called qubits) can be either a one or a zero, or they can simultaneously exist as a one and a zero. This property is called the superposition principle and, together with entanglement and quantum interference, it is what allows quantum computers to, theoretically, solve certain problems much faster than normal, or “classical,” computers could. It will take a long time until we actually have quantum computers, but we are already trying to figure out what they can do.

    What is an example of a problem only solvable by a quantum computer?

    It is a mathematical fact that any integer number can be factored into the product of prime numbers. For example, 21 can be written as 3 x 7, which are both prime numbers. Factoring a number is pretty straightforward when it is a small number, but factoring a number with a thousand digits would actually take a classical computer billions and billions of years—more time than the age of the universe! However, in 1994 Peter Shor showed that quantum computers would be so powerful that they would be able to factor numbers very quickly. This is important because many current cryptographic systems—the algorithms that protect your credit card information when you make a purchase online, for example—are based on factoring large numbers with the assumption that some codes cannot be cracked for billions of years. Quantum computing would change the way we do cryptography.

    What got you interested in quantum information?

    During my undergraduate education, I was looking online for interesting things to read, and found some lecture notes about quantum computation which turned out to be by Caltech’s John Preskill [Richard P. Feynman Professor of Theoretical Physics]. They are a beautiful set of lecture notes and they were really my first contact with quantum information and, in fact, with quantum mechanics. I have been working in quantum information science ever since. And now that I’m on the Caltech faculty, I have an office right down the hall from Preskill!

    What is your background?

    I am originally from Brazil. I did my bachelors and masters degrees there in physics, and my PhD at Imperial College London. After that, I moved among London, Brazil, and Switzerland for various postdocs. Then I became faculty at University College London. Last year I was working with the research group at Microsoft, and now I am here at Caltech. The types of problems I have worked on have varied with time, but they are all within quantum information theory. It is stimulating to see how the field has progressed in the past 10 years since I started working on it.

    What are you particularly excited about now that you are at Caltech?

    I can’t think of a better place than Caltech to do quantum information. There are many people working on it from different angles, for example, in the intersection of quantum information and condensed-matter physics, or high-energy physics. I am very excited that I get to collaborate with them.

    What do you like to do in your free time?

    I used to go traveling a lot, but six months ago my wife and I had a baby, so he is keeping us busy. Along with work and exercise, that basically takes up all my time.

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    The California Institute of Technology (commonly referred to as Caltech) is a private research university located in Pasadena, California, United States. Caltech has six academic divisions with strong emphases on science and engineering. Its 124-acre (50 ha) primary campus is located approximately 11 mi (18 km) northeast of downtown Los Angeles. “The mission of the California Institute of Technology is to expand human knowledge and benefit society through research integrated with education. We investigate the most challenging, fundamental problems in science and technology in a singularly collegial, interdisciplinary atmosphere, while educating outstanding students to become creative members of society.”
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  • richardmitnick 3:52 pm on May 21, 2016 Permalink | Reply
    Tags: , Computing a secret, Quantum Computing, , unbreakable key   

    From U Waterloo: “Computing a secret, unbreakable key” 

    U Waterloo bloc

    University of Waterloo

    May 20, 2016
    Nick Manning
    University of Waterloo

    What once took months by some of the world’s leading scientists can now be done in seconds by undergraduate students thanks to software developed at the University of Waterloo’s Institute for Quantum Computing, paving the way for fast, secure quantum communication.

    Researchers at the Institute for Quantum Computing (IQC) at the University of Waterloo developed the first available software to evaluate the security of any protocol for Quantum Key Distribution (QKD).

    QKD allows two parties, Alice and Bob, to establish a shared secret key by exchanging photons. Photons behave according to the laws of quantum mechanics, and the laws state that you cannot measure a quantum object without disturbing it. So if an eavesdropper, Eve, intercepts and measures the photons, she will cause a disturbance that is detectable by Alice and Bob. On the other hand, if there is no disturbance, Alice and Bob can guarantee the security of their shared key.

    In practice, loss and noise in an implementation always leads to some disturbance, but a small amount of disturbance implies a small amount of information about the key is available to Eve. Characterizing this amount of information allows Alice and Bob to remove it from Eve at the cost of the length of the resulting final key. The main theoretical problem in QKD is how to calculate the allowed length of this final secret key for any given protocol and the experimentally observed disturbance.

    A mathematical approach was still needed to perform this difficult calculation. The researchers opted to take a numerical approach, and for practical reasons they transformed the key rate calculation to the dual optimization problem.

    “We wanted to develop a program that would be fast and user-friendly. It also needs to work for any protocol,” said Patrick Coles, an IQC postdoctoral fellow. “The dual optimization problem dramatically reduced the number of parameters and the computer does all the work.”


    The paper, Numerical approach for unstructured quantum key distribution, published in Nature Communications today presented three findings. First, the researchers tested the software against previous results for known studied protocols. Their results were in perfect agreement. They then studied protocols that had never been studied before. Finally, they developed a framework to inform users how to enter the data using a new protocol into the software.

    “The exploration of QKD protocols so far concentrated on protocols that allowed tricks to perform the security analysis. The work by our group now frees us to explore protocols that are adapted to the technological capabilities” noted Norbert Lütkenhaus, a professor with IQC and the Department of Physics and Astronomy at the University of Waterloo.

    See the full article here .

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    In just half a century, the University of Waterloo, located at the heart of Canada’s technology hub, has become a leading comprehensive university with nearly 36,000 full- and part-time students in undergraduate and graduate programs.

    Consistently ranked Canada’s most innovative university, Waterloo is home to advanced research and teaching in science and engineering, mathematics and computer science, health, environment, arts and social sciences. From quantum computing and nanotechnology to clinical psychology and health sciences research, Waterloo brings ideas and brilliant minds together, inspiring innovations with real impact today and in the future.

    As home to the world’s largest post-secondary co-operative education program, Waterloo embraces its connections to the world and encourages enterprising partnerships in learning, research, and commercialization. With campuses and education centres on four continents, and academic partnerships spanning the globe, Waterloo is shaping the future of the planet.

  • richardmitnick 11:39 am on May 14, 2016 Permalink | Reply
    Tags: , , , Quantum Computing   

    From NOVA: “A Quantum Computer Has Been Hooked Up to the Cloud For the First Time” 



    04 May 2016
    Allison Eck

    You can now entangle quantum qubits directly from your smartphone.

    A team at IBM has announced today that it has hooked up a quantum processor—housed at the IBM T.J. Watson Research Center in New York—to the cloud. For the first time in history, non-scientists and scientists alike can run quantum experiments from their desktop or mobile devices.

    “It’s really about starting to have a new community of quantum learners,” said Jay Gambetta, manager of the Theory of Quantum Computing and Information Group at IBM. “We’re trying to take the mysteriousness out of quantum.”

    The five-qubit processor is maintained at a temperature of 15 millikelvin. That’s 180 times colder than outer space.

    IBM is calling the cloud-based quantum platform the IBM Research Quantum Experience (which consists of a simulator as well as the live processor), and it’s a step in the direction of creating a universal quantum computer: one that can perform any calculation that is in the realm of what quantum mechanics predicts. No such computer exists today, but IBM suspects that researchers will find the means to develop one within the next decade.

    Quantum computing is a complicated beast compared to classical computing. Classical computers use bits to process information, where a bit represents either a zero or a one. Quantum computing, on other other hand, employs qubits—which represent either a zero, a one, or a superposition of both.

    IBM’s quantum computer holds five superconducting qubits, a relatively small amount. The most expensive modern-day classical computer could emulate a 30- or 40-qubit system, the researchers say. So it’s not as though IBM’s cloud-based quantum processor is going to solve anything that scientists can’t already figure out using a classical computer. Instead, the strength of IBM’s processor is derived from its use as an educational tool—anyone who is curious can experiment, play with real qubits, and explore tutorials related to quantum computing.

    In addition, scientists who access the processor will be able to use it to develop a better intuition for quantum computing. “We’ll know more about nature itself when we understand these algorithms,” Gambetta said. Specifically, experts can become more skilled at parsing quantum “noise,” or the uncertainty in physical characteristics of quantum nature. If they can minimize uncertainty—flukes in the system that cause the quantum computer to malfunction—in a small, five-qubit processor, then they can scale those lessons to create stronger quantum computers in the future.

    Eventually, given the invention of 50- to 100-qubit processors, scientists may be able to deduce the complex behavior of molecules using quantum computing. They could even make significant strides in artificial intelligence, processing big data, and more.

    IBM’s announcement also marks the launch of the IBM Research Frontiers Institute, a consortium of organizations from various industries (including Samsung and Honda) that plans to collaborate on ground-breaking computing technologies. As classical computing becomes less relevant and Moore’s law starts to fade, such projects will become even more necessary. As Gambetta noted, the amount we know about quantum computing now is similar to what we knew about classical computing in the 1950s and 60s. It’s back to square one.

    “Everything you know about computing, you have to relearn it,” he said.



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    NOVA is the highest rated science series on television and the most watched documentary series on public television. It is also one of television’s most acclaimed series, having won every major television award, most of them many times over.

  • richardmitnick 11:13 am on April 1, 2016 Permalink | Reply
    Tags: , , Quantum Computing   

    From Griffith: “Unlocking the gates to quantum computing” 

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    Griffith University

    An artist’s rendering of the quantum Fredkin (controlled-SWAP) gate, powered by entanglement, operating on photonic qubits. (Image by Raj Patel and Geoff Pryde, Center for Quantum Dynamics, Griffith University.)

    Researchers have overcome one of the key challenges to quantum computing by simplifying a complex quantum logic operation. They demonstrated this by experimentally realising a challenging circuit, the quantum Fredkin gate, for the first time.

    “The allure of quantum computers is the unparalleled processing power that they provide compared to current technology,” said Dr Raj Patel from Griffith’s Centre for Quantum Dynamics.

    “Much like our everyday computer, the brains of a quantum computer consist of chains of logic gates, although quantum logic gates harness quantum phenomena.”

    The main stumbling block to actually creating a quantum computer has been in minimising the number of resources needed to efficiently implement processing circuits.

    “Similar to building a huge wall out lots of small bricks, large quantum circuits require very many logic gates to function. However, if larger bricks are used the same wall could be built with far fewer bricks,” said Dr Patel.

    In an experiment involving researchers from Griffith University and the University of Queensland, it was demonstrated how to build larger quantum circuits in a more direct way without using small logic gates.

    At present, even small and medium scale quantum computer circuits cannot be produced because of the requirement to integrate so many of these gates into the circuits. One example is the Fredkin (controlled- SWAP) gate. This is a gate where two qubits are swapped depending on the value of the third.

    Usually the Fredkin gate requires implementing a circuit of five logic operations. The research team used the quantum entanglement of photons – particles of light – to implement the controlled-SWAP operation directly.

    “There are quantum computing algorithms, such as Shor’s algorithm for finding prime factors, that require the controlled-SWAP operation,” said Professor Tim Ralph from the University of Queensland.

    The quantum Fredkin gate can also be used to perform a direct comparison of two sets of qubits (quantum bits) to determine whether they are the same or not. This is not only useful in computing but is an essential feature of some secure quantum communication protocols where the goal is to verify that two strings, or digital signatures, are the same.”

    Professor Geoff Pryde, from Griffith’s Centre for Quantum Dynamics, is the project’s chief investigator.

    “What is exciting about our scheme is that it is not limited to just controlling whether qubits are swapped, but can be applied to a variety of different operations opening up ways to control larger circuits efficiently,” said Professor Pryde.

    “This could unleash applications that have so far been out of reach.”

    The team is part of the Australian Research Council’s Centre for Quantum Computation and Communication Technology, an effort to exploit Australia’s strong expertise in developing quantum information technologies.

    The research has been published as A quantum Fredkin gate in Science Advances [no link present].

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    Griffith U Campus

    In 1971, Griffith was created to be a new kind of university—one that offered new degrees in progressive fields such as Asian studies and environmental science. At the time, these study areas were revolutionary—today, they’re more important than ever.

    Since then, we’ve grown into a comprehensive, research-intensive university, ranking in the top 5% of universities worldwide. Our teaching and research spans five campuses in South East Queensland and all disciplines, while our network of more than 120,000 graduates extends around the world.

    Griffith continues the progressive traditions of its namesake, Sir Samuel Walker Griffith, who was twice the Premier of Queensland, the first Chief Justice of the High Court of Australia, and the principal author of the Australian Constitution.

  • richardmitnick 7:41 am on March 18, 2016 Permalink | Reply
    Tags: , , Quantum Computing   

    From Physics: “Q&A: Building Quantum Computers with Superconductors” 

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    March 17, 2016
    No writer credit found

    Britton Plourde explains why he’s homed in on using superconducting circuits to make a quantum computer.

    Quantum approach to big data. MIT
    Quantum approach to big data. MIT

    Circuits have always captivated Britton Plourde. As a kid he designed and built model airplanes, a hobby that required wiring together numerous electronic parts. In graduate school he turned to fabricating even smaller and more complex circuits made from micrometer-sized superconducting materials. Today Plourde heads a group at Syracuse University in New York that is trying to build superconducting elements for a quantum computer. To learn more, Physics met up with Plourde at his lab at Syracuse.

    –Katherine Wright
    What are you working on at the moment?

    My focus is on quantum bits (qubits): How to make them, how to make them better, and how to increase the qubit lifetime—the time it takes the qubit to decay from a quantum superposition of states to a classical state. I’m also trying to understand ways to make multiple qubits interact with each other, to do things like generate entangled states between several qubits.

    Your main interest is superconducting qubits. What are they?

    They are microfabricated electrical circuits that contain superconducting elements, and they behave like artificial atoms with discrete energy levels. Two different electrical excitations in the circuit correspond to the two states of the qubit.

    There are many ways to make qubits. Why have you focused on using superconductors?

    Right before I started my postdoc at UC Berkley in 2000, a group in Japan had made the very first superconducting qubit. At the time, superconducting circuits seemed like an intriguing research direction, but they only had lifetimes of a nanosecond or so—nowhere close to being practical. But superconducting elements can, in principle, be integrated into a large processor using techniques similar to those developed by the semiconductor industry, which could allow for the rapid manufacture of billions of superconducting circuits. We wanted to leverage the same technology for quantum computers. But first we had to figure out if we could make robust quantum states from superconducting circuits. Back in 2000 that was a big if.

    And now?

    Right now we’re at a level where it’s feasible to build a small-scale quantum processor with a superconducting circuit. It’s possible to make systems with ten qubits, maybe a little bit beyond that. More importantly, qubit lifetimes have improved by 5 orders of magnitude since 2000. Also there have been key steps towards implementing circuits and control techniques that can correct for quantum errors, a critical element for building a quantum computer. It’s exciting!

    What calculations have been run on superconducting quantum computers?

    There have been several initial demonstrations of quantum algorithms, including factoring 15 into 5 and 3. The factoring algorithm was one of the original algorithms that kicked off research into quantum computers because there is no known efficient classical algorithm that can factor large numbers into primes. As the number gets really big, the time for the computation blows up rapidly. If a number contained 2000 bits—600 digits—the time needed to factor it would be longer than the age of the Universe. A quantum computer could factor such a large number in roughly a day, but the computer would need well over a million qubits, amongst other things, so we’re a little ways off!

    Are there other things a quantum computer could do?

    There is currently a lot of interest in quantum simulation—using a quantum computer to simulate another quantum system, such as complex molecules, which are difficult to simulate numerically on a classical computer. This wouldn’t need anywhere near a million qubits, but could be done with say tens or hundreds. [Simulation] will probably be the first breakthrough for quantum processors in which they perform faster than a classical approach.

    Do you think we’ll ever have personal quantum computers?

    No. There is not going to be a day when everybody has a quantum computer on their desks. And quantum computers will not be able to solve all problems faster than a classical computer—that’s a common misconception. There are many problems where a quantum computer would be slower, or simply wouldn’t make sense to use. You wouldn’t run a spreadsheet or write your term paper for history class on a quantum computer.

    Why is 2016 a great time to be working on quantum computing?

    The field is at this exciting intersection between fundamental physics and cutting-edge technology. We’re now able to engineer artificial quantum systems and to study fundamental properties of quantum mechanics in big systems that we can control. That’s neat. And several companies are trying to build superconducting quantum computers. Someone getting an advanced degree in this area certainly has good opportunities.

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    Physicists are drowning in a flood of research papers in their own fields and coping with an even larger deluge in other areas of physics. How can an active researcher stay informed about the most important developments in physics? Physics highlights a selection of papers from the Physical Review journals. In consultation with expert scientists, the editors choose these papers for their importance and/or intrinsic interest. To highlight these papers, Physics features three kinds of articles: Viewpoints are commentaries written by active researchers, who are asked to explain the results to physicists in other subfields. Focus stories are written by professional science writers in a journalistic style and are intended to be accessible to students and non-experts. Synopses are brief editor-written summaries. Physics provides a much-needed guide to the best in physics, and we welcome your comments (physics@aps.org).

  • richardmitnick 9:01 am on January 26, 2016 Permalink | Reply
    Tags: A new quantum approach to big data, , , Quantum Computing   

    From MIT: “A new quantum approach to big data” 

    MIT News

    January 25, 2016
    David L. Chandler | MIT News Office

    Quantum approach to big data
    This diagram demonstrates the simplified results that can be obtained by using quantum analysis on enormous, complex sets of data. Shown here are the connections between different regions of the brain in a control subject (left) and a subject under the influence of the psychedelic compound psilocybin (right). This demonstrates a dramatic increase in connectivity, which explains some of the drug’s effects (such as “hearing” colors or “seeing” smells). Such an analysis, involving billions of brain cells, would be too complex for conventional techniques, but could be handled easily by the new quantum approach, the researchers say. Courtesy of the researchers

    From gene mapping to space exploration, humanity continues to generate ever-larger sets of data — far more information than people can actually process, manage, or understand.

    Machine learning systems can help researchers deal with this ever-growing flood of information. Some of the most powerful of these analytical tools are based on a strange branch of geometry called topology, which deals with properties that stay the same even when something is bent and stretched every which way.

    Such topological systems are especially useful for analyzing the connections in complex networks, such as the internal wiring of the brain, the U.S. power grid, or the global interconnections of the Internet. But even with the most powerful modern supercomputers, such problems remain daunting and impractical to solve. Now, a new approach that would use quantum computers to streamline these problems has been developed by researchers at MIT, the University of Waterloo, and the University of Southern California.

    The team describes their theoretical proposal this week in the journal Nature Communications. Seth Lloyd, the paper’s lead author and the Nam P. Suh Professor of Mechanical Engineering, explains that algebraic topology is key to the new method. This approach, he says, helps to reduce the impact of the inevitable distortions that arise every time someone collects data about the real world.

    In a topological description, basic features of the data (How many holes does it have? How are the different parts connected?) are considered the same no matter how much they are stretched, compressed, or distorted. Lloyd explains that it is often these fundamental topological attributes “that are important in trying to reconstruct the underlying patterns in the real world that the data are supposed to represent.”

    It doesn’t matter what kind of dataset is being analyzed, he says. The topological approach to looking for connections and holes “works whether it’s an actual physical hole, or the data represents a logical argument and there’s a hole in the argument. This will find both kinds of holes.”

    Using conventional computers, that approach is too demanding for all but the simplest situations. Topological analysis “represents a crucial way of getting at the significant features of the data, but it’s computationally very expensive,” Lloyd says. “This is where quantum mechanics kicks in.” The new quantum-based approach, he says, could exponentially speed up such calculations.

    Lloyd offers an example to illustrate that potential speedup: If you have a dataset with 300 points, a conventional approach to analyzing all the topological features in that system would require “a computer the size of the universe,” he says. That is, it would take 2300 (two to the 300th power) processing units — approximately the number of all the particles in the universe. In other words, the problem is simply not solvable in that way.

    “That’s where our algorithm kicks in,” he says. Solving the same problem with the new system, using a quantum computer, would require just 300 quantum bits — and a device this size may be achieved in the next few years, according to Lloyd.

    “Our algorithm shows that you don’t need a big quantum computer to kick some serious topological butt,” he says.

    There are many important kinds of huge datasets where the quantum-topological approach could be useful, Lloyd says, for example understanding interconnections in the brain. “By applying topological analysis to datasets gleaned by electroencephalography or functional MRI, you can reveal the complex connectivity and topology of the sequences of firing neurons that underlie our thought processes,” he says.

    The same approach could be used for analyzing many other kinds of information. “You could apply it to the world’s economy, or to social networks, or almost any system that involves long-range transport of goods or information,” says Lloyd, who holds a joint appointment as a professor of physics. But the limits of classical computation have prevented such approaches from being applied before.

    While this work is theoretical, “experimentalists have already contacted us about trying prototypes,” he says. “You could find the topology of simple structures on a very simple quantum computer. People are trying proof-of-concept experiments.”

    Ignacio Cirac, a professor at the Max Planck Institute of Quantum Optics in Munich, Germany, who was not involved in this research, calls it “a very original idea, and I think that it has a great potential.” He adds “I guess that it has to be further developed and adapted to particular problems. In any case, I think that this is top-quality research.”

    The team also included Silvano Garnerone of the University of Waterloo in Ontario, Canada, and Paolo Zanardi of the Center for Quantum Information Science and Technology at the University of Southern California. The work was supported by the Army Research Office, Air Force Office of Scientific Research, Defense Advanced Research Projects Agency, Multidisciplinary University Research Initiative of the Office of Naval Research, and the National Science Foundation.

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  • richardmitnick 8:29 pm on December 20, 2015 Permalink | Reply
    Tags: , , Quantum Computing   

    From MIT Tech Review: “Google’s Quantum Dream Machine” 

    MIT Technology Review
    M.I.T Technology Review

    December 18, 2015
    Tom Simonite

    John Martinis has been researching how quantum computers could work for 30 years. Now he could be on the verge of finally making a useful one. No image credit.

    John Martinis used the arm of his reading glasses to indicate the spot where he intends to demonstrate an almost unimaginably powerful new form of computer in a few years. It is a cylindrical socket an inch and a half across, at the bottom of a torso-sized stack of plates, blocks, and wires of brass, copper, and gold. The day after I met with him this fall, he loaded the socket with an experimental superconducting chip etched with a microscopic Google logo and cooled the apparatus to a hundredth of a degree Celsius above absolute zero. To celebrate that first day of testing the machine, Martinis threw what he called “a little party” at a brewpub with colleagues from his newly outfitted Google lab in Santa Barbara, California.

    That party was nothing compared with the celebration that will take place if Martinis and his group can actually create the wonder computer they seek. Because it would harness the strange properties of quantum physics that arise in extreme conditions like those on the ultracold chip, the new computer would let a Google coder run calculations in a coffee break that would take a supercomputer of today millions of years. The software that Google has developed on ordinary computers to drive cars or answer questions could become vastly more intelligent. And earlier-stage ideas bubbling up at Google and its parent company, such as robots that can serve as emergency responders or software that can converse at a human level, might become real.

    The theoretical underpinnings of quantum computing are well established. And physicists can build the basic units, known as qubits, out of which a quantum computer would be made. They can even operate qubits together in small groups. But they have not made a fully working, practical quantum computer.

    Martinis is a towering figure in the field: his research group at the University of California, Santa Barbara, has demonstrated some of the most reliable qubits around and gotten them running some of the code a quantum computer would need to function. He was hired by Google in June 2014 after persuading the company that his team’s technology could mature rapidly with the right support. With his new Google lab up and running, Martinis guesses that he can demonstrate a small but useful quantum computer in two or three years. “We often say to each other that we’re in the process of giving birth to the quantum computer industry,” he says.

    Google and quantum computing are a match made in algorithmic heaven. The company is often said to be defined by an insatiable hunger for data. But Google has a more pressing strategic addiction: to technology that extracts information from data, and even creates intelligence from it. The company was founded to commercialize an algorithm for ranking Web pages, and it built its financial foundations with systems that sell and target ads. More recently, Google has invested heavily in the development of AI software that can learn to understand language or images, perform basic reasoning, or steer a car through traffic—all things that remain tricky for conventional computers but should be a breeze for quantum ones. “Machine learning is a core, transformative way by which we’re rethinking how we’re doing everything,” Google’s CEO, Sundar Pichai, recently informed investors. Supporting that effort would be the first of many jobs for Martinis’s new quantum industry.

    Dream maker

    As recently as last week the prospect of a quantum computer doing anything useful within a few years seemed remote. Researchers in government, academic, and corporate labs were far from combining enough qubits to make even a simple proof-of-principle machine. A well-funded Canadian startup called D-Wave Systems sold a few of what it called “the world’s first commercial quantum computers” but spent years failing to convince experts that the machines actually were doing what a quantum computer should (see The CIA and Jeff Bezos Bet on Quantum Computing).

    Then NASA summoned journalists to building N-258 at its Ames Research Center in Mountain View, California, which since 2013 has hosted a D-Wave computer bought by Google. There Hartmut Neven, who leads the Quantum Artificial Intelligence lab Google established to experiment with the D-Wave machine, unveiled the first real evidence that it can offer the power proponents of quantum computing have promised. In a carefully designed test, the superconducting chip inside D-Wave’s computer—known as a quantum annealer—had performed 100 million times faster than a conventional processor.

    However, this kind of advantage needs to be available in practical computing tasks, not just contrived tests. “We need to make it easier to take a problem that comes up at an engineer’s desk and put it into the computer,” said Neven, a talkative machine-learning expert. That’s where Martinis comes in. Neven doesn’t think D-Wave can get a version of its quantum annealer ready to serve Google’s engineers quickly enough, so he hired Martinis to do it. “It became clear that we can’t just wait,” Neven says. “There’s a list of shortcomings that need to be overcome in order to arrive at a real technology.” He says the qubits on D-Wave’s chip are too unreliable and aren’t wired together thickly enough. (D-Wave’s CEO, Vern Brownell, responds that he’s not worried about competition from Google.)

    Google will be competing not only with whatever improvements D-Wave can make, but also with Microsoft and IBM, which have substantial quantum computing projects of their own (see Microsoft’s Quantum Mechanics and IBM Shows Off a Quantum Computing Chip). But those companies are focused on designs much further from becoming practically useful. Indeed, a rough internal time line for Google’s project estimates that Martinis’s group can make a quantum annealer with 100 qubits as soon as 2017. D-Wave’s latest chip already has 1,097 qubits, but Neven says a high-quality chip with fewer qubits will probably be useful for some tasks nonetheless. A quantum annealer can run only one particular algorithm, but it happens to be one well suited to the areas Google most cares about. The applications that could particularly benefit include pattern recognition and machine learning, says William Oliver, a senior staff member at MIT Lincoln Laboratory who has studied the potential of quantum computing.

    John Martinis, 57, is the perfect person to wrestle a mind-bogglingly complex strand of quantum physics research into a new engineering discipline. Not only can he dive into the esoteric math, but he loves to build things. Operating even a single qubit is a puzzle assembled from deep quantum theory, solid-state physics, materials science, microfabrication, mechanical design, and conventional electronics. Martinis, who is tall with a loud, friendly voice, makes a point of personally mastering the theory and technical implementation of every piece. Giving a tour of his new lab at Google, he is as excited about the new soldering irons and machine tools in the conventional workshop area as he is about the more sophisticated equipment that chills chips and operates them. “To me it’s fun,” he says. “I’ve been able to do experiments no one else could do, because I could build my own electronics.”

    This experimental chip, etched with the Google logo, is cooled to just above absolute zero in order to generate quantum effects.No image credit.

    Martinis and his team have to be adept at so many things because qubits are fickle. They can be made in various ways—Martinis uses aluminum loops chilled with tiny currents until they become superconductors—but all represent data by means of delicate quantum states that are easily distorted or destroyed by heat and electromagnetic noise, potentially ruining a calculation.

    Qubits use their fragile physics to do the same thing that transistors use electricity to do on a conventional chip: represent binary bits of information, either 0 or 1. But qubits can also attain a state, called a superposition, that is effectively both 0 and 1 at the same time. Qubits in a superposition can become linked by a phenomenon known as entanglement, which means an action performed on one has instant effects on the other. Those effects allow a single operation in a quantum computer to do the work of many, many more operations in a conventional computer. In some cases, a quantum computer’s advantage over a conventional one should grow exponentially with the amount of data to be worked on.

    The difficulty of creating qubits that are stable enough is the reason we don’t have quantum computers yet. But Martinis has been working on that for more than 11 years and thinks he’s nearly there. The coherence time of his qubits, or the length of time they can maintain a superposition, is tens of microseconds—about 10,000 times the figure for those on D-Wave’s chip.

    Martinis’s confidence in his team’s hardware even has him thinking he can build Google an alternative to a quantum annealer that would be even more powerful. A universal quantum computer, as it would be called, could be programmed to take on any kind of problem, not just one kind of math. The theory behind that approach is actually better understood than the one for annealers, in part because most of the time and money in quantum computing research have been devoted to universal quantum computing. But qubits have not been reliable enough to translate the theory into a working universal quantum computer.

    This structure of metal plates is necessary to cool and shield quantum chips. No image credit.

    Until March, that is, when Martinis and his team became the first to demonstrate qubits that crossed a crucial reliability threshold for a universal quantum computer (see Google Researchers Make Quantum Computing Components More Reliable). They got a chip with nine qubits to run part of an error-checking program, called the surface code, that’s necessary for such a computer to operate (IBM has since gotten part of the surface code working on four qubits). “We demonstrated the technology to a point where I knew we could scale up,” says Martinis. “This was for real.”

    Martinis aims to show off a complete universal quantum computer with about 100 qubits around the same time he delivers Google’s new quantum annealer, in about two years. That would be a milestone in computer science, but it would be unlikely to help Google’s programmers right away. Such is the complexity of the surface code that although a chip with 100 qubits could run the error-checking program, it would be unable to do any useful work in addition to that, says Robert McDermott, who leads a quantum computing research group at the University of Wisconsin. Yet Martinis thinks that once he can get his qubits reliable enough to put 100 of them on a universal quantum chip, the path to combining many more will open up. “This is something we understand pretty well,” he says. “It’s hard to get coherence but easy to scale up.”

    Stupid algorithms

    When Martinis explains why his technology is needed at Google, he doesn’t spare the feelings of the people working on AI. “Machine-learning algorithms are really kind of stupid,” he says, with a hint of wonder in his voice. “They need so many examples to learn.”

    Indeed, the machine learning used by Google and other computing companies is pathetic next to the way humans or animals pick up new skills or knowledge. Teaching a piece of software new tricks, such as how to recognize cars and cats in photos, generally requires thousands or millions of carefully curated and labeled examples. Although a technique called deep learning has recently produced striking advances in the accuracy with which software can learn to interpret images and speech, more complex faculties like understanding the nuances of language remain out of machines’ reach.

    Figuring out how Martinis’s chips can make Google’s software less stupid falls to Neven. He thinks that the prodigious power of qubits will narrow the gap between machine learning and biological learning—and remake the field of artificial intelligence. “Machine learning will be transformed into quantum learning,” he says. That could mean software that can learn from messier data, or from less data, or even without explicit instruction. For instance, Google’s researchers have designed an algorithm they think could allow machine-learning software to pick up a new trick even if as much as half the example data it’s given is incorrectly labeled. Neven muses that this kind of computational muscle could be the key to giving computers capabilities today limited to humans. “People talk about whether we can make creative machines–the most creative systems we can build will be quantum AI systems,” he says.

    More practically, with only D-Wave’s machine to practice on for now, Google’s researchers can’t do much more than speculate about what exactly they could or should do with the chips Martinis is building. Even when they do get their hands on them, it will take time to invent and build the infrastructure needed to operate large numbers of the exotic devices so they can contribute materially to Google’s business.

    Neven is confident that Google’s quantum craftsmen and his team can get through all that. He pictures rows of superconducting chips lined up in data centers for Google engineers to access over the Internet relatively soon. “I would predict that in 10 years there’s nothing but quantum machine learning–you don’t do the conventional way anymore,” he says. A smiling Martinis warily accepts that vision. “I like that, but it’s hard,” he says. “He can say that, but I have to build it.”

    See the full article here .

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  • richardmitnick 10:17 am on December 7, 2015 Permalink | Reply
    Tags: , Quantum Computing,   

    From UNSW: “Major innovation boost for UNSW’s quantum mission” 

    U NSW bloc

    University of New South Wales

    07 Dec 2015
    Denise Knight

    Professor Michelle Simmons

    UNSW Australia welcomes the federal government’s announcement today of a $26 million investment in the University’s world-leading quantum computing research.

    The major funding boost over five years will support the development of silicon quantum computing technology in Australia in association with the Australian Research Council (ARC) Centre for Quantum Computation and Communication Technology, headquartered at UNSW.

    It is based on a focused, ambitious and targeted program to build a 10 qubit prototype – demonstrating all the fundamental criteria of a scalable quantum computer – within five years.

    The announcement has been made as part of the federal government’s $1.1 billion National Innovation and Science Agenda.

    “Australia needs to take advantage of and evolve with the rapid pace of this technological change,” the government’s statement said.

    “If Australian researchers are successful in developing a quantum computing capability, it would mean the development of a valuable new industry.

    “Quantum computers have the potential to solve problems in minutes that would take conventional computers centuries. The technology will transform Australian and global business, from banks undertaking financial analysis, transport companies planning optimal logistic routes, or improvements in medical drug design.”

    UNSW President and Vice-Chancellor Professor Ian Jacobs said: “UNSW researchers, led by Scientia Professor Michelle Simmons, are currently leading the global race to build the world’s first quantum computer.

    “I applaud the government’s vision in recognising the global significance of this research. It is a wonderful funding boost and follows a substantial investment by UNSW in this ground-breaking work,” Professor Jacobs said.

    Director of the ARC Centre of Excellence for Quantum Computation and Communication Technology Professor Simmons welcomed the announcement.

    “We are delighted by this news. Quantum computing is a transformational technology in which Australia has an international lead, and there is now an opportunity for translating this research here in Australia. It is based on a focused, ambitious and targeted program to build a 10 qubit prototype – demonstrating all the fundamental criteria of a scalable quantum computer – within five years,” said Professor Simmons.

    “This announcement sends a very powerful message about supporting internationally leading Australian research in areas of breakthrough technology. Our program has had backing from visionary Australian companies who are technology leaders in their space, such as the Commonwealth Bank, who see the long-term benefit to their organisation.”

    Without a boost to public and private investment, Australia would have been at risk of missing out on the long-term benefits of the research conducted at the ARC Centre of Excellence, Professor Simmons said.

    Quantum computing in silicon is an entirely new system at the atomic-scale and Australia leads the world in single atom engineering. In the long term, one of these next-generation quantum computers has the potential to exceed the combined power of all the computers now on Earth for certain high value applications. They will be ideal for searching huge databases much faster than conventional computers, and for performing tasks beyond the capability of even the most powerful supercomputers, such as modelling complex biological molecules for drug development.

    Broader innovation strategy welcomed

    Professor Jacobs said the federal government’s broader strategy to boost the country’s innovation capacity is an important development and recognises the essential role greater collaboration between industry and universities will play in securing future economic and social prosperity.

    “The national Innovation Statement is an important contribution to the ongoing effort by governments, universities, industry and research institutions to scale up innovation and work more collaboratively for a far greater impact.

    “Universities clearly play a key role in that agenda and by working effectively with industry, government and leaders across the entire innovation ecosystem, we have a profound impact,” he said.

    “UNSW welcomes the Prime Minister’s commitment to major research infrastructure, which supports the entire innovation agenda. We particularly welcome the long-term commitment over a 10-year period for the National Collaborative Research Infrastructure Strategy.”

    This announcement sends a very powerful message about supporting internationally leading Australian research in areas of breakthrough technology.

    UNSW is at the forefront of innovation. It leads nationally in industry-linked research funding under the ARC Linkage Grant scheme; has the largest student start-up program in Australia; and was one of the first adopters of the world-leading easy access IP scheme.

    “We see the creation of intellectual property and knowledge as a national treasure and asset, to be shared with society. We share our knowledge and innovation with industry, government and society through our teaching, consultancy, collaborative and contract research, licensing, company creation, networking and professional development,” Professor Jacobs said.

    The University’s new 10-year strategy, UNSW 2025, includes a bold commitment to significantly scale up our innovation efforts. Some of these commitments include:

    Expanding UNSW industry incubators on campus and 100 more student start-ups per year
    A new Innovation Park adjacent to UNSW Randwick campus by 2020
    Opening an internationally accredited Clinical Health Research Facility
    A new Academic Health Science Partnership (three universities, three local health districts and six medical research institutes)
    1000 new industry internships and a Fellowship Scheme
    Expanding our Easy Access IP model and an Easy Access Innovation Portal
    Vouchers for SMEs, entrepreneurs and start-ups to purchase university engagement.

    See the full article here .

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    U NSW Campus

    Welcome to UNSW Australia (The University of New South Wales), one of Australia’s leading research and teaching universities. At UNSW, we take pride in the broad range and high quality of our teaching programs. Our teaching gains strength and currency from our research activities, strong industry links and our international nature; UNSW has a strong regional and global engagement.

    In developing new ideas and promoting lasting knowledge we are creating an academic environment where outstanding students and scholars from around the world can be inspired to excel in their programs of study and research. Partnerships with both local and global communities allow UNSW to share knowledge, debate and research outcomes. UNSW’s public events include concert performances, open days and public forums on issues such as the environment, healthcare and global politics. We encourage you to explore the UNSW website so you can find out more about what we do.

  • richardmitnick 10:04 am on December 3, 2015 Permalink | Reply
    Tags: , Quantum Computing,   

    From TUM: “Quantum computer made of standard semiconductor materials” 

    Techniche Universitat Munchen

    Techniche Universitat Munchen

    Electron in a quantum dot influenced by the spins in the proximity – Image: Fabian Flassik / TUM

    Prof. Jonathan J. Finley
    Walter Schottky Institute
    Technical University of Munich
    85748 Garching; Germany
    Tel.: +49 89 289 11481

    Physicists at the Technical University of Munich, the Los Alamos National Laboratory and Stanford University (USA) have tracked down semiconductor nanostructure mechanisms that can result in the loss of stored information – and halted the amnesia using an external magnetic field. The new nanostructures comprise common semiconductor materials compatible with standard manufacturing processes.

    Quantum bits, qubits for short, are the basic logical elements of quantum information processing (QIP) that may represent the future of computer technology. Since they process problems in a quantum-mechanical manner, such quantum computers might one day solve complex problems much more quickly than currently possible, so the hope of researchers.

    In principle, there are various possibilities of implementing qubits: photons are an option equally as viable as confined ions or atoms whose states can be altered in a targeted manner using lasers. The key questions regarding their potential use as memory units are how long information can be stored in the system and which mechanisms might lead to a loss of information.

    A team of physicists headed by Alexander Bechtold and Professor Jonathan Finley at the Walter Schottky Institute of the Technical University of Munich and the Excellence Cluster Nanosystems Initiative Munich (NIM) have now presented a system comprising a single electron trapped in a semiconductor nanostructure. Here, the electron’s spin serves as the information carrier.

    The researchers were able to precisely demonstrate the existence of different data loss mechanisms and also showed that stored information can nonetheless be retained using an external magnetic field.

    Electrons trapped in a quantum dot

    The TUM physicists evaporated indium gallium arsenide onto a gallium arsenide substrate to form their nanostructure. As a result of the different lattice spacing of the two semiconductor materials strain is produced at the interface between the crystal grids. The system thus forms nanometer-scale “hills” – so-called quantum dots.

    When the quantum dots are cooled down to liquid helium temperatures and optically excited, a singe electron can be trapped in each of the quantum dots. The spin states of the electrons can then be used as information stores. Laser pulses can read and alter the states optically from outside. This makes the system ideal as a building block for future quantum computers.

    Spin up or spin down correspond to the standard logical information units 0 and 1. But, on top of this come additional intermediate states of quantum mechanical up and down superpositions.

    Hitherto unknown memory loss mechanisms

    However, there is one problem: “We found out that the strain in the semiconductor material leads to a new and until recently unknown mechanism that results in the loss of quantum information,” says Alexander Bechtold. The strain creates tiny electric fields in the semiconductor that influence the nuclear spin orientation of the atomic nuclei.

    “It’s a kind of piezoelectric effect,” says Bechthold. “It results in uncontrolled fluctuations in the nuclear spins.” These can, in turn, modify the spin of the electrons, i.e. the stored information. The information is lost within a few hundred nanoseconds.

    In addition, Alexander Bechthold’s team was able to provide concrete evidence for further information loss mechanisms, for example that electron spins are generally influenced by the spins of the surrounding 100,000 atomic nuclei.

    Preventing quantum mechanical amnesia

    “However, both loss channels can be switched off when a magnetic field of around 1.5 tesla is applied,” says Bechtold. “This corresponds to the magnetic field strength of a strong permanent magnet. It stabilizes the nuclear spins and the encoded information remains intact.”

    “Overall, the system is extremely promising,” according to Jonathan Finley, head of the research group. “The semiconductor quantum dots have the advantage that they harmonize perfectly with existing computer technology since they are made of similar semiconductor material.” They could even be equipped with electrical contacts, allowing them to be controlled not only optically using a laser, but also using voltage pulses.

    The research was funded by the European Union (S3 Nano and BaCaTeC), the US Department of Energy, the US Army Research Office (ARO), the German Research Foundation DFG (excellence cluster Nanosystems Munich (NIM) and SFB 631), the Alexander von Humboldt Foundation as well as the TUM Institute for Advanced Study (Focus Group Nanophotonics and Quantum Optics).


    Three-stage decoherence dynamics of an electron spin qubit in an optically active quantum dot; Alexander Bechtold, Dominik Rauch, Fuxiang Li, Tobias Simmet, Per-Lennart Ardelt, Armin Regler, Kai Müller, Nikolai A. Sinitsyn and Jonathan J. Finley; Nature Physics, 11, 1005-1008 (2015) – DOI: 10.1038/nphys3470

    See the full article here .

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    Techniche Universitat Munchin Campus

    Technische Universität München (TUM) is one of Europe’s top universities. It is committed to excellence in research and teaching, interdisciplinary education and the active promotion of promising young scientists. The university also forges strong links with companies and scientific institutions across the world. TUM was one of the first universities in Germany to be named a University of Excellence. Moreover, TUM regularly ranks among the best European universities in international rankings.

  • richardmitnick 8:32 pm on September 28, 2015 Permalink | Reply
    Tags: , , Quantum Computing,   

    From WIRED: “The Other Way A Quantum Computer Could Revive Moore’s Law” 

    Wired logo


    Cade Metz

    D-Wave’s quantum chip. Google

    Google is upgrading its quantum computer. Known as the D-Wave, Google’s machine is making the leap from 512 qubits—the fundamental building block of a quantum computer—to more than a 1000 qubits. And according to the company that built the system, this leap doesn’t require a significant increase in power, something that could augur well for the progress of quantum machines.

    Together with NASA and the Universities Space Research Association, or USRA, Google operates its quantum machine at the NASA Ames Research center not far from its Mountain View, California headquarters. Today, D-Wave Systems, the Canadian company that built the machine, said it has agreed to provide regular upgrades to the system—keeping it “state-of-the-art”—for the next seven years. Colin Williams, director of business development and strategic partnerships for D-Wave, calls this “the biggest deal in the company’s history.” The system is also used by defense giant Lockheed Martin, among others.

    Though the D-Wave machine is less powerful than many scientists hope quantum computers will one day be, the leap to 1000 qubits represents an exponential improvement in what the machine is capable of. What is it capable of? Google and its partners are still trying to figure that out. But Google has said it’s confident there are situations where the D-Wave can outperform today’s non-quantum machines, and scientists at the University of Southern California have published research suggesting that the D-Wave exhibits behavior beyond classical physics.

    Over the life of Google’s contract, if all goes according to plan, the performance of the system will continue to improve. But there’s another characteristic to consider. Williams says that as D-Wave expands the number of qubits, the amount of power needed to operate the system stays roughly the same. “We can increase performance with constant power consumption,” he says. At a time when today’s computer chip makers are struggling to get more performance out of the same power envelope, the D-Wave goes against the trend.

    The Qubit

    A quantum computer operates according to the principles of quantum mechanics, the physics of very small things, such as electrons and photons. In a classical computer, a transistor stores a single “bit” of information. If the transistor is “on,” it holds a 1, and if it’s “off,” it holds a 0. But in quantum computer, thanks to what’s called the superposition principle, information is held in a quantum system that can exist in two states at the same time. This “qubit” can store a 0 and 1 simultaneously.

    Two qubits, then, can hold four values at any given time (00, 01, 10, and 11). And as you keep increasing the number of qubits, you exponentially increase the power of the system. The problem is that building a qubit is a extreme difficult thing. If you read information from a quantum system, it “decoheres.” Basically, it turns into a classical bit that houses only a single value.

    D-Wave believes it has found a way around this problem. It released its first machine, spanning 16 qubits, in 2007. Together with NASA, Google started testing the machine when it reached 512 qubits a few years back. Each qubit, D-Wave says, is a superconducting circuit—a tiny loop of flowing current—and these circuits are dropped to extremely low temperatures so that the current flows in both directions at once. The machine then performs calculations using algorithms that, in essence, determine the probability that a collection of circuits will emerge in a particular pattern when the temperature is raised.

    Reversing the Trend

    Some have questioned whether the system truly exhibits quantum properties. But researchers at USC say that the system appears to display a phenomenon called “quantum annealing” that suggests it’s truly operating in the quantum realm. Regardless, the D-Wave is not a general quantum computer—that is, it’s not a computer for just any task. But D-Wave says the machine is well-suited to “optimization” problems, where you’re facing many, many different ways forward and must pick the best option, and to machine learning, where computers teach themselves tasks by analyzing large amount of data.

    D-Wave says that most of the power needed to run the system is related to the extreme cooling. The entire system consumes about 15 kilowatts of power, while the quantum chip itself uses a fraction of a microwatt. “Most of the power,” Williams says, “is being used to run the refrigerator.” This means that the company can continue to improve its performance without significantly expanding the power it has to use. At the moment, that’s not hugely important. But in a world where classical computers are approaching their limits, it at least provides some hope that the trend can be reversed.

    See the full article here .

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