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  • richardmitnick 8:52 am on March 1, 2018 Permalink | Reply
    Tags: , Quantum Computing,   

    From University of Chicago: “UChicago scientists to lead $10 million NSF ‘expedition’ for practical quantum computing” 

    U Chicago bloc

    University of Chicago

    February 27, 2018
    Rob Mitchum

    A multimode resonator used to store large numbers of qubits, the fundamental component of a quantum computer. Photo by Nate Earnest/David Schuster Laboratory

    To operate, quantum computers require temperatures near absolute zero, conditions created by a dilution refrigerator. Photo by Nate Earnest/David Schuster Laboratory.

    University of Chicago computer scientists will lead a $10 million “expedition” into the burgeoning field of quantum computing, bringing applications of the nascent technology for computer science, physics, chemistry and other fields at least a decade closer to practical use.

    Quantum computers harness the unique properties of quantum physics in machines that scientists hope will eventually perform complex calculations that are prohibitively slow or even impossible for today’s computers. In recent months, companies such as IBM, Intel and Google have unveiled new quantum computing prototypes approaching 50 quantum bits—or “qubits”—a new milestone in the race for machines capable of producing unprecedented discoveries.

    Yet despite these advances, there remains a wide gap between the quantum designs currently in use and the algorithms necessary to make full use of their power. The new, multi-institutional Enabling Practical-Scale Quantum Computing project, funded by the National Science Foundation’s Expeditions in Computing program, will bridge this gap through the co-design of hardware and software that helps scientists realize the potential of quantum computing more rapidly. Expeditions are the largest single-project investments made by the NSF and represent the most visionary and high-impact research in computer science.

    “We want to close the gap enough that we can do something promising with these machines,” said Fred Chong, the Seymour Goodman Professor in the Department of Computer Science at the University of Chicago and lead investigator on the project. “What we aim to do is to make quantum algorithms and machines meet, in a useful way, 10 or more years earlier than they would otherwise—five years from now instead of 15 years from now.”

    Uniting experts in algorithms, software, computer architecture and education from UChicago, MIT, Princeton, Georgia Tech and the University of California, Santa Barbara, EPiQC will develop these elements in tandem to take full advantage of new quantum machines. The collaboration will also establish a community of academic and industry partners and create new educational programs for students from elementary school to graduate school, training the next generation of quantum computer scientists.

    “Without a coordinated effort such as EPiQC, what’s going to happen is these computers will come out and no one will be able to program them, and they’ll need a much larger machine in order to do the computation that they want to do,” said Diana Franklin, director of computer science education at UChicago STEM Education and a research associate professor at UChicago. “It makes it so that practical quantum computers can be released so much earlier than they would be otherwise.”

    Missing pieces in quantum computing

    The promise of quantum computing lies in the ability of qubits to occupy a “superposition” of states, rather than the binary 1 or 0 of classical computing bits. Due to this difference, each additional qubit doubles the computing power of a machine, producing exponential gains that could eventually push quantum computers past the capabilities of today’s largest supercomputers. Scientists could then use these machines to run simulations and solve equations too complex for classical computers, leading to new discoveries in drug and material design, agriculture, cryptography and transportation optimization.

    However, many of the algorithms designed thus far to exploit these quantum advantages require the use of much more powerful machines than will be available in the near future. Scientists also lack the software needed to adapt these algorithms for practical use on actual machines, as well as the infrastructure tools necessary for programming these new technologies.

    “The big missing piece in quantum computing is what can we do with it that’s useful,” Chong said. “We want to think about it in very practical terms. What happens when you have a small number of devices, you can only run them for a short amount of time, and you have noise and errors—will the algorithms work then, and how can we change them to make them work better? And how can we change the machine to make the algorithms work better?”

    The project’s education and outreach efforts will focus on exposing students of all ages to quantum concepts and principles, preparing them for the new approaches needed to program and use quantum computers. The collaboration also will engage partners from industry and other universities to form a consortium that can share research ideas and new tools as they are developed.

    “EPiQC will play an essential role in researching efficient co-design of algorithms, software and devices, as well as creating tools to put quantum in front of a wider audience for even greater quantum programming creativity, and eventual breakthrough quantum applications,” said Jay Gambetta, manager of quantum information and computation at IBM Research. “EPiQC will also develop curricula to help train a much-needed workforce to drive quantum computing forward.”

    The EPiQC project will leverage substantial investments by the University of Chicago in computer science, including a major faculty hiring initiative and new facilities for computer and data science. The project also will coordinate with UChicago STEM Education and the Chicago Quantum Exchange, a partnership of UChicago, Argonne National Laboratory and Fermi National Laboratory for advancing academic and industrial efforts in the science and engineering of quantum information. Additional UChicago faculty on the project include John Reppy, professor in the Department of Computer Science; and David Schuster, assistant professor in the Department of Physics.

    “Part of what we want to do is not only produce tools and educate people and help the community grow, but also help people appreciate that there are some really important problems to be solved here, and inspire people to work on them,” Chong said. “It’s really one of our core missions to build a research community with enough critical mass to spur innovation and realize the potential of this incredibly promising computing technology.”

    See the full article here .

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    U Chicago Campus

    An intellectual destination

    One of the world’s premier academic and research institutions, the University of Chicago has driven new ways of thinking since our 1890 founding. Today, UChicago is an intellectual destination that draws inspired scholars to our Hyde Park and international campuses, keeping UChicago at the nexus of ideas that challenge and change the world.

  • richardmitnick 7:31 am on February 27, 2018 Permalink | Reply
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    From ETH Zürich: “Teaching quantum physics to a computer” 

    ETH Zurich bloc

    ETH Zürich

    Oliver Morsch

    An international collaboration led by ETH physicists has used machine learning to teach a computer how to predict the outcomes of quantum experiments. The results could prove to be essential for testing future quantum computers.

    Using neural networks, physicists taught a computer to predict the results of quantum experiments. (Graphic: http://www.colourbox.com)

    Physics students spend many years learning to master the often counterintuitive laws and effects of quantum mechanics. For instance, the quantum state of a physical system may be undetermined until a measurement is made, and a measurement on one part of the system can influence the state of a distant part without any exchange of information. It is enough to make the mind boggle. Once the students graduate and start doing research, the problems continue: to exactly determine the state of some quantum system in an experiment, one has to carefully prepare it and make lots of measurements, over and over again.

    Very often, what one is actually interested in cannot even be measured directly. An international team of researchers led by Giuseppe Carleo, a lecturer at the Institute for Theoretical Physics of ETH Zürich, has now developed machine learning software that enables a computer to “learn” the quantum state of a complex physical system based on experimental observations and to predict the outcomes of hypothetical measurements. In the future, their software could be used to test the accuracy of quantum computers.

    Quantum physics and handwriting

    The principle of his approach, Carleo explains, is rather simple. He uses an intuitive analogy that avoids the complications of quantum physics: “What we do, in a nutshell, is like teaching the computer to imitate my handwriting. We will show it a bunch of written samples, and step by step it then learns to replicate all my a’s, l’s and so forth.”

    The way the computer does this is by looking at the ways, for instance, in which an “l” is written when it follows an “a”. These may not always be the same, so the computer will calculate a probability distribution that expresses mathematically how often a letter is written in a certain way when it is preceded by some other letter. “Once the computer has figured out that distribution, it could then reproduce something that looks very much like my handwriting”, Carleo says.

    A neural network (top) “learns” the quantum state of a spin system from measurement data by trying different possibilities of the spin directions (bottom) and correcting itself step by step. (Graphic: ETH Zürich / G. Carleo)

    Quantum physics is, of course, much more complicated than a person’s handwriting. Still, the principle that Carleo (who recently moved to the Flatiron Institute in New York), together with Matthias Troyer, Guglielmo Mazzola (both at ETH) and Giacomo Torlai from the University of Waterloo as well as colleagues at the Perimeter Institute and the company D-Wave in Canada have used for their machine learning algorithm is quite similar.

    The quantum state of the physical system is encoded in a so-called neural network, and learning is achieved in small steps by translating the current state of the network into predicted measurement probabilities. Those probabilities are then compared to the actually measured data, and adjustments are made to the network in order to make them match better in the next round. Once this training period is finished, one can then use the quantum state stored in the neural network for “virtual” experiments without actually performing them in the laboratory.

    Faster tomography for quantum states

    “Using machine learning to extract a quantum state from measurements has a number of advantages”, Carleo explains. He cites one striking example, in which the quantum state of a collection of just eight quantum objects (trapped ions) had to be experimentally determined. Using a standard approached called quantum tomography, around one million measurements were needed to achieve the desired accuracy. With the new method, a much smaller number of measurements could do the same job, and substantially larger systems, previously inaccessible, could be studied.

    This is encouraging, since common wisdom has it that the number of calculations necessary to simulate a complex quantum system on a classical computer grows exponentially with the number of quantum objects in the system. This is mainly because of a phenomenon called entanglement, which causes distant parts of the quantum system to be intimately connected although they do not exchange information. The approach used by Carleo and his collaborators takes this into account by using a layer of “hidden” neurons, which allow the computer to encode the correct quantum state in a much more compact fashion.

    Testing quantum computers

    Being able to study quantum systems with a large number of components – or “qubits”, as they are often called – also has important implications for future quantum technologies, as Carleo points out: “If we want to test quantum computers with more than a handful of qubits, that won’t be possible with conventional means because of the exponential scaling. Our machine learning approach, however, should put us in a position to test quantum computers with as many as 100 qubits.”

    Also, the machine learning software can help experimental physicists by allowing them to perform virtual measurements that would be hard to do in the laboratory, such as measuring the degree of entanglement of a system composed of many interacting qubits. So far, the method has only been tested on artificially generated data, but the researchers plan to use it for analysing real quantum experiments very soon.

    Science paper:
    Torlai G, Mazzola G, Carrasquilla J, Troyer M, Melko R, Carleo G: Neural-network quantum state tomography. Nature Physics.

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    ETH Zurich campus
    ETH Zürich is one of the leading international universities for technology and the natural sciences. It is well known for its excellent education, ground-breaking fundamental research and for implementing its results directly into practice.

    Founded in 1855, ETH Zürich today has more than 18,500 students from over 110 countries, including 4,000 doctoral students. To researchers, it offers an inspiring working environment, to students, a comprehensive education.

    Twenty-one Nobel Laureates have studied, taught or conducted research at ETH Zürich, underlining the excellent reputation of the university.

  • richardmitnick 12:02 pm on February 15, 2018 Permalink | Reply
    Tags: , , , Quantum Computing, , , U Vienna   

    From U Vienna: “Fingerprints of quantum entanglement” 

    University of Vienna

    15. February 2018

    Dr. Borivoje Dakic
    Fakultät für Physik
    Universität Wien & IQOQI Wien, ÖAW
    1090 – Wien, Boltzmanngasse 5
    + 43-4277-725 80

    Mag. Alexandra Frey
    Pressebüro der Universität Wien
    Forschung und Lehre
    Universität Wien
    1010 – Wien, Universitätsring 1
    +43-1-4277-175 33
    +43-664-60277-175 33

    Dipl.-Soz. Sven Hartwig
    Leitung Öffentlichkeit & Kommunikation
    Österreichische Akademie der Wissenschaften
    1010 – Wien, Dr. Ignaz Seipel-Platz 2
    +43 1 51581-13 31

    Entangled qubits are sent to measurement devices which output a sequence of zeroes and ones. This pattern heavily depends on the type of measurements performed on individual qubits. If we pick the set of measurements in a peculiar way, entanglement will leave unique fingerprints in the measurement patterns (Copyright: Juan Palomino).

    Quantum entanglement is a key feature of a quantum computer. Yet, how can we verify that a quantum computer indeed incorporates a large-scale entanglement? Using conventional methods is hard since they require a large number of repeated measurements. Aleksandra Dimić from the University of Belgrade and Borivoje Dakić from the Austrian Academy of Sciences and the University of Vienna have developed a novel method where in many cases even a single experimental run suffices to prove the presence of entanglement. Their surprising results will be published in the online open access journal npj Quantum Information of the Nature Publishing group.

    The ultimate goal of quantum information science is to develop a quantum computer, a fully-fledged controllable device which makes use of the quantum states of subatomic particles to store information. As with all quantum technologies, quantum computing is based on a peculiar feature of quantum mechanics, quantum entanglement. The basic units of quantum information, the qubits, need to correlate in this particular way in order for the quantum computer to achieve its full potential.

    One of the main challenges is to make sure that a fully functional quantum computer is working as anticipated. In particular, scientists need to show that the large number of qubits are reliably entangled. Conventional methods require a large number of repeated measurements on the qubits for reliable verification. The more often a measurement run is repeated the more certain one can be about the presence of entanglement. Therefore, if one wants to benchmark entanglement in large quantum systems it will require a lot of resources and time, which is practically difficult or simply impossible. The main question arises: can we prove entanglement with only a low number of measurement trials?

    Now researchers from the University of Belgrade, the University of Vienna and the Austrian Academy of Sciences have developed a novel verification method which requires significantly fewer resources and, in many cases, even only a single measurement run to prove large-scale entanglement with a high confidence. For Aleksandra Dimić from the University of Belgrade, the best way to understand this phenomenon is to use the following analogy: “Let us consider a machine which simultaneously tosses, say, ten coins. We manufactured the machine such that it should produce correlated coins. We now want to validate whether the machine produces the anticipated result. Imagine a single trial revealing all coins landing on tails. This is a clear signature of correlations, as ten independent coins have 0.01% chance to land on the same side simultaneously. From such an event, we certify the presence of correlations with more than 99.9% confidence. This situation is very similar to quantum correlations captured by entanglement.” Borivoje Dakić says: “In contrast to classical coins, qubits can be measured in many, many different ways. The measurement result is still a sequence of zeros and ones, but its structure heavily depends on how we choose to measure individual qubits”, he continues. “We realized that, if we pick these measurements in a peculiar way, entanglement will leave unique fingerprints in the measured pattern”, he concludes.

    The developed method promises a dramatic reduction in time and resources needed for reliable benchmark of future quantum devices.

    Publication in npj Quantum Information:
    A.Dimić and B.Dakić, “Single-copy enntaglement detection”, npj Quantum Information, 2018.
    DOI: 10.1038/s41534-017-0055-x

    See the full article here .

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    The University of Vienna (German: Universität Wien) is a public university located in Vienna, Austria. It was founded by Duke Rudolph IV in 1365 and is one of the oldest universities in the German-speaking world. With its long and rich history, the University of Vienna has developed into one of the largest universities in Europe, and also one of the most renowned, especially in the Humanities. It is associated with 15 Nobel prize winners and has been the academic home to a large number of scholars of historical as well as of academic importance.

  • richardmitnick 12:56 pm on February 6, 2018 Permalink | Reply
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    From Symmetry: “Learning to speak quantum” 

    Symmetry Mag


    Laura Dattaro

    Artwork by Sandbox Studio, Chicago with Ana Kova

    Particle physicists are studying ways to harness the power of the quantum realm to further their research.

    In a 1981 lecture, the famed physicist Richard Feynman wondered if a computer could ever simulate the entire universe. The difficulty with this task is that, on the smallest scales, the universe operates under strange rules: Particles can be here and there at the same time; objects separated by immense distances can influence each other instantaneously; the simple act of observing can change the outcome of reality.

    “Nature isn’t classical, dammit,” Feynman told his audience, “and if you want to make a simulation of nature, you’d better make it quantum mechanical.”

    Quantum computers

    Feynman was imagining a quantum computer, a computer with bits that acted like the particles of the quantum world. Today, nearly 40 years later, such computers are starting to become a reality, and they pose a unique opportunity for particle physicists.


    “The systems that we deal with in particle physics are intrinsically quantum mechanical systems,” says Panagiotis Spentzouris, head of Fermilab’s Scientific Computing Division. “Classical computers cannot simulate large entangled quantum systems. You have plenty of problems that we would like to be able to solve accurately without making approximations that we hope we will be able to do on the quantum computer.”

    Quantum computers allow for a more realistic representation of quantum processes. They take advantage of a phenomenon known as superposition, in which a particle such as an electron exists in a probabilistic state spread across multiple locations at once.

    Unlike a classical computer bit, which can be either on or off, a quantum bit—or qubit—can be on, off, or a superposition of both on and off, allowing for computations to be performed simultaneously instead of sequentially.

    This not only speeds up computations; it makes currently impossible ones possible. A problem that could effectively trap a normal computer in an infinite loop, testing possibility after possibility, could be solved almost instantaneously by a quantum computer. This processing speed could be key for particle physicists, who wade through enormous amounts of data generated by detectors.

    In the first demonstration of this potential, a team at CalTech recently used a type of quantum computer called a quantum annealer to “rediscover” the Higgs boson, the particle that, according to the Standard Model of particle physics, gives mass to every other fundamental particle.

    Standard Model of Particle Physics from Symmetry Magazine

    Scientists originally discovered the Higgs boson in 2012 using particle detectors at the Large Hadron Collider at CERN research center in Europe.

    CERN CMS Higgs Event

    CERN ATLAS Higgs Event

    They created Higgs bosons by converting the energy of particle collisions temporarily into matter. Those temporary Higgs bosons quickly decayed, converting their energy into other, more common particles, which the detectors were able to measure.

    Scientists identified the mass of the Higgs boson by adding up the masses of those less massive particles, the decay products. But to do so, they needed to pick out which of those particles came from the decay of Higgs bosons, and which ones came from something else. To a detector, a Higgs boson decay can look remarkably similar to other, much more common decays.

    LHC scientists trained a machine learning algorithm to find the Higgs signal against the decay background—the needle in the haystack. This training process required a huge amount of simulated data.

    Physicist Maria Spiropulu, who was on the team that discovered the Higgs the first time around, wanted to see if she could improve the process with quantum computing. The group she leads at CalTech used a quantum computer from a company called D-Wave to train a similar machine learning algorithm. They found that the quantum computer trained the machine learning algorithm on a significantly smaller amount of data than the classical method required. In theory, this would give the algorithm a head start, like giving someone looking for the needle in the haystack expert training in spotting the glint of metal before turning their eyes to the hay.

    “The machine cannot learn easily,” Spiropulu says. “It needs huge, huge data. In the quantum annealer, we have a hint that it can learn with small data, and if you learn with small data you can use it as initial conditions later.”

    Some scientists say it may take a decade or more to get to the point of using quantum computers regularly in particle physics, but until then they will continue to make advances to enhance their research.

    Quantum sensors

    Quantum mechanics is also disrupting another technology used in particle physics: the sensor, the part of a particle detector that picks up the energy from a particle interaction.

    In the quantum world, energy is discrete. The noun quantum means “a specific amount” and is used in physics to mean “the smallest quantity of energy.” Classical sensors generally do not make precise enough measurements to pick up individual quanta of energy, but a new type of quantum sensor can.

    “A quantum sensor is one that is able to sense these individual packets of energy as they arrive,” says Aaron Chou, a scientist at Fermilab. “A non-quantum sensor would not be able to resolve the individual arrivals of each of these little packets of energy, but would instead measure a total flow of the stuff.”

    Chou is taking advantage of these quantum sensors to probe the nature of dark matter. Using technology originally developed for quantum computers, Chou and his team are building ultrasensitive detectors for a type of theorized dark matter particle known as an axion.

    “We’re taking one of the qubit designs that was previously created for quantum computing and we’re trying to use those to sense the presence of photons that came from the dark matter,” Chou says.

    For Spiropulu, these applications of quantum computers represent an elegant feedback system in the progression of technology and scientific application. Basic research in physics led to the initial transistors that fed the computer science revolution, which is now on the edge of transforming basic research in physics.

    “You want to disrupt computing, which was initially a physics advance,” Spiropulu says. “Now we are using physics configurations and physics systems themselves to assist computer science to solve any problem, including physics problems.”

    See the full article here .

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    Symmetry is a joint Fermilab/SLAC publication.

  • richardmitnick 3:10 pm on December 10, 2017 Permalink | Reply
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    From IST Austria: “Essential quantum computer component downsized by two orders of magnitude” 

    Institute of Science and Technology Austria

    November 14, 2017

    Researchers at IST Austria have built compact photon directional devices. Their micrometer-scale, nonmagnetic devices route microwave photons and can shield qubits from harmful noise.

    The new nonreciprocal device acts as a roundabout for photons.
    Here, arrows show the direction of photons propagation.
    Credit: IST Austria/Birgit Rieger

    Qubits, or quantum bits, are the key building blocks that lie at the heart of every quantum computer. In order to perform a computation, signals need to be directed to and from qubits. At the same time, these qubits are extremely sensitive to interference from their environment, and need to be shielded from unwanted signals, in particular from magnetic fields. It is thus a serious problem that the devices built to shield qubits from unwanted signals, known as nonreciprocal devices, are themselves producing magnetic fields. Moreover, they are several centimeters in size, which is problematic, given that a large number of such elements is required in each quantum processor. Now, scientists at the Institute of Science and Technology Austria (IST Austria), simultaneously with competing groups in Switzerland and the United States, have decreased the size of nonreciprocal devices by two orders of magnitude. Their device, whose function they compare to that of a traffic roundabout for photons, is only about a tenth of a millimeter in size, and—maybe even more importantly—it is not magnetic. Their study was published in the open access journal Nature Communications.

    When researchers want to receive a signal, for instance a microwave photon, from a qubit, but also prevent noise and other spurious signals from traveling back the same way towards the qubit, they use nonreciprocal devices, such as isolators or circulators. These devices control the signal traffic, similar to the way traffic is regulated in everyday life. But in the case of a quantum computer, it is not cars that cause the traffic but photons in transmission lines. “Imagine a roundabout in which you can only drive counterclockwise”, explains first author Dr. Shabir Barzanjeh, who is a postdoc in Professor Johannes Fink’s group at IST Austria. “At exit number one, at the bottom, there is our qubit. Its faint signal can go to exit number two at the top. But a signal coming in from exit number two cannot travel the same path back to the qubit. It is forced to travel in a counterclockwise manner, and before it reaches exit one, it encounters exit three. There, we block it and keep it from harming the qubit.”

    The ‘roundabouts’ the group has designed consist of aluminum circuits on a silicon chip and they are the first to be based on micromechanical oscillators: Two small silicon beams oscillate on the chip like the strings of a guitar and interact with the electrical circuit. These devices are tiny in size—only about a tenth of a millimeter in diameter—, one of the major advantages the new component has over its traditional predecessors, which were a few centimeters wide.

    Currently, only a few qubits have been used to test the principles of quantum computers, but in the future, thousands or even millions of qubits will be connected together, and many of these qubits will require their own circulator. “Imagine building a processor that has millions of such centimeter-size components. It would be enormous and impractical,” says Shabir Barzanjeh. “Using our nonmagnetic and very compact on-chip circulators instead makes life a lot easier.” Yet some hurdles need to be overcome before the devices will be used for this specific application. For example, the available signal bandwidth is currently still quite small, and the required drive powers might harm the qubits. However, the researchers are confident that these problems will turn out to be solvable.

    See the full article here.

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    The Institute of Science and Technology Austria (IST Austria) is a young international institute dedicated to basic research and graduate education in the natural and mathematical sciences, located in Klosterneuburg on the outskirts of Vienna. Established jointly by the federal government of Austria and the provincial government of Lower Austria, the Institute was inaugurated in 2009 and will grow to about 90 research groups by 2026.

    The governance and management structures of IST Austria guarantee its independence and freedom from political and commercial influences. The Institute is headed by the President, who is appointed by the Board of Trustees and advised by the Scientific Board. The first President of IST Austria is Thomas A. Henzinger, a leading computer scientist and former professor of the University of California at Berkeley and the EPFL Lausanne in Switzerland.

  • richardmitnick 10:31 am on December 10, 2017 Permalink | Reply
    Tags: , , Quantum Computing,   

    From JQI: “Quantum simulators wield control over more than 50 qubits” 

    JQI bloc

    Joint Quantum Institute

    November 29, 2017 [Just appeared in social media.]
    E. Edwards

    Research Contact
    Christopher Monroe

    Atoms provide a robust platform for observing quantum magnets in action.

    Artist’s depiction of quantum simulation. Lasers manipulate an array of over 50 atomic qubits in order to study the dynamics of quantum magnetism (credit: E. Edwards/JQI).

    Two independent teams of scientists, including one from the Joint Quantum Institute, have used more than 50 interacting atomic qubits to mimic magnetic quantum matter, blowing past the complexity of previous demonstrations. The results appear in this week’s issue of Nature.

    As the basis for its quantum simulation, the JQI team deploys up to 53 individual ytterbium ions—charged atoms trapped in place by gold-coated and razor-sharp electrodes. A complementary design by Harvard and MIT researchers uses 51 uncharged rubidium atoms confined by an array of laser beams. With so many qubits these quantum simulators are on the cusp of exploring physics that is unreachable by even the fastest modern supercomputers. And adding even more qubits is just a matter of lassoing more atoms into the mix.

    “Each ion qubit is a stable atomic clock that can be perfectly replicated,” says JQI Fellow Christopher Monroe*, who is also the co-founder and chief scientist at the startup IonQ Inc. “They are effectively wired together with external laser beams. This means that the same device can be reprogrammed and reconfigured, from the outside, to adapt to any type of quantum simulation or future quantum computer application that comes up.” Monroe has been one of the early pioneers in quantum computing and his research group’s quantum simulator is part of a blueprint for a general-purpose quantum computer.

    Quantum hardware for a quantum problem

    While modern, transistor-driven computers are great for crunching their way through many problems, they can screech to a halt when dealing with more than 20 interacting quantum objects. That’s certainly the case for quantum magnetism, in which the interactions can lead to magnetic alignment or to a jumble of competing interests at the quantum scale.

    “What makes this problem hard is that each magnet interacts with all the other magnets,” says research scientist Zhexuan Gong, lead theorist and co-author on the study. “With the 53 interacting quantum magnets in this experiment, there are over a quadrillion possible magnet configurations, and this number doubles with each additional magnet. Simulating this large-scale problem on a conventional computer is extremely challenging, if at all possible.”

    When these calculations hit a wall, a quantum simulator may help scientists push the envelope on difficult problems. This is a restricted type of quantum computer that uses qubits to mimic complex quantum matter. Qubits are isolated and well-controlled quantum systems that can be in a combination of two or more states at once. Qubits come in different forms, and atoms—the versatile building blocks of everything—are one of the leading choices for making qubits. In recent years, scientists have controlled 10 to 20 atomic qubits in small-scale quantum simulations.

    Currently, tech industry behemoths, startups and university researchers are in a fierce race to build prototype quantum computers that can control even more qubits. But qubits are delicate and must stay isolated from the environment to protect the device’s quantum nature. With each added qubit this protection becomes more difficult, especially if qubits are not identical from the start, as is the case with fabricated circuits. This is one reason that atoms are an attractive choice that can dramatically simplify the process of scaling up to large-scale quantum machinery.

    An atomic advantage

    Unlike the integrated circuitry of modern computers, atomic qubits reside inside of a room-temperature vacuum chamber that maintains a pressure similar to outer space. This isolation is necessary to keep the destructive environment at bay, and it allows the scientists to precisely control the atomic qubits with a highly engineered network of lasers, lenses, mirrors, optical fibers and electrical circuitry.

    “The principles of quantum computing differ radically from those of conventional computing, so there’s no reason to expect that these two technologies will look anything alike,” says Monroe.

    In the 53-qubit simulator, the ion qubits are made from atoms that all have the same electrical charge and therefore repel one another. But as they push each other away, an electric field generated by a trap forces them back together. The two effects balance each other, and the ions line up single file. Physicists leverage the inherent repulsion to create deliberate ion-to-ion interactions, which are necessary for simulating of interacting quantum matter.

    The quantum simulation begins with a laser pulse that commands all the qubits into the same state. Then, a second set of laser beams interacts with the ion qubits, forcing them to act like tiny magnets, each having a north and south pole. The team does this second step suddenly, which jars the qubits into action. They feel torn between two choices, or phases, of quantum matter. As magnets, they can either align their poles with their neighbors to form a ferromagnet or point in random directions yielding no magnetization. The physicists can change the relative strengths of the laser beams and observe which phase wins out under different laser conditions.

    The entire simulation takes only a few milliseconds. By repeating the process many times and measuring the resulting states at different points during the simulation, the team can see the process as it unfolds from start to finish. The researchers observe how the qubit magnets organize as different phases form, dynamics that the authors say are nearly impossible to calculate using conventional means when there are so many interactions.

    This quantum simulator is suitable for probing magnetic matter and related problems. But other kinds of calculations may need a more general quantum computer with arbitrarily programmable interactions in order to get a boost.

    “Quantum simulations are widely believed to be one of the first useful applications of quantum computers,” says Alexey Gorshkov**, JQI Fellow and co-author of the study. “After perfecting these quantum simulators, we can then implement quantum circuits and eventually quantum-connect many such ion chains together to build a full-scale quantum computer with a much wider domain of applications.”

    As they look to add even more qubits, the team believes that its simulator will embark on more computationally challenging terrain, beyond magnetism. “We are continuing to refine our system, and we think that soon, we will be able to control 100 ion qubits, or more,” says Jiehang Zhang, the study’s lead author and postdoctoral researcher. “At that point, we can potentially explore difficult problems in quantum chemistry or materials design.”

    Written by E. Edwards

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    We are on the verge of a new technological revolution as the strange and unique properties of quantum physics become relevant and exploitable in the context of information science and technology.

    The Joint Quantum Institute (JQI) is pursuing that goal through the work of leading quantum scientists from the Department of Physics of the University of Maryland (UMD), the National Institute of Standards and Technology (NIST) and the Laboratory for Physical Sciences (LPS). Each institution brings to JQI major experimental and theoretical research programs that are dedicated to the goals of controlling and exploiting quantum systems.

  • richardmitnick 4:10 pm on December 7, 2017 Permalink | Reply
    Tags: , , Quantum Computing, , Quantum Simulation Could Shed Light on the Origins of Life   

    From MIT Tech Review: “Quantum Simulation Could Shed Light on the Origins of Life” 

    MIT Technology Review
    M.I.T Technology Review

    December 7, 2017
    No writer credit

    For decades computer scientists have created artificial life to test ideas about evolution. Doing so on a quantum computer could help capture the role quantum mechanics may have played.

    What role does quantum mechanics play in the machinery of life? Nobody is quite sure, but in recent years, physicists have begun to investigate all kinds of possibilities. In the process, they have gathered evidence suggesting that quantum mechanics plays an important role in photosynthesis, in bird navigation, and perhaps in our sense of smell.

    There is even a speculative line of thought that quantum processes must have governed the origin of life itself and the formulation of the genetic code. The work to study these questions is ongoing and involves careful observation of the molecules of life.

    But there is another way to approach this question from the bottom up. Computer scientists have long toyed with artificial life forms built from computer code. This code lives in a silicon-based landscape where its fitness is measured against some selection criteria.

    The process of quantum evolution and the creation of artificial quantum life. No image credit.

    It reproduces by combining with other code or by the mutation of its own code. And the fittest code has more offspring while the least fit dies away. In other words, the code evolves. Computer scientists have used this approach to study various aspects of life, evolution, and the emergence of complexity.

    This is an entirely classical process following ordinary Newtonian steps, one after the other. The real world, on the other hand, includes quantum mechanics and the strange phenomena that it allows. That’s how the question arises of whether quantum mechanics can play a role in evolution and even in the origin of life itself.

    So an important first step is to reproduce this process of evolution in the quantum world, creating artificial quantum life forms. But is this possible?

    Today we get an answer thanks to the work of Unai Alvarez-Rodriguez and a few pals at the University of the Basque Country in Spain. These guys have created a quantum version of artificial life for the first time. And they say their results are the first examples of quantum evolution that allows physicists to explore the way complexity emerges in the quantum world.

    The experiment is simple in principle. The team think of quantum life as consisting of two parts—a genotype and a phenotype. Just as with carbon-based life, the quantum genotype contains the quantum information that describes the individual—its genetic code. The genotype is the part of the quantum life unit that is transmitted from one generation to the next.

    The phenotype, on the other hand, is the manifestation of the genotype that interacts with the real world—the “body” of the individual. “This state, together with the information it encodes, is degraded during the lifetime of the individual,” say Alvarez-Rodriguez and co.

    So each unit of quantum life consists of two qubits—one representing the genotype and the other the phenotype. “The goal is to reproduce the characteristic processes of Darwinian evolution, adapted to the language of quantum algorithms and quantum computing,” say the team.

    The first step in the evolutionary process is reproduction. Alvarez-Rodriguez and co do this using the process of entanglement, which allows the transmission of quantum states from one object to another. In this case, they entangle the genotype qubit with a blank state, and then transfer its quantum information.

    The next stage is survival, which depends on the phenotype. Alvarez-Rodriguez and co do this by transfering an aspect of the genotype state to another blank state, which becomes the phenotype. The phenotype then interacts with the environment and eventually dissipates.

    This process is equivalent to aging and dying, and the time it takes depends on the genotype. Those that live longer are implicitly better suited to their environment and are preferentially reproduced in the next generation.

    There is another important aspect of evolution—how individuals differ from each other. In ordinary evolution, variation occurs in two ways. The first is through sexual recombination, where the genotype from two individuals combines. The second is by mutation, where random changes occur in the genotype during the reproductive process.

    Alvarez-Rodriguez and co employ this second type of variation in their quantum world. When the quantum information is transferred from one generation to the next, the team introduce a random change—in this case a rotation of the quantum state. And this, in turn, determines the phenotype and how it interacts with its environment.

    So that’s the theory. The experiment itself is tricky because quantum computers are still in their infancy. Nevertheless, Alvarez-Rodriguez and co have made use of the IBM QX, a superconducting quantum computer at IBM’s T.J. Watson Laboratories that the company has made publicly accessible via the cloud. The company claims that some 40,000 individuals have signed up to use the service and have together run some 275,000 quantum algorithms through the device.

    Alvarez-Rodriguez and co used the five-qubit version of the machine, which runs quantum algorithms that allow two-qubit interactions. However, the system imposes some limitations on the process of evolution that the team want to run. For example, it does not allow the variations introduced during the reproductive process to be random.

    Instead, the team run the experiment several times, introducing a different known rotation in each run, and then look at the results together. In total, they run the experiment thousands of times to get a good sense of the outcomes.

    In general, the results match the theoretical predictions with high fidelity. “The experiments reproduce the characteristic properties of the sought quantum natural selection scenario,” say Alvarez-Rodriguez and co.

    And the team say that the mutations have an important impact on the outcomes: “[They] significantly improved the fidelity of the quantum algorithm outcome.” That’s not so different from the classical world, where mutations help species adapt to changing environments.

    Of course, there are important caveats. The limitations of IBM’s quantum computer raise important questions about whether the team has really simulated evolution. But these issues should be ironed out in the near future.

    All this work is the result of the team’s long focus on quantum life. Back in 2015, we reported on the team’s work in simulating quantum life on a classical computer. Now they have taken the first step in testing these ideas on a real quantum computer.

    And the future looks bright. Quantum computer technology is advancing rapidly, which this should allow Alvarez-Rodriguez and co to create quantum life in more complex environments. IBM, for example, has a 20-qubit processor online and is testing a 50-qubit version.

    That will make possible a variety of new experiments on quantum life. The most obvious will include the ability for quantum life forms to interact with each other and perhaps reproduce by sexual recombination—in other words, by combining elements of their genotypes. Another possibility will be to allow the quantum life forms to move and see how this influences their interactions and fitness for survival.

    Just what will emerge isn’t clear. But Alvarez-Rodriguez and co hope their quantum life forms will become important models for exploring the emergence of complexity in the quantum world.

    Eventually, that should feed into our understanding of the role of quantum processes in carbon-based life forms and the origin of life itself. The ensuing debate will be fascinating to watch.

    Ref: Quantum Artificial Life in an IBM Quantum Computer

    See the full article here .

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    The mission of MIT Technology Review is to equip its audiences with the intelligence to understand a world shaped by technology.

    • stewarthoughblog 10:48 pm on December 7, 2017 Permalink | Reply

      It is always nice for computer simulations to be developed for virtually all scientific phenomenon. However, the true realism is the critical measure of their relevance and veracity. Since there are no known methods for origin of life development sequences, only possible scenarios for the complexity progression essential for a first organism are possible following some logical progression of events.

      Consequently, the computer code can simulate some logical process and provide a learning method for what logically must have happened, but its relevance to reality is unknown without experimental verification.


  • richardmitnick 1:11 pm on December 5, 2017 Permalink | Reply
    Tags: , , New system could shed light on a host of complex processes, , Quantum Computing, Quantum simulator, Researchers create quantum calculator, With more than 50 coherent qubits this is one of the largest quantum systems ever created with individual assembly and measurement   

    From Harvard Gazette: “Researchers create quantum calculator” 

    Harvard University
    Harvard Gazette

    November 30, 2017
    Peter Reuell

    New system could shed light on a host of complex processes.

    Physics Professors Markus Greiner (left) and Mikhail Lukin led a Harvard-MIT team that developed a 51-qubit quantum simulator, one of the largest such systems yet built. Jon Chase/Harvard Staff Photographer

    Programming a computer is generally a fairly arduous process, involving hours of coding, not to mention the laborious work of debugging, testing, and documenting to make sure it works properly.

    But for a team of physicists from the Harvard-MIT Center for Ultracold Atoms and the California Institute of Technology, things are actually much tougher.

    Working in a Harvard Physics Department lab, a team of researchers led by Harvard Professors Mikhail Lukin and Markus Greiner and Massachusetts Institute of Technology Professor Vladan Vuletic developed a special type of quantum computer, known as a quantum simulator, that is programmed by capturing super-cooled rubidium atoms with lasers and arranging them in a specific order, then allowing quantum mechanics to do the necessary calculations.

    The system could be used to shed light on a host of complex quantum processes, including the connection between quantum mechanics and material properties, and it could investigate new phases of matter and solve complex real-world optimization problems. The system is described in a Nov. 30 paper published in the journal Nature.

    The combination of the system’s large size and high degree of quantum coherence make it an important achievement, researchers say. With more than 50 coherent qubits, this is one of the largest quantum systems ever created with individual assembly and measurement.

    In the same issue of Nature, a team from the Joint Quantum Institute at the University of Maryland described a similarly sized system of cold charged ions, also controlled with lasers. Taken together, these complimentary advances constitute a major step toward large-scale quantum machines.

    “Everything happens in a small vacuum chamber where we have a very dilute vapor of atoms which are cooled close to absolute zero,” Lukin said. “When we focus about 100 laser beams through this cloud, each of them acts like a trap. The beams are so tightly focused, they can either grab one atom or zero; they can’t grab two. And that’s when the fun starts.”

    A close up of a laser used in the quantum simulator to trap atoms for manipulation. Jon Chase/Harvard Staff Photographer

    Using a microscope, researchers can take images of the captured atoms in real time, and then arrange them in arbitrary patterns for input.

    “We assemble them in a way that’s very controlled,” said Ahmed Omran, a postdoctoral fellow in Lukin’s lab and a co-author of the paper. “Starting with a random pattern, we decide which trap needs to go where to arrange them into desired clusters.”

    As researchers begin feeding energy into the system, the atoms begin to interact with each other. Those interactions, Lukin said, give the system its quantum nature.

    “We make the atoms interact, and that’s really what’s performing the computation,” Omran said. “In essence, as we excite the system with laser light, it self-organizes. It’s not that we say this atom has to be a one or a zero — we could do that easily just by throwing light on the atoms — but what we do is allow the atoms to perform the computation for us, and then we measure the results.”

    Those results, Lukin and colleagues said, could shed light on complex quantum mechanical phenomena that are all but impossible to model using conventional computers.

    “If you have an abstract model where a certain number of particles are interacting with each other in a certain way, the question is why don’t we just sit down at a computer and simulate it that way?” asked Ph.D. student Alexander Keesling, another co-author. “The reason is because these interactions are quantum mechanical in nature. If you try to simulate these systems on a computer, you’re restricted to very small system sizes, and the number of parameters are limited.

    “If you make systems larger and larger, very quickly you will run out of memory and computing power to simulate it on a classical computer,” he added. “The way around that is to actually build the problem with particles that follow the same rules as the system you’re simulating. That’s why we call this a quantum simulator.”

    Though it’s possible to use classical computers to model small quantum systems, the simulator developed by Lukin and colleagues uses 51 qubits, making it virtually impossible to replicate using conventional computing techniques.

    “It is important that we can start by simulating small systems using our machine,” he said. “So we are able to show those results are correct … until we get to the larger systems, because there is no simple comparison we can make.”

    “When we start off, all the atoms are in a classical state. And when we read out at the end, we obtain a string of classical bits, zeros, and ones,” said Hannes Bernien, another postdoctoral fellow in Lukin’s lab, and also a co-author. “But in order to get from the start to the end, they have to go through the complex quantum mechanical state. If you have a substantial error rate, the quantum mechanical state will collapse.”

    It’s that coherent quantum state, Bernien said, that allows the system to work as a simulator, and also makes the machine a potentially valuable tool for gaining insight into complex quantum phenomena and eventually performing useful calculations. The system already allows researchers to obtain unique insights into transformations between different types of quantum phases, called quantum phase transitions. It may also help shed light on new and exotic forms of matter, Lukin said.

    “Normally, when you talk about phases of matter, you talk about matter being in equilibrium,” he said. “But some very interesting new states of matter may occur far away from equilibrium … and there are many possibilities for that in the quantum domain. This is a completely new frontier.”

    Already, Lukin said, the researchers have seen evidence of such states. In one of the first experiments conducted with the new system, the team discovered a coherent non-equilibrium state that remained stable for a surprisingly long time.

    “Quantum computers will be used to realize and study such non-equilibrium states of matter in the coming years,” he said. “Another intriguing direction involves solving complex optimization problems. It turns out one can encode some very complicated problems by programming atom locations and interactions between them. In such systems, some proposed quantum algorithms could potentially outperform classical machines. It’s not yet clear whether they will or not, because we just can’t test them classically. But we are on the verge of entering the regime where we can test them on the fully quantum machines containing over 100 controlled qubits. Scientifically, this is really exciting.”

    Other co-authors of the study were visiting scientist Sylvain Schwartz, Harvard graduate students Harry Levine and Soonwon Choi, research associate Alexander S. Zibrov, and Professor Manuel Endres.

    This research was supported with funding from the National Science Foundation, the Center for Ultracold Atoms, the Army Research Office, and the Vannevar Bush Faculty Fellowship.

    See the full article here .

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    Harvard University campus
    Harvard is the oldest institution of higher education in the United States, established in 1636 by vote of the Great and General Court of the Massachusetts Bay Colony. It was named after the College’s first benefactor, the young minister John Harvard of Charlestown, who upon his death in 1638 left his library and half his estate to the institution. A statue of John Harvard stands today in front of University Hall in Harvard Yard, and is perhaps the University’s best known landmark.

    Harvard University has 12 degree-granting Schools in addition to the Radcliffe Institute for Advanced Study. The University has grown from nine students with a single master to an enrollment of more than 20,000 degree candidates including undergraduate, graduate, and professional students. There are more than 360,000 living alumni in the U.S. and over 190 other countries.

  • richardmitnick 1:05 pm on November 29, 2017 Permalink | Reply
    Tags: Quantum Computing, ,   

    From U Sidney via Science Alert: “Physicists Just Invented an Essential Component Needed For Quantum Computers” 

    U Sidney bloc

    University of Sidney

    Science Alert

    29 NOV 2017

    (The University of Sydney)

    They’re using a new state of matter for this.

    In 2016, the Nobel Prize in Physics went to three British scientists for their work on superconductors and superfluids, which included the explanation of a rather odd phase of matter.

    Now, for the first time, their discovery has a practical application – shrinking an electrical component to a size that will help quantum computers reach a scale that just might make them useful.

    In a collaboration with Stanford University in the US, a team of scientists from the University of Sydney and Microsoft have used the newly found phase of matter – topological insulator – in shrinking an electrical component called a circulator 1,000 times smaller.

    That’s super good news when it comes to squeezing more qubits into a small enough space.

    If you missed the fuss last year, a trio of physicists received the Nobel Prize for discovering that under certain conditions some materials could easily conduct electrons along their surface, but remain an insulator within.

    Most importantly, they discovered cases where matter transitioned between states without breaking something called symmetry, as happens when water atoms rearrange into ice or vapour.

    As we shrink electrical components down to virtually atomic scales, the way electrons move in different dimensions becomes increasingly important.

    Enter the qubit – a chunky piece of electronics that uses the probabilities of an unmeasured bit of matter to perform calculations classical computers can’t hope to match.

    We can make qubits in a variety of ways, and are getting pretty good at stringing them together in ever larger numbers.

    But shrinking qubits to sizes small enough that we can shove hundreds of thousands into a small-enough space is a challenge.

    “Even if we had millions of qubits today, it is not clear that we have the classical technology to control them,” says David Reilly, a physicist at the University of Sydney and Director of Microsoft Station Q.

    “Realising a scaled-up quantum computer will require the invention of new devices and techniques at the quantum-classical interface.”

    One such device is called a circulator, which is kind-of like a roundabout for electrical signals, ensuring information heads in one direction only.

    Until now, the smallest versions of this hardware could be held in the palm of your hand.

    This is now set to change as scientists have shown a magnetised wafer made of a particular topological insulator could do the job, and be made 1,000 times smaller than existing components.

    “Such compact circulators could be implemented in a variety of quantum hardware platforms, irrespective of the particular quantum system used,” says the study’s lead author, Alice Mahoney.

    In many respects, we’re still at the pre-vacuum-tube and magnetic tape phase of quantum computers – they’re more promise than practical.

    But if we keep seeing advances like this, it won’t be long before we’ll be bringing you news of quantum computers cracking problems which leave our best supercomputers gasping.

    This research was published in Nature Communications.

    See the full article here .

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    U Sidney campus

    Our founding principle as Australia’s first university was that we would be a modern and progressive institution. It’s an ideal we still hold dear today.

    When Charles William Wentworth proposed the idea of Australia’s first university in 1850, he imagined “the opportunity for the child of every class to become great and useful in the destinies of this country”.

    We’ve stayed true to that original value and purpose by promoting inclusion and diversity for the past 160 years.

    It’s the reason that, as early as 1881, we admitted women on an equal footing to male students. Oxford University didn’t follow suit until 30 years later, and Jesus College at Cambridge University did not begin admitting female students until 1974.

    It’s also why, from the very start, talented students of all backgrounds were given the chance to access further education through bursaries and scholarships.

    Today we offer hundreds of scholarships to support and encourage talented students, and a range of grants and bursaries to those who need a financial helping hand.

  • richardmitnick 4:58 pm on November 14, 2017 Permalink | Reply
    Tags: , , , , , Quantum Circuits Company, Quantum Computing, , , Robert Schoelkopf is at the forefront of a worldwide effort to build the world’s first quantum computer,   

    From NYT: “Yale Professors Race Google and IBM to the First Quantum Computer” 

    New York Times

    The New York Times

    NOV. 13, 2017

    Prof. Robert Schoelkopf inside a lab at Yale University. Quantum Circuits, the start-up he has created with two of his fellow professors, is located just down the road. Credit Roger Kisby for The New York Times

    Robert Schoelkopf is at the forefront of a worldwide effort to build the world’s first quantum computer. Such a machine, if it can be built, would use the seemingly magical principles of quantum mechanics to solve problems today’s computers never could.

    Three giants of the tech world — Google, IBM, and Intel — are using a method pioneered by Mr. Schoelkopf, a Yale University professor, and a handful of other physicists as they race to build a machine that could significantly accelerate everything from drug discovery to artificial intelligence. So does a Silicon Valley start-up called Rigetti Computing. And though it has remained under the radar until now, those four quantum projects have another notable competitor: Robert Schoelkopf.

    After their research helped fuel the work of so many others, Mr. Schoelkopf and two other Yale professors have started their own quantum computing company, Quantum Circuits.

    Based just down the road from Yale in New Haven, Conn., and backed by $18 million in funding from the venture capital firm Sequoia Capital and others, the start-up is another sign that quantum computing — for decades a distant dream of the world’s computer scientists — is edging closer to reality.

    “In the last few years, it has become apparent to us and others around the world that we know enough about this that we can build a working system,” Mr. Schoelkopf said. “This is a technology that we can begin to commercialize.”

    Quantum computing systems are difficult to understand because they do not behave like the everyday world we live in. But this counterintuitive behavior is what allows them to perform calculations at rate that would not be possible on a typical computer.

    Today’s computers store information as “bits,” with each transistor holding either a 1 or a 0. But thanks to something called the superposition principle — behavior exhibited by subatomic particles like electrons and photons, the fundamental particles of light — a quantum bit, or “qubit,” can store a 1 and a 0 at the same time. This means two qubits can hold four values at once. As you expand the number of qubits, the machine becomes exponentially more powerful.

    Todd Holmdahl, who oversees the quantum project at Microsoft, said he envisioned a quantum computer as something that could instantly find its way through a maze. “A typical computer will try one path and get blocked and then try another and another and another,” he said. “A quantum computer can try all paths at the same time.”

    The trouble is that storing information in a quantum system for more than a short amount of time is very difficult, and this short “coherence time” leads to errors in calculations. But over the past two decades, Mr. Schoelkopf and other physicists have worked to solve this problem using what are called superconducting circuits. They have built qubits from materials that exhibit quantum properties when cooled to extremely low temperatures.

    With this technique, they have shown that, every three years or so, they can improve coherence times by a factor of 10. This is known as Schoelkopf’s Law, a playful ode to Moore’s Law, the rule that says the number of transistors on computer chips will double every two years.

    Professor Schoelkopf, left, and Prof. Michel Devoret working on a device that can reach extremely low temperatures to allow a quantum computing device to function. Credit Roger Kisby for The New York Times

    “Schoelkopf’s Law started as a joke, but now we use it in many of our research papers,” said Isaac Chuang, a professor at the Massachusetts Institute of Technology. “No one expected this would be possible, but the improvement has been exponential.”

    These superconducting circuits have become the primary area of quantum computing research across the industry. One of Mr. Schoelkopf’s former students now leads the quantum computing program at IBM. The founder of Rigetti Computing studied with Michel Devoret, one of the other Yale professors behind Quantum Circuits.

    In recent months, after grabbing a team of top researchers from the University of California, Santa Barbara, Google indicated it is on the verge of using this method to build a machine that can achieve “quantum supremacy” — when a quantum machine performs a task that would be impossible on your laptop or any other machine that obeys the laws of classical physics.

    There are other areas of research that show promise. Microsoft, for example, is betting on particles known as anyons. But superconducting circuits appear likely to be the first systems that will bear real fruit.

    The belief is that quantum machines will eventually analyze the interactions between physical molecules with a precision that is not possible today, something that could radically accelerate the development of new medications. Google and others also believe that these systems can significantly accelerate machine learning, the field of teaching computers to learn tasks on their own by analyzing data or experiments with certain behavior.

    A quantum computer could also be able to break the encryption algorithms that guard the world’s most sensitive corporate and government data. With so much at stake, it is no surprise that so many companies are betting on this technology, including start-ups like Quantum Circuits.

    The deck is stacked against the smaller players, because the big-name companies have so much more money to throw at the problem. But start-ups have their own advantages, even in such a complex and expensive area of research.

    “Small teams of exceptional people can do exceptional things,” said Bill Coughran, who helped oversee the creation of Google’s vast internet infrastructure and is now investing in Mr. Schoelkopf’s company as a partner at Sequoia. “I have yet to see large teams inside big companies doing anything tremendously innovative.”

    Though Quantum Circuits is using the same quantum method as its bigger competitors, Mr. Schoelkopf argued that his company has an edge because it is tackling the problem differently. Rather than building one large quantum machine, it is constructing a series of tiny machines that can be networked together. He said this will make it easier to correct errors in quantum calculations — one of the main difficulties in building one of these complex machines.

    But each of the big companies insist that they hold an advantage — and each is loudly trumpeting its progress, even if a working machine is still years away.

    Mr. Coughran said that he and Sequoia envision Quantum Circuits evolving into a company that can deliver quantum computing to any business or researcher that needs it. Another investor, Canaan’s Brendan Dickinson, said that if a company like this develops a viable quantum machine, it will become a prime acquisition target.

    “The promise of a large quantum computer is incredibly powerful,” Mr. Dickinson said. “It will solve problems we can’t even imagine right now.”

    See the full article here .

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