From Stanford: “An experiment proposed by Stanford theorists finds evidence for the Majorana fermion, a particle that’s its own antiparticle”

Stanford University

July 20, 2017
Glennda Chui

Media Contact
Amy Adams, Stanford News Service:
(650) 796-3695
amyadams@stanford.edu

In 1928, physicist Paul Dirac made the stunning prediction that every fundamental particle in the universe has an antiparticle – its identical twin but with opposite charge. When particle and antiparticle met they would be annihilated, releasing a poof of energy. Sure enough, a few years later the first antimatter particle – the electron’s opposite, the positron – was discovered, and antimatter quickly became part of popular culture.

But in 1937, another brilliant physicist, Ettore Majorana, introduced a new twist: He predicted that in the class of particles known as fermions, which includes the proton, neutron, electron, neutrino and quark, there should be particles that are their own antiparticles.

The Standard Model of elementary particles (more schematic depiction), with the three generations of matter, gauge bosons in the fourth column, and the Higgs boson in the fifth.

Now a team including Stanford scientists says it has found the first firm evidence of such a Majorana fermion.

Credit: Image courtesy of Stanford University

It was discovered in a series of lab experiments on exotic materials at the University of California in collaboration with Stanford University. The team was led by UC-Irvine Associate Professor Jing Xia and UCLA Professor Kang Wang, and followed a plan proposed by Shoucheng Zhang, professor of physics at Stanford, and colleagues. The team reported the results July 20 in Science.

MAJORANAS IN MOTION Majorana fermions (blue, red, and purple lines) travel through a topological insulator (horizontal bar) with a superconductor layered on top in this illustration of new experiments to detect the fermions. Green lines indicate electrons travelling on the edges of the topological insulator. Beijing Sondii Technology Co Ltd.

“Our team predicted exactly where to find the Majorana fermion and what to look for as its ‘smoking gun’ experimental signature,” said Zhang, a theoretical physicist and one of the senior authors of the research paper. “This discovery concludes one of the most intensive searches in fundamental physics, which spanned exactly 80 years.”

Although the search for the famous fermion seems more intellectual than practical, he added, it could have real-life implications for building robust quantum computers, although this is admittedly far in the future.

The particular type of Majorana fermion the research team observed is known as a “chiral” fermion because it moves along a one-dimensional path in just one direction. While the experiments that produced it were extremely difficult to conceive, set up and carry out, the signal they produced was clear and unambiguous, the researchers said.

“This research culminates a chase for many years to find chiral Majorana fermions. It will be a landmark in the field,” said Tom Devereaux, director of the Stanford Institute for Materials and Energy Sciences (SIMES) at SLAC National Accelerator Laboratory, where Zhang is a principal investigator.

“It does seem to be a really clean observation of something new,” said Frank Wilczek, a theoretical physicist and Nobel laureate at the Massachusetts Institute of Technology who was not involved in the study. “It’s not fundamentally surprising, because physicists have thought for a long time that Majorana fermions could arise out of the types of materials used in this experiment. But they put together several elements that had never been put together before, and engineering things so this new kind of quantum particle can be observed in a clean, robust way is a real milestone.”

Search for ‘quasiparticles’

Majorana’s prediction applied only to fermions that have no charge, like the neutron and neutrino. Scientists have since found an antiparticle for the neutron, but they have good reasons to believe that the neutrino could be its own antiparticle, and there are four experiments underway to find out – including EXO-200, the latest incarnation of the Enriched Xenon Observatory, in New Mexico. But these experiments are extraordinarily difficult and are not expected to produce an answer for about a decade.

About 10 years ago, scientists realized that Majorana fermions might also be created in experiments that explore the physics of materials – and the race was on to make that happen.

What they’ve been looking for are “quasiparticles” – particle-like excitations that arise out of the collective behavior of electrons in superconducting materials, which conduct electricity with 100 percent efficiency. The process that gives rise to these quasiparticles is akin to the way energy turns into short-lived “virtual” particles and back into energy again in the vacuum of space, according to Einstein’s famous equation E = mc2. While quasiparticles are not like the particles found in nature, they would nonetheless be considered real Majorana fermions.

Over the past five years, scientists have had some success with this approach, reporting that they had seen promising Majorana fermion signatures in experiments involving superconducting nanowires.

But in those cases the quasiparticles were “bound” – pinned to one particular place, rather than propagating in space and time – and it was hard to tell if other effects were contributing to the signals researchers saw, Zhang said.

A ‘smoking gun’

In the latest experiments at UCLA and UC-Irvine, the team stacked thin films of two quantum materials – a superconductor and a magnetic topological insulator – and sent an electrical current through them, all inside a chilled vacuum chamber.

The top film was a superconductor. The bottom one was a topological insulator, which conducts current only along its surface or edges but not through its middle. Putting them together created a superconducting topological insulator, where electrons zip along two edges of the material’s surface without resistance, like cars on a superhighway.

It was Zhang’s idea to tweak the topological insulator by adding a small amount of magnetic material to it. This made the electrons flow one way along one edge of the surface and the opposite way along the opposite edge.

Then the researchers swept a magnet over the stack. This made the flow of electrons slow, stop and switch direction. These changes were not smooth, but took place in abrupt steps, like identical stairs in a staircase.

At certain points in this cycle, Majorana quasiparticles emerged, arising in pairs out of the superconducting layer and traveling along the edges of the topological insulator just as the electrons did. One member of each pair was deflected out of the path, allowing the researchers to easily measure the flow of the individual quasiparticles that kept forging ahead. Like the electrons, they slowed, stopped and changed direction – but in steps exactly half as high as the ones the electrons took.

These half-steps were the smoking gun evidence the researchers had been looking for.

The results of these experiments are not likely to have any effect on efforts to determine if the neutrino is its own antiparticle, said Stanford physics Professor Giorgio Gratta, who played a major role in designing and planning EXO-200.

“The quasiparticles they observed are essentially excitations in a material that behave like Majorana particles,” Gratta said. “But they are not elementary particles and they are made in a very artificial way in a very specially prepared material. It’s very unlikely that they occur out in the universe, although who are we to say? On the other hand, neutrinos are everywhere, and if they are found to be Majorana particles we would show that nature not only has made this kind of particles possible but, in fact, has literally filled the universe with them.”

He added, “Where it gets more interesting is that analogies in physics have proved very powerful. And even if they are very different beasts, different processes, maybe we can use one to understand the other. Maybe we will discover something that is interesting for us, too.”

Angel particle

Far in the future, Zhang said, Majorana fermions could be used to construct robust quantum computers that aren’t thrown off by environmental noise, which has been a big obstacle to their development. Since each Majorana is essentially half a subatomic particle, a single qubit of information could be stored in two widely separated Majorana fermions, decreasing the chance that something could perturb them both at once and make them lose the information they carry.

For now, he suggests a name for the chiral Majorana fermion his team discovered: the “angel particle,” in reference to the best-selling 2000 thriller “Angels and Demons” in which a secret brotherhood plots to blow up the Vatican with a time bomb whose explosive power comes from matter-antimatter annihilation. Unlike in the book, he noted, in the quantum world of the Majorana fermion there are only angels – no demons.

The materials used for this study were produced at UCLA by a team led by postdoctoral researcher Qing Lin He and graduate student Lei Pan. Scientists from the KACST Center for Excellence in Green Nanotechnology in Saudia Arabia, UC-Davis, Florida State University, Fudan University in Shanghai and Shanghai Tech University also contributed to the experiment. Major funding came from the SHINES Center, an Energy Frontier Research Center at UC-Riverside funded by the U.S. Department of Energy Office of Science. Zhang’s work was funded by the DOE Office of Science through SIMES.

See the full article here .

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Stem Education Coalition

Leland and Jane Stanford founded the University to “promote the public welfare by exercising an influence on behalf of humanity and civilization.” Stanford opened its doors in 1891, and more than a century later, it remains dedicated to finding solutions to the great challenges of the day and to preparing our students for leadership in today’s complex world. Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto. Since 1952, more than 54 Stanford faculty, staff, and alumni have won the Nobel Prize, including 19 current faculty members

From UCLA: “Technique for measuring and controlling electron state is a breakthrough in quantum computing”

UCLA

July 06, 2017
Meghan Steele Horan

UCLA professor HongWen Jiang (center) and graduate students Blake Freeman and Joshua Schoenfield affixing a quantum dot device to the gold plate of a cooling chamber. Nick Penthorn.

During their research for a new paper on quantum computing, HongWen Jiang, a UCLA professor of physics, and Joshua Schoenfield, a graduate student in his lab, ran into a recurring problem: They were so excited about the progress they were making that when they logged in from home to their UCLA desktop — which allows only one user at a time — the two scientists repeatedly knocked each other off of the remote connection.

The reason for their enthusiasm: Jiang and his team created a way to measure and control the energy differences of electron valley states in silicon quantum dots, which are a key component of quantum computing research. The technique could bring quantum computing one step closer to reality.

“It’s so exciting,” said Jiang, a member of the California NanoSystems Institute. “We didn’t want to wait until the next day to find out the outcome.”

Quantum computing could enable more complex information to be encoded on much smaller computer chips, and it holds promise for faster, more secure problem-solving and communications than today’s computers allow.

In standard computers, the fundamental components are switches called bits, which use 0s and 1s to indicate that they are off or on. The building blocks of quantum computers, on the other hand, are quantum bits, or qubits.

The UCLA researchers’ breakthrough was being able to measure and control a specific state of a silicon quantum dot, known as a valley state, an essential property of qubits. The research was published in Nature Communications.

“An individual qubit can exist in a complex wave-like mixture of the state 0 and the state 1 at the same time,” said Schoenfield, the paper’s first author. “To solve problems, qubits must interfere with each other like ripples in a pond. So controlling every aspect of their wave-like nature is essential.”

Silicon quantum dots are small, electrically confined regions of silicon, only tens of nanometers across, that can trap electrons. They’re being studied by Jiang’s lab — and by researchers around the world — for their possible use in quantum computing because they enable scientists to manipulate electrons’ spin and charge.

Besides electrons’ spin and charge, another of their most important properties is their “valley state,” which specifies where an electron will settle in the non-flat energy landscape of silicon’s crystalline structure. The valley state represents a location in the electron’s momentum, as opposed to an actual physical location.

Scientists have realized only recently that controlling valley states is critical for encoding and analyzing silicon-based qubits, because even the tiniest imperfections in a silicon crystal can alter valley energies in unpredictable ways.

“Imagine standing on top of a mountain and looking down to your left and right, noticing that the valleys on either side appear to be the same but knowing that one valley was just 1 centimeter deeper than the other,” said Blake Freeman, a UCLA graduate student and co-author of the study. “In quantum physics, even that small of a difference is extremely important for our ability to control electrons’ spin and charge states.”

At normal temperatures, electrons bounce around, making it difficult for them to rest in the lowest energy point in the valley. So to measure the tiny energy difference between two valley states, the UCLA researchers placed silicon quantum dots inside a cooling chamber at a temperature near absolute zero, which allowed the electrons to settle down. By shooting fast electrical pulses of voltage through them, the scientists were able to move single electrons in and out of the valleys. The tiny difference in energy between the valleys was determined by observing the speed of the electron’s rapid switching between valley states.

After manipulating the electrons, the researchers ran a nanowire sensor very close to the electrons. Measuring the wire’s resistance allowed them to gauge the distance between an electron and the wire, which in turn enabled them to determine which valley the electron occupied.

The technique also enabled the scientists, for the first time, to measure the extremely small energy difference between the two valleys — which had been impossible using any other existing method.

In the future, the researchers hope to use more sophisticated voltage pulses and device designs to achieve full control over multiple interacting valley-based qubits.

“The dream is to have an array of hundreds or thousands of qubits all working together to solve a difficult problem,” Schoenfield said. “This work is an important step toward realizing that dream.”

The research was supported by the U.S. Army Research Office.

See the full article here .

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For nearly 100 years, UCLA has been a pioneer, persevering through impossibility, turning the futile into the attainable.

We doubt the critics, reject the status quo and see opportunity in dissatisfaction. Our campus, faculty and students are driven by optimism. It is not naïve; it is essential. And it has fueled every accomplishment, allowing us to redefine what’s possible, time after time.

This can-do perspective has brought us 12 Nobel Prizes, 12 Rhodes Scholarships, more NCAA titles than any university and more Olympic medals than most nations. Our faculty and alumni helped create the Internet and pioneered reverse osmosis. And more than 100 companies have been created based on technology developed at UCLA.

From NERSC: “Record-breaking 45-qubit Quantum Computing Simulation Run at NERSC on Cori”

NERSC

NERSC Cray Cori II supercomputer

LBL NERSC Cray XC30 Edison supercomputer

NERSC Hopper Cray XE6supercomputer

June 1, 2017
Kathy Kincade
kkincade@lbl.gov
+1 510 495 2124

When two researchers from the Swiss Federal Institute of Technology (ETH Zurich) announced in April that they had successfully simulated a 45-qubit quantum circuit, the science community took notice: it was the largest ever simulation of a quantum computer, and another step closer to simulating “quantum supremacy”—the point at which quantum computers become more powerful than ordinary computers.

A multi-qubit chip developed in the Quantum Nanoelectronics Laboratory at Lawrence Berkeley National Laboratory.

The computations were performed at the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility at the U.S. Department of Energy’s Lawrence Berkeley National Laboratory. Researchers Thomas Häner and Damien Steiger, both Ph.D. students at ETH, used 8,192 of 9,688 Intel Xeon Phi processors on NERSC’s newest supercomputer, Cori, to support this simulation, the largest in a series they ran at NERSC for the project.

“Quantum computing” has been the subject of dedicated research for decades, and with good reason: quantum computers have the potential to break common cryptography techniques and simulate quantum systems in a fraction of the time it would take on current “classical” computers. They do this by leveraging the quantum states of particles to store information in qubits (quantum bits), a unit of quantum information akin to a regular bit in classical computing. Better yet, qubits have a secret power: they can perform more than one calculation at a time. One qubit can perform two calculations in a quantum superposition, two can perform four, three eight, and so forth, with a corresponding exponential increase in quantum parallelism. Yet harnessing this quantum parallelism is difficult, as observing the quantum state causes the system to collapse to just one answer.

So how close are we to realizing a true working prototype? It is generally thought that a quantum computer deploying 49 qubits—a unit of quantum information—will be able to match the computing power of today’s most powerful supercomputers. Toward this end, Häner and Steiger’s simulations will aid in benchmarking and calibrating near-term quantum computers by carrying out quantum supremacy experiments with these early devices and comparing them to their simulation results. In the mean time, we are seeing a surge in investments in quantum computing technology from the likes of Google, IBM and other leading tech companies—even Volkswagen—which could dramatically accelerate the development process.

Simulation and Emulation of Quantum Computers

Both emulation and simulation are important for calibrating, validating and benchmarking emerging quantum computing hardware and architectures. In a paper [ACM=DL]presented at SC16, Häner and Steiger wrote: “While large-scale quantum computers are not yet available, their performance can be inferred using quantum compilation frameworks and estimates of potential hardware specifications. However, without testing and debugging quantum programs on small scale problems, their correctness cannot be taken for granted. Simulators and emulators … are essential to address this need.”

That paper discussed emulating quantum circuits—a common representation of quantum programs—while the 45-qubit paper focuses on simulating quantum circuits. Emulation is only possible for certain types of quantum subroutines, while the simulation of quantum circuits is a general method that also allows the inclusion of the effects of noise. Such simulations can be very challenging even on today’s fastest supercomputers, Häner and Steiger explained. For the 45-qubit simulation, for example, they used most of the available memory on each of the 8,192 nodes. “This increases the probability of node failure significantly, and we could not expect to run on the full system for more than an hour without failure,” they said. “We thus had to reduce time-to-solution at all scales (node-level as well as cluster-level) to achieve this simulation.”

Optimizing the quantum circuit simulator was key. Häner and Steiger employed automatic code generation, optimized the compute kernels and applied a scheduling algorithm to the quantum supremacy circuits, thus reducing the required node-to-node communication. During the optimization process they worked with NERSC staff and used Berkeley Lab’s Roofline Model to identify potential areas where performance could be boosted.

In addition to the 45-qubit simulation, which used 0.5 petabytes of memory on Cori and achieved a performance of 0.428 petaflops, they also simulated 30-, 36- and 42-qubit quantum circuits. When they compared the results with simulations of 30- and 36-qubit circuits run on NERSC’s Edison system, they found that the Edison simulations also ran faster.

“Our optimizations improved the performance – the number of floating-point operations per time – by 10x for Edison and between 10x and 20x for Cori (depending on the circuit to simulate and the size per node),” Häner and Steiger said. “The time-to-solution decreased by over 12x when compared to the times of a similar simulation reported in a recent paper on quantum supremacy by Boixo and collaborators, which made the 45-qubit simulation possible.”

Looking ahead, the duo is interested in performing more quantum circuit simulations at NERSC to determine the performance of near-term quantum computers solving quantum chemistry problems. They are also hoping to use solid-state drives to store larger wave functions and thus try to simulate even more qubits.

See the full article here.

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Stem Education Coalition

The National Energy Research Scientific Computing Center (NERSC) is the primary scientific computing facility for the Office of Science in the U.S. Department of Energy. As one of the largest facilities in the world devoted to providing computational resources and expertise for basic scientific research, NERSC is a world leader in accelerating scientific discovery through computation. NERSC is a division of the Lawrence Berkeley National Laboratory, located in Berkeley, California. NERSC itself is located at the UC Oakland Scientific Facility in Oakland, California.

More than 5,000 scientists use NERSC to perform basic scientific research across a wide range of disciplines, including climate modeling, research into new materials, simulations of the early universe, analysis of data from high energy physics experiments, investigations of protein structure, and a host of other scientific endeavors.

The NERSC Hopper system, a Cray XE6 with a peak theoretical performance of 1.29 Petaflop/s. To highlight its mission, powering scientific discovery, NERSC names its systems for distinguished scientists. Grace Hopper was a pioneer in the field of software development and programming languages and the creator of the first compiler. Throughout her career she was a champion for increasing the usability of computers understanding that their power and reach would be limited unless they were made to be more user friendly.

Grace Hopper

NERSC is known as one of the best-run scientific computing facilities in the world. It provides some of the largest computing and storage systems available anywhere, but what distinguishes the center is its success in creating an environment that makes these resources effective for scientific research. NERSC systems are reliable and secure, and provide a state-of-the-art scientific development environment with the tools needed by the diverse community of NERSC users. NERSC offers scientists intellectual services that empower them to be more effective researchers. For example, many of our consultants are themselves domain scientists in areas such as material sciences, physics, chemistry and astronomy, well-equipped to help researchers apply computational resources to specialized science problems.

From Yale: “Expanding the Quantum Computing Toolbox”

Yale University

January 17, 2017 [Just found this in social media.]
Noah Kravitz

In 2011, Canadian tech company D-Wave stunned the world by announcing that it would market a functioning quantum computer. Soon, companies ranging from Google to NASA bought versions of the device, and scientists began scrambling to evaluate what potentially was the biggest technological breakthrough of the century. One third-party test, in which the new quantum computer solved a complex math problem 3,600 times faster than a cutting-edge IBM supercomputer, seemed to substantiate D-Wave’s claims of quantum computation. Other tests found no evidence of quantum activity at all.

Quantum computing, an idea which has captivated physicists and computer scientists alike since its conception in the 1980s, has proven difficult to realize in practice. Because quantum computers rely on the uncertainty built into the laws of quantum physics, they are extremely sensitive to their environments. A small imperfection in even a single component of the design can be devastating. One technical challenge is that heat energy can disrupt the fragile quantum states, so quantum technology is usually cooled almost to absolute zero (-273 degrees Celsius). D-Wave’s quantum computer is small enough to hold in the palm of your hand but has to be housed in a 10-foot-tall refrigerator.

Yale researchers, led by Professor of Electrical Engineering and Physics Hong Tang, have developed a new version of a device called a piezo-optomechanical resonator that could allow quantum computers to operate at higher temperatures. The paper [Physical Review Letters], which is co-authored by graduate students Xu Han and Chang-Ling Zou, describes an improved method of connecting information in physical and electrical domains. This advance could be used as the basis for reliable memory storage for quantum computers—an important step towards stronger quantum computing.

D-Wave’s putative quantum computer made headlines as possibly realizing decades’ worth of theoretical physics research. Here, qubits are assembled on a circuit board, much like the layout of a classical computer. Image courtesy of Wikipedia

From Schrodinger’s cat to national security

Quantum computing fundamentally differs from classical computing in that it relies on the non-intuitive quantum properties of light and matter. In familiar classical computation, information is stored as bits which can take on the values 0 and 1—they are simple on/off electrical switches, and it is easy to check their positions. The computer then performs tasks using sequences of logical operations on the bits. For example, it might say that if bit A is 0, then bit B should be set to 0, but if bit A is 1, then bit B should be set to 1; or that bit C should be set to 1 only if bits A and B are different.

In quantum computing, by contrast, the situation is not so straightforward. First of all, information is stored in qubits (short for “quantum bits”) which have more than two possible values: 0, 1, and a combination of 0 and 1. These qubits are particles with distinct measurable quantum states corresponding to “0” and “1,” but one of the principles of quantum physics is that sometimes we can predict the result of a measurement only in terms of probabilities. So in quantum mechanics, even though sometimes we might know that we will always measure the particle as “0,” there can also exist a scenario in which there is a 50 percent chance of finding the particle in the “0” state and a 50 percent chance of finding it in the “1” state. The surprising part is that, mathematically speaking, the latter particle is actually in both states equally until we measure it as being in one or the other, and it is meaningful to think of such a qubit as having value ½ representing a “mixed” state even though ½ is not a possible measurement.

Another useful property of quantum mechanics called entanglement links the measurements of different particles. For example, if particles A and B are entangled, then we might know that whenever we measure both particles, we will get one “0” and one “1.” In this case, measuring one qubit immediately determines the value of the other, and it is possible to use this property to “teleport” information!

Unlike classical bits, which have only two possible values, qubits have a range of values from 0 to 1. In this common model of a qubit (where the North pole of the sphere represents the “0” state and the South pole the “1” state), the state of the qubit can be visualized as a point on the surface of the sphere. For instance, the value of any point above the equator is between 0 and ½, and the value of any point below the equator is between ½ and 1. Image courtesy of Columbia Science Review

The unique logical underpinnings of quantum computation allow quantum computer to approach old problems in new ways. Since qubits are more complex than regular bits, quantum algorithms are often more streamlined than their classical counterparts, especially when searching for optimal solutions to problems. For example, if we want to find a car that is hidden behind one door out of a million, a classical computer would have to check the doors one by one, and, in the worst-case scenario, it would have to make a million queries. A quantum computer, by contrast, can use a probabilistic algorithm to find the car in at most only a thousand queries.

Quantum computation has potential applications in many problems that would take classical computers longer than the age of the Earth. In the best-known example of this principle of “quantum speedup,” computer scientists have created a quantum algorithm that can factor large numbers (essentially a needle-in-a-haystack problem like the car example above) exponentially faster than is possible for any classical algorithm. Although this problem may not seem very exciting, it in fact underlies many more complex processes such as cryptography. Similar principles apply to choosing cost-effective combinations of building materials and even to identifying keywords for news articles. Unsurprisingly, quantum computation is often the best way to model complex natural systems.

We have made significant progress over the past few decades towards meeting the challenges of quantum computing. As early as the mid-1990s, we have manipulated qubits and written codes to correct spontaneous errors in quantum computers. In the 2000s, we demonstrated long-distance entanglement. In 2013, Hong Tang and his team contributed to the corpus of knowledge when they determined a method for measuring quantum systems without permanently altering them. Now, in 2016, the Tang Lab at Yale has once again expanded the quantum computing toolbox, this time in the stubbornly challenging field of information storage and transfer.

A new approach to quantum memory

You probably carry around in your pocket a crucial piece of the new Yale device: Smartphones contain the materials that Tang and his team used to bridge the mechanical-electrical gap. Piezoelectrics are materials, usually crystals, that accumulate charge when compressed, twisted or bent. For instance, when a piezoelectric sheet is creased, a net negative charge forms at the fold, and net positive charges form at the ends. Conversely, when an external magnetic field causes charges in a piezoelectric to move, the object responds by changing shape physically. In this way, vibrations in physical objects and electrical fields can easily be connected, or, as physicists say, coupled. Piezoelectrics in smartphones often power tiny speakers— they convert electrical signals into sound waves, which arise from physical pulses.

The Yale piezo-optomechanical device consists of a pair of tiny resonators: a silicon wafer and a wire loop situated above it. “It is useful to think of a resonator like a tuning fork because it responds most powerfully to a particular resonant frequency,” said Han, an electrical engineering Ph.D candidate who worked on the project. The two ends of the wire loop do not quite connect, so electrical charges tend to bounce back and forth around the circle, which functions as an electrical resonator in the microwave region of the electromagnetic spectrum. The wafer, which is about as thick as five sheets of paper, functions as an acoustic, or mechanical, resonator. This resonator is coated with a thin layer of aluminum nitride, a piezoelectric material, which facilitates the exchange of oscillations—and energy— between mechanical and electrical components. “If you want to transfer information between two systems, it is necessary to have an efficient coupling mechanism,” Han said.

Professor Hong Tang (right) and graduate students Chang-Ling Zou (left) and Xu Han (not pictured) developed a piezo-optomechanical resonator that has applications to quantum memory storage. Image courtesy of Hong Tang

The idea of coupling between mechanical and microwave electrical domains is not new; the Yale team’s innovation is achieving stronger coupling on a smaller scale. The key is using resonators with a higher frequency: Whereas other designs have used frequencies on the order of a few million oscillations per second, the Yale design runs at ten billion oscillations per second. As a result, the device is solidly in the so-called strong-coupling regime —meaning that the rate of information transfer is greater than the natural energy dissipation rates of the individual systems—and transmitted signals are clearer and longer-lasting. Yet high frequency comes at the cost of increased construction difficulties. “Since the device is small, it is more susceptible to perturbations in the environment,” Tang said. As a result, the design carefully balances considerations of compactness and robustness.

The researchers believe that applications of their breakthrough lie mostly in the far future. “This is fundamental research, so it’s not immediately pertinent to daily life,” Han said. Instead, the piezo-optomechanical resonator’s real value is as a component of more complex systems. Because of the strong coupling achieved, it is well suited for quantum uses where “noise” from ambient heat (analogous to TV static) would otherwise be disruptive. “For high-frequency devices, the temperature requirement is not as low,” Han said. Chang-Ling Zou, a postdoctoral student in Tang’s lab, hopes to develop this strength into a basis for quantum memory storage, which is currently unfeasible at most temperatures. Small vibrating crystals would serve as physical memory, and the resonators would convert between these crystals and the computational part of the computer, which would likely operate in the microwave domain.

The Yale team is also looking to incorporate visible light into their design. “The next step is integrating an optical resonator and using the acoustic resonator as an intermediary between microwave and optics,” Han said. Accomplishing this feat could improve computer signal processing, radio receiving efficiency, and information transmission across long distances via optical fiber cables.

Given its versatility, the piezo-optomechanical resonator may find its way into all kinds of applications. From analyzing the stock market to sending trans-Atlantic messages, you can expect to hear more about this small device in big-time situations.

See the full article here .

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Stem Education Coalition

Yale University comprises three major academic components: Yale College (the undergraduate program), the Graduate School of Arts and Sciences, and the professional schools. In addition, Yale encompasses a wide array of centers and programs, libraries, museums, and administrative support offices. Approximately 11,250 students attend Yale.

From SA: “Quantum Computing Becomes More Accessible”

Scientific American

June 26, 2017
Dario Gil

Credit: World Economic Forum

Quantum computing has captured imaginations for almost 50 years. The reason is simple: it offers a path to solving problems that could never be answered with classical machines. Examples include simulating chemistry exactly to develop new molecules and materials and solving complex optimization problems, which seek the best solution from among many possible alternatives. Every industry has a need for optimization, which is one reason this technology has so much disruptive potential.

Until recently, access to nascent quantum computers was restricted to specialists in a few labs around the world. But progress over the past several years has enabled the construction of the world’s first prototype systems that can finally test out ideas, algorithms and other techniques that until now were strictly theoretical.

Quantum computers tackle problems by harnessing the power of quantum mechanics. Rather than considering each possible solution one at a time, as a classical machine would, they behave in ways that cannot be explained with classical analogies. They start out in a quantum superposition of all possible solutions, and then they use entanglement and quantum interference to home in on the correct answer—processes that we do not observe in our everyday lives. The promise they offer, however, comes at the cost of them being difficult to build. A popular design requires superconducting materials (kept 100 times colder than outer space), exquisite control over delicate quantum states and shielding for the processor to keep out even a single stray ray of light.

Existing machines are still too small to fully solve problems more complex than supercomputers can handle today. Nevertheless, tremendous progress has been made. Algorithms have been developed that will run faster on a quantum machine. Techniques now exist that prolong coherence (the lifetime of quantum information) in superconducting quantum bits by a factor of more than 100 compared with 10 years ago. We can now measure the most important kinds of quantum errors. And in 2016 IBM provided the public access to the first quantum computer in the cloud—the IBM Q experience—with a graphical interface for programming it and now an interface based on the popular programming language Python. Opening this system to the world has fueled innovations that are vital for this technology to progress, and to date more than 20 academic papers have been published using this tool. The field is expanding dramatically. Academic research groups and more than 50 start-ups and large corporations worldwide are focused on making quantum computing a reality.

With these technological advancements and a machine at anyone’s fingertips, now is the time for getting “quantum ready.” People can begin to figure out what they would do if machines existed today that could solve new problems. And many quantum computing guides are available online to help them get started.

There are still many obstacles. Coherence times must improve, quantum error rates must decrease, and eventually, we must mitigate or correct the errors that do occur. Researchers will continue to drive innovations in both the hardware and software. Investigators disagree, however, over which criteria should determine when quantum computing has achieved technological maturity. Some have proposed a standard defined by the ability to perform a scientific measurement so obscure that it is not easily explained to a general audience. I and others disagree, arguing that quantum computing will not have emerged as a technology until it can solve problems that have commercial, intellectual and societal importance. The good news is, that day is finally within our sights.

See the full article here .

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Stem Education Coalition

Scientific American, the oldest continuously published magazine in the U.S., has been bringing its readers unique insights about developments in science and technology for more than 160 years.

From COSMOS: “Multi-coloured photons in 100 dimensions may make quantum computers stronger”

COSMOS

29 June 2017
Andrew Masterson

An illustration showing high-dimensional color-entangled photon states from a photonic chip, manipulated and transmitted via telecommunications systems.
Michael Kues.

By using manipulating the frequency of entangled photons, researchers have found a way to make more stable tools for quantum computing from off-the-shelf equipment.

Researchers using off-the-shelf telecommunications equipment have created a 100-dimensional quantum system from the entanglement of two subatomic particles.

The system can be controlled and manipulated to perform high-level gateway functions – a critical component of any viable quantum computer – the scientists report in the journal Nature.

The team, led by Michael Kues of the University of Glasgow, effectively created a quantum photon generator on a chip. The tiny device uses a micro-ring resonator generate entangled pairs of photons from a laser input.

The entanglement is far from simple. Each photon is composed of a superposition of several different colours, all expressed simultaneously, giving the photon several dimensions. The expression of any individual colour – or frequency, if you like – is mirrored across the two entangled photons, regardless of the distance between them.

The complexity of the photon pairs represents a major step forward in manipulating quantum entities.

Almost all research into quantum states, for the purpose of developing quantum computing, has to date focussed on qubits: artificially created subatomic particles that exist in a superposition two possible states. (They are the quantum equivalent of standard computing ‘bits’, basic units that are capable only of being switched between 1 and 0, or yes/no, or on/off.)

Kues and colleagues are instead working with qudits, which are essentially qubits with superpositions comprising three or more states.

In 2016, Russian researchers showed that qudit-based quantum computing systems were inherently more stable than their two dimensional predecessors.

The Russians, however, were working with a subset of qudits called qutrits, which comprise a superposition of three possible states. Kues and his team upped the ante considerably, fashioning qudits comprising 10 possible states – one for each of the colours, or frequencies, of the photon – giving an entangled pair a minimum of 100.

And that’s just the beginning. Team member Roberto Morandotti of the University of Electronic Science and Technology of China, in Chengdu, suggests that further refinement will produce entangled two-qudit systems containing as many as 9000 dimensions, bringing a robustness and complexity to quantum computers that is at present unreachable.

Kues adds that perhaps the most attractive feature of his team’s achievement is that it was done using commercially available components. This means that the strategy can be quickly and easily adapted by other researchers in the field, potentially ushering in a period of very rapid development.

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From MIT: “Toward mass-producible quantum computers”

MIT News

May 26, 2017 [Always glad to find something I missed.]
Larry Hardesty

A team of researchers from MIT, Harvard University, and Sandia National Laboratories reports a new technique for creating targeted defects in diamond materials, which is simpler and more precise than its predecessors and could benefit diamond-based quantum computing devices.

Quantum computers are experimental devices that offer large speedups on some computational problems. One promising approach to building them involves harnessing nanometer-scale atomic defects in diamond materials.

But practical, diamond-based quantum computing devices will require the ability to position those defects at precise locations in complex diamond structures, where the defects can function as qubits, the basic units of information in quantum computing. In today’s issue of Nature Communications, a team of researchers from MIT, Harvard University, and Sandia National Laboratories reports a new technique for creating targeted defects, which is simpler and more precise than its predecessors.

In experiments, the defects produced by the technique were, on average, within 50 nanometers of their ideal locations.

“The dream scenario in quantum information processing is to make an optical circuit to shuttle photonic qubits and then position a quantum memory wherever you need it,” says Dirk Englund, an associate professor of electrical engineering and computer science who led the MIT team. “We’re almost there with this. These emitters are almost perfect.”

The new paper has 15 co-authors. Seven are from MIT, including Englund and first author Tim Schröder, who was a postdoc in Englund’s lab when the work was done and is now an assistant professor at the University of Copenhagen’s Niels Bohr Institute. Edward Bielejec led the Sandia team, and physics professor Mikhail Lukin led the Harvard team.

Appealing defects

Quantum computers, which are still largely hypothetical, exploit the phenomenon of quantum “superposition,” or the counterintuitive ability of small particles to inhabit contradictory physical states at the same time. An electron, for instance, can be said to be in more than one location simultaneously, or to have both of two opposed magnetic orientations.

Where a bit in a conventional computer can represent zero or one, a “qubit,” or quantum bit, can represent zero, one, or both at the same time. It’s the ability of strings of qubits to, in some sense, simultaneously explore multiple solutions to a problem that promises computational speedups.

Diamond-defect qubits result from the combination of “vacancies,” which are locations in the diamond’s crystal lattice where there should be a carbon atom but there isn’t one, and “dopants,” which are atoms of materials other than carbon that have found their way into the lattice. Together, the dopant and the vacancy create a dopant-vacancy “center,” which has free electrons associated with it. The electrons’ magnetic orientation, or “spin,” which can be in superposition, constitutes the qubit.

A perennial problem in the design of quantum computers is how to read information out of qubits. Diamond defects present a simple solution, because they are natural light emitters. In fact, the light particles emitted by diamond defects can preserve the superposition of the qubits, so they could move quantum information between quantum computing devices.

Silicon switch

The most-studied diamond defect is the nitrogen-vacancy center, which can maintain superposition longer than any other candidate qubit. But it emits light in a relatively broad spectrum of frequencies, which can lead to inaccuracies in the measurements on which quantum computing relies.

In their new paper, the MIT, Harvard, and Sandia researchers instead use silicon-vacancy centers, which emit light in a very narrow band of frequencies. They don’t naturally maintain superposition as well, but theory suggests that cooling them down to temperatures in the millikelvin range — fractions of a degree above absolute zero — could solve that problem. (Nitrogen-vacancy-center qubits require cooling to a relatively balmy 4 kelvins.)

To be readable, however, the signals from light-emitting qubits have to be amplified, and it has to be possible to direct them and recombine them to perform computations. That’s why the ability to precisely locate defects is important: It’s easier to etch optical circuits into a diamond and then insert the defects in the right places than to create defects at random and then try to construct optical circuits around them.

In the process described in the new paper, the MIT and Harvard researchers first planed a synthetic diamond down until it was only 200 nanometers thick. Then they etched optical cavities into the diamond’s surface. These increase the brightness of the light emitted by the defects (while shortening the emission times).

Then they sent the diamond to the Sandia team, who have customized a commercial device called the Nano-Implanter to eject streams of silicon ions. The Sandia researchers fired 20 to 30 silicon ions into each of the optical cavities in the diamond and sent it back to Cambridge.

Mobile vacancies

At this point, only about 2 percent of the cavities had associated silicon-vacancy centers. But the MIT and Harvard researchers have also developed processes for blasting the diamond with beams of electrons to produce more vacancies, and then heating the diamond to about 1,000 degrees Celsius, which causes the vacancies to move around the crystal lattice so they can bond with silicon atoms.

After the researchers had subjected the diamond to these two processes, the yield had increased tenfold, to 20 percent. In principle, repetitions of the processes should increase the yield of silicon vacancy centers still further.

When the researchers analyzed the locations of the silicon-vacancy centers, they found that they were within about 50 nanometers of their optimal positions at the edge of the cavity. That translated to emitted light that was about 85 to 90 percent as bright as it could be, which is still very good.

“It’s an excellent result,” says Jelena Vuckovic, a professor of electrical engineering at Stanford University who studies nanophotonics and quantum optics. “I hope the technique can be improved beyond 50 nanometers, because 50-nanometer misalignment would degrade the strength of the light-matter interaction. But this is an important step in that direction. And 50-nanometer precision is certainly better than not controlling position at all, which is what we are normally doing in these experiments, where we start with randomly positioned emitters and then make resonators.”

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From U Toronto: “U of T’s Creative Destruction Lab goes quantum”

University of Toronto

May 26, 2017
Chris Sorensen

Startups participating in CDL’s new quantum machine learning stream will have cloud access to Vancouver-based D-Wave’s quantum computer (photo courtesy D-Wave Systems)

Startup accelerator launches new quantum machine learning stream for startups.

The roughly 25 startups lucky enough to be accepted to a new quantum machine learning stream at a University of Toronto accelerator are about to become part of a very exclusive club.

The Creative Destruction Lab (CDL) at U of T’s Rotman School of Management said Thursday that it will provide the startups with access to the world’s only commercially available quantum computers, built by Vancouver’s D-Wave Systems, beginning in September.

To date, only a handful of U.S.-based organizations have had the tens of millions to invest in D-Wave’s bleeding edge technology. They include Google, Lockheed Martin and Los Alamos National Laboratory.

“We’re removing the barriers to entry to what’s available in terms of quantum computing – and that will hopefully spawn new, interesting applications from early-stage startups,” said Daniel Mulet, an associate director at the CDL accelerator in Toronto, which focuses on scaling science-based startups with artificial intelligence, or AI, technologies.

It’s yet another example of how U of T has emerged as a hotbed of computer science research that’s spawning a host of futuristic, AI-equipped companies, ranging from legal research firm ROSS Intelligence to medical startup Deep Genomics. In just the past few months, the university also helped launch the Vector Institute for Artificial Intelligence and saw star AI researcher Raquel Urtasun form a partnership with ride-sharing giant Uber, which plans to set up a driverless car lab in Toronto.

Making D-Wave’s quantum machines available to CDL startups follows in the footsteps of other U of T efforts to ensure researchers have access to the latest and most powerful computing tools. The university is one of several members of the Southern Ontario Smart Computing Innovation Platform (SOSCIP), which offers researchers access to several powerful computing platforms, including IBM’s Watson.

Mulet said CDL, which recently announced a bold cross-Canada expansion, is now hoping to lay the groundwork for the next phase of AI development by combining machine learning – computers capable of learning without explict human instructions – with the nascent, but potentially game-changing field of quantum computing.

“Canada, with companies like D-Wave, and groups like IQC and Perimeter, has all the elements to seed a quantum computing and quantum machine learning software industry,” he said, referring to the University of Waterloo’s Institute for Quantum Computing and the Perimeter Institute for Theoretical Physics. “We would like it to happen here before it happens somewhere else in the world.”

For those unfamiliar with quantum computers, D-Wave’s machine will sound like something straight out of a science fiction movie. It’s a giant black box, about the size of a garden shed, that surrounds a core cooled 180 times below the temperature of deep space. The heavily shielded, otherworldly interior is necessary to allow the quantum bits, or qubits, to exhibit their quantum properties.

So what, exactly, is a quantum computer and what does it have to do with machine learning?

The idea is to harness the mind-bending properties of quantum mechanics to achieve an exponential increase in computational power. That includes the quantum principle of superposition, which allows quantum particles to exist in more than one state simultaneously. One oft-used explanation (and the one cited by Prime Minister Justin Trudeau last year): classical computer bits are binary, with a value of either one or zero – on or off – whereas a quantum qubit can be both one and zero – on and off – at the same time.

D-Wave co-founder Eric Ladizinsky offered a more visual explanation during a 2014 conference in London. Imagine, he said, trying to find an X scribbled inside one of the 50 million books in the U.S. Library of Congress. A traditional computer functions like a person trying to systimatically open each book and flip through its pages, he continued, “but what if, somehow, I could put you in this magical state of quantum superposition, so you were in 50 million parallel realities and in each one you could try opening a different book?”

“We are making a bet that in the next five years quantum speedup useful for machine learning will be achieved,” Mulet said. “When you apply that to creating intelligent systems, those systems become that much more powerful.”

Vern Brownell, the CEO of D-Wave, said the partnership with CDL, and the prospect of building an ecosystem of quantum AI and machine learning startups, spoke directly to the company’s vision of “bringing quantum computing out of the research lab and into the real world.”

While CDL won’t have one of D-Wave’s $15 million 2000Q computers on location at U of T, Mulet says the up to 40 individuals accepted to the program – the application deadline is July 24 – will have access to its computational power through the cloud. The startups will also receive training from the same teams that D-Wave dispatches to its large corporate customers, and will participate in an intensive “bootcamp” led by Peter Wittek, a Barcelona-based research scientist who wrote the first textbook on quantum machine learning.

CDL said three Silicon Valley-based venture capital firms – Bloomberg Beta, Data Collective and Spectrum 28 – will offer to invest pre-seed capital in every company admitted to, or formed in, the program, so long as they meet certain basic criteria.

Mulet added the arrangement with D-Wave, whose founder Geordie Rose is a CDL Fellow, was three years in the making.

It should be noted, however, that D-Wave’s vision of quantum computing isn’t shared by everyone in the field. The company focuses on a particular type of quantum function known as quantum annealing, which can only be used to solve certain types of optimization problems – and even then D-Wave’s machines don’t always outperform traditional computers. By contrast, other researchers, including those at IBM, are striving to build a univerisal quantum machine that could handle various types of complex calculations that would take classical computers months or even years to solve.

In the meantime, a growing number of researchers are experimenting with D-Wave’s machines. One example: Scientists at Volkswagen recently used a similar cloud-based version of D-Wave’s machine to figure out the fastest way to send 10,000 Beijing taxi cabs to the nearest airport without creating a traffic jam. Other problems that D-Wave claims can be tackled with its system include optimizing cancer radio therapy, developing new drug types and designing more efficient water networks.

D-Wave has also courted controversy in the past because it wasn’t always clear to researchers whether its machines truly demonstrated quantum properties.

Mulet, however, says the academic debate surrounding D-Wave’s approach is less interesting to CDL than what its startups do with it. “We’re proponents of building impactful companies,” he said, adding that CDL plans to incorporate other types of quantum computers when they become commercially available. “So it doesn’t really matter where your science and technology comes from as long as it creates value for customers.”

See the full article here .

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Established in 1827, the University of Toronto has one of the strongest research and teaching faculties in North America, presenting top students at all levels with an intellectual environment unmatched in depth and breadth on any other Canadian campus.

Established in 1827, the University of Toronto has one of the strongest research and teaching faculties in North America, presenting top students at all levels with an intellectual environment unmatched in depth and breadth on any other Canadian campus.

From COSMOS: “Doped diamond may lead to everyday quantum computers”

COSMOS

29 May 2017
Andrew Masterson

Precise placement of atoms in a diamond lattice may be a handy technique for quantum computer manufacture. Victor Habbick Visions / Getty

Quantum computers are still halfway mythical, but they are moving closer to reality step by tiny step.

One of the most widely favoured structures for building viable quantum computers is a diamond surface dotted with irregularities only a couple of atoms wide.

The problem researchers face, however, is making sure those irregularities – essentially atom-scale holes and accompanying bits of atom-wide foreign material – are drilled into the diamond substrate in exactly the right spot.

A report at Nature Communications by a team from MIT, Harvard University, and Sandia National Laboratories, in the US, covers a new method of doing so, creating the “defects” in the diamond crystal structure within 50 nanometres of their optimal locations.

The precise placement of the irregularities – known as “dopant-vacancies” in the business – is a critical outcome if quantum computers are ever to end up on the market.

This is because the combination of a tiny hole and a couple of atoms of non-diamond matter – nitrogen, for instance – can be engineered to act as a qubit, the fundamental element of quantum computing.

At the heart of a qubit is a subatomic particle that can simultaneously occupy a number of contradictory states – on, off, and a “superposition” of both together, for instance. The combination of the hole, the foreign atoms, and the light refracted through the diamond combine to create an elegant qubit.

At least, theoretically. To date, most experimental work has been done using nitrogen dopant-vacancies. These have the advantage of being able to maintain superposition longer than other candidates, but emit light across a broad range of frequencies, making information retrieval difficult.

The MIT-Harvard-Sandia team, led by Tim Schröder, experimented instead with silicon-based defects, which emit light in a much narrower range. That advantage, however, comes with its own challenge: the silicon dopant-vacancies need to be chilled to within a few thousands of a degree above absolute zero if they are to maintain a superposition for any length of time.

That remains a challenge still to be met, however. The import of the current study, published in the journal Nature Communications, lies in the increase in the accuracy of positioning the defects in the diamond.

To achieve this, scientists at MIT and Harvard first created a sliver of diamond only 200 nanometres thick. Onto this they etched tiny cavities.

The substrate was then sent to the Sandia laboratories, where each cavity was bombarded with 20 to 30 silicon ions. The process led to only about two percent of the cavities attracting silicon residents.

Back at MIT a second new process was employed. The diamond sliver was heated to 1000 ºC, at which temperature its component lattice became malleable, allowing the researchers to align more cavities with more silicon particles – taking the total number of dopant-vacancies to 20%.

Most of the irregularities thus produced were within 50 nanometres of their optimal position, and shone at around 85% of optimal brightness.

A quantum computer in every household is still a long way off, but this study marks a potentially important step in the journey.

See the full article here .

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From Stanford: “Stanford team brings quantum computing closer to reality with new materials”

Stanford University

May 9, 2017
Tom Abate

Researchers are developing quantum computers based on light rather than electricity. At Stanford, new materials could be the key to progress in this field. (Image credit: iStock/Pobytov)

For 60 years computers have become smaller, faster and cheaper. But engineers are approaching the limits of how small they can make silicon transistors and how quickly they can push electricity through devices to create digital ones and zeros.

That limitation is why Stanford electrical engineering Professor Jelena Vuckovic is looking to quantum computing, which is based on light rather than electricity. Quantum computers work by isolating spinning electrons inside a new type of semiconductor material. When a laser strikes the electron, it reveals which way it is spinning by emitting one or more quanta, or particles, of light. Those spin states replace the ones and zeros of traditional computing.

Vuckovic, who is one of the world’s leading researchers in the field, said quantum computing is ideal for studying biological systems, doing cryptography or data mining – in fact, solving any problem with many variables.

“When people talk about finding a needle in a haystack, that’s where quantum computing comes in,” she said.

Marina Radulaski, a postdoctoral fellow in Vuckovic’s lab, said the problem-solving potential of quantum computers stems from the complexity of the laser-electron interactions at the core of the concept.

“With electronics you have zeros and ones,” Radulaski said. “But when the laser hits the electron in a quantum system, it creates many possible spin states, and that greater range of possibilities forms the basis for more complex computing.”

Capturing electrons

Harnessing information based on the interactions of light and electrons is easier said than done. Some of the world’s leading technology companies are trying to build massive quantum computers that rely on materials super-cooled to near absolute zero, the theoretical temperature at which atoms would cease to move.

In her own studies of nearly 20 years, Vuckovic has focused on one aspect of the challenge: creating new types of quantum computer chips that would become the building blocks of future systems.

“To fully realize the promise of quantum computing we will have to develop technologies that can operate in normal environments,” she said. “The materials we are exploring bring us closer toward finding tomorrow’s quantum processor.”

The challenge for Vuckovic’s team is developing materials that can trap a single, isolated electron. Working with collaborators worldwide, they have recently tested three different approaches to the problem, one of which can operate at room temperature – a critical step if quantum computing is going to become a practical tool.

In all three cases the group started with semiconductor crystals, material with a regular atomic lattice like the girders of a skyscraper. By slightly altering this lattice, they sought to create a structure in which the atomic forces exerted by the material could confine a spinning electron.

“We are trying to develop the basic working unit of a quantum chip, the equivalent of the transistor on a silicon chip,” Vuckovic said.

Quantum dots

One way to create this laser-electron interaction chamber is through a structure known as a quantum dot. Physically, the quantum dot is a small amount of indium arsenide inside a crystal of gallium arsenide. The atomic properties of the two materials are known to trap a spinning electron.

In a recent paper in Nature Physics, Kevin Fischer, a graduate student in the Vuckovic lab, describes how the laser-electron processes can be exploited within such a quantum dot to control the input and output of light. By sending more laser power to the quantum dot, the researchers could force it to emit exactly two photons rather than one. They say the quantum dot has practical advantages over other leading quantum computing platforms but still requires cryogenic cooling, so it may not be useful for general-purpose computing. However, it could have applications in creating tamper-proof communications networks.

Color centers

In two other papers Vuckovic took a different approach to electron capture, by modifying a single crystal to trap light in what is called a color center.

In a recent paper published in NanoLetters, her team focused on color centers in diamond. In nature the crystalline lattice of a diamond consists of carbon atoms. Jingyuan Linda Zhang, a graduate student in Vuckovic’s lab, described how a 16-member research team replaced some of those carbon atoms with silicon atoms. This one alteration created color centers that effectively trapped spinning electrons in the diamond lattice.

But like the quantum dot, most diamond color center experiments require cryogenic cooling. Though that is an improvement over other approaches that required even more elaborate cooling, Vuckovic wanted to do better.

So she worked with another global team to experiment with a third material, silicon carbide. Commonly known as carborundum, silicon carbide is a hard, transparent crystal used to make clutch plates, brake pads and bulletproof vests. Prior research had shown that silicon carbide could be modified to create color centers at room temperature. But this potential had not yet been made efficient enough to yield a quantum chip.

Vuckovic’s team knocked certain silicon atoms out of the silicon carbide lattice in a way that created highly efficient color centers. They also fabricated nanowire structures around the color centers to improve the extraction of photons. Radulaski was the first author on that experiment, which is described in another NanoLetters paper. She said the net results – an efficient color center, operating at room temperature, in a material familiar to industry – were huge pluses.

“We think we’ve demonstrated a practical approach to making a quantum chip,” Radulaski said.

But the field is still in its early days and electron tapping is no simple feat. Even the researchers aren’t sure which method or methods will win out.

“We don’t know yet which approach is best, so we continue to experiment,” Vuckovic said.

The diamond research team included Stanford faculty members Zhi-Xun Shen, the Paul Pigott Professor in Physical Sciences, professor of photon science, of physics and of applied physics, and a senior fellow at the Precourt Institute for Energy; Nicholas Melosh, an associate professor of materials science and engineering and of photon science; and Steven Chu, the William R. Kenan Jr. Professor, professor of physics and of molecular and cellular physiology, and member of Stanford Bio-X and the Stanford Neurosciences Institute.

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Leland and Jane Stanford founded the University to “promote the public welfare by exercising an influence on behalf of humanity and civilization.” Stanford opened its doors in 1891, and more than a century later, it remains dedicated to finding solutions to the great challenges of the day and to preparing our students for leadership in today’s complex world. Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto. Since 1952, more than 54 Stanford faculty, staff, and alumni have won the Nobel Prize, including 19 current faculty members