Monthly Archives: March 2017

Search Engines for Brain Available at Sight: The Reboot Human Brain Project

The human brain is smaller than you might expect: One of them, dripping with formaldehyde, fits in a single gloved hand of a lab supervisor here at the Jülich Research Center, in Germany.

Soon, this rubbery organ will be frozen solid, coated in glue, and then sliced into several thousand wispy slivers, each just 60 micrometers thick. A custom apparatus will scan those sections using 3D polarized light imaging (3D-PLI) to measure the spatial orientation of nerve fibers at the micrometer level. The scans will be gathered into a colorful 3D digital reconstruction depicting the direction of individual nerve fibers on larger scales—roughly 40 gigabytes of data for a single slice and up to a few petabytes for the entire brain. And this brain is just one of several to be scanned.

Neuroscientists hope that by combining and exploring data gathered with this and other new instruments they’ll be able to answer fundamental questions about the brain. The quest is one of the final frontiers—and one of the greatest challenges—in science.

Imagine being able to explore the brain the way you explore a website. You might search for the corpus callosum—the stalk that connects the brain’s two hemispheres—and then flip through individual nerve fibers in it. Next, you might view networks of cells as they light up during a verbal memory test, or scroll through protein receptors embedded in the tissue.

Right now, neuroscientists can’t do that. They lack the hardware to store and access the avalanche of brain data being produced around the world. They lack the software to bridge the gaps from genes, molecules, and cells to networks, connectivity, and human behavior.

“We don’t have the faintest idea of the molecular basis for diseases like Alzheimer’s or schizophrenia or others. That’s why there are no cures,” says Paolo Carloni, director of the Institute for Computational Biomedicine at Jülich. “To make a big difference, we have to dissect [the brain] into little pieces and build it up again.”

That’s why there’s no choice but to move from small-scale investigations to large, collaborative efforts. “The brain is too complex to sit in your office and solve it alone,” says neuroscientist Katrin Amunts, who coleads the 3D-PLI project at Jülich. Neuroscientists need to make the same transition that physicists and geneticists once did—from solo practitioners to consortia—and that transformation won’t be easy.

Chip Hall of Fame: Western Digital WD1402A UART

Gordon Bell is famous for launching the PDP series of minicomputers at Digital Equipment Corp. in the 1960s. These ushered in the era of networked and interactive computing that would come to full flower with the introduction of the personal computer in the 1970s. But while minicomputers as a distinct class now belong to the history books, Bell also invented a lesser known but no less significant piece of technology that’s still in action all over the world: The universal asynchronous receiver/transmitter, or UART.

UARTs are used to let two digital devices communicate with each other by sending bits one at a time over a serial interface without bothering the device’s primary processor with the details.

Today, more sophisticated serial setups are available, such as the ubiquitous USB standard, but for a time UARTs ruled supreme as the way to, for example, connect modems to PCs. And the simple UART still has its place, not least as the communication method of last resort with a lot of modern network equipment.

The UART was invented because of Bell’s own need to connect a Teletype to a PDP-1, a task that required converting parallel signals into serial signals. He cooked up a circuit that used some 50 discrete components. The idea proved popular and Western Digital, a small company making calculator chips, offered to create a single-chip version of the UART. Western Digital founder Al Phillips still remembers when his vice president of engineering showed him the Rubylith sheets with the design, ready for fabrication. “I looked at it for a minute and spotted an open circuit,” Phillips says. “The VP got hysterical.” Western Digital introduced the WD1402A around 1971, and other versions soon followed.

Rigetti Launches Full-Stack Quantum Computing Service and Quantum IC Fab

Much of the ongoing quantum computing battle among tech giants such as Google and IBM has focused on developing the hardware necessary to solve impossible classical computing problems. A Berkeley-based startup looks to beat those larger rivals with a one-two combo: a fab lab designed for speedy creation of better quantum circuits and a quantum computing cloud service that provides early hands-on experience with writing and testing software.

Rigetti Computing recently unveiled its Fab-1 facility, which will enable its engineers to rapidly build new generations of quantum computing hardware based on quantum bits, or qubits. The facility can spit out entirely new designs for 3D-integrated quantum circuits within about two weeks—much faster than the months usually required for academic research teams to design and build new quantum computing chips. It’s not so much a quantum computing chip factory as it is a rapid prototyping facility for experimental designs.

“We’re fairly confident it’s the only dedicated quantum computing fab in the world,” says Andrew Bestwick, director of engineering at Rigetti Computing. “By the standards of industry, it’s still quite small and the volume is low, but it’s designed for extremely high-quality manufacturing of these quantum circuits that emphasizes speed and flexibility.”

But Rigetti is not betting on faster hardware innovation alone. It has also announced its Forest 1.0 service that enables developers to begin writing quantum software applications and simulating them on a 30-qubit quantum virtual machine. Forest 1.0 is based on Quil—a custom instruction language for hybrid quantum/classical computing—and open-source python tools intended for building and running Quil programs.

By signing up for the service, both quantum computing researchers and scientists in other fields will get the chance to begin practicing how to write and test applications that will run on future quantum computers. And it’s likely that Rigetti hopes such researchers from various academic labs or companies could end up becoming official customers.

“We’re a full stack quantum computing company,” says Madhav Thattai, Rigetti’s chief strategy officer. “That means we do everything from design and fabrication of quantum chips to packaging the architecture needed to control the chips, and then building the software so that people can write algorithms and program the system.”

Much still has to be done before quantum computing becomes a practical tool for researchers and companies. Rigetti’s approach to universal quantum computing uses silicon-based superconducting qubits that can take advantage of semiconductor manufacturing techniques common in today’s computer industry. That means engineers can more easily produce the larger arrays of qubits necessary to prove that quantum computing can outperform classical computing—a benchmark that has yet to be reached.

Google researchers hope to demonstrate such “quantum supremacy” over classical computing with a 49-qubit chip by the end of 2017. If they succeed, it would be an “incredibly exciting scientific achievement,” Bestwick says. Rigetti Computing is currently working on scaling up from 8-qubit chips.

But even that huge step forward in demonstrating the advantages of quantum computing would not result in a quantum computer that is a practical problem-solving tool. Many researchers believe that practical quantum computing requires systems to correct the quantum errors that can arise in fragile qubits. Error correction will almost certainly be necessary to achieve the future promise of 100-million-qubit systems that could perform tasks that are currently impractical, such as cracking modern cryptography keys.

Though it may seem like quantum computing demands far-off focus, Rigetti Computing is complementing its long-term strategy with a near-term strategy that can serve clients long before more capable quantum computers arise. The quantum computing cloud service is one example of that. The startup also believes a hybrid system that combines classical computing architecture with quantum computing chips can solve many practical problems in the short term, especially in the fields of machine learning and chemistry. What’s more, says Rigetti, such hybrid classical/quantum computers can perform well even without error correction.

“We’ve uncovered a whole new class of problems that can be solved by the hybrid model,” Bestwick says. “There is still a large role for classical computing to own the shell of the problem, but we can offload parts of the problem that the quantum computing resource can handle.”

There is another tall hurdle that must be overcome before we’ll be able to build the quantum computing future: There are not many people in the world qualified to build a full-stack quantum computer. But Rigetti Computing is focused on being a full-stack quantum computing company that’s attractive to talented researchers and engineers who want to work at a company that is trying to take this field beyond the academic lab to solve real-world problems.

Much of Rigetti’s strategy here revolves around its Junior Quantum Engineer Program, which helps recruit and train the next generation of quantum computing engineers. The program, says Thattai, selects some of the “best undergraduates in applied physics, engineering, and computer science” to learn how to build full-stack quantum computing in the most hands-on experience possible. It’s a way to ensure that the company continues to feed the talent pipeline for the future industry.

On the client side, Rigetti is not yet ready to name its main customers. But it did confirm that it has partnered with NASA to develop potential quantum computing applications. Venture capital firms seem impressed by the startup’s near-term and long-term strategies as well, given news earlier this year that Rigetti had raised $64 million in series A and B funding led by Andreessen Horowitz and Vy Capital.

Whether it’s clients or investors, Rigetti has sought out like-minded people who believe in the startup’s model of preparing for the quantum computing future beyond waiting on the hardware.

“Those people know that when the technology crosses the precipice of being beyond what classical computing can do, it will flip very, very quickly in one generation,” Thattai says. “The winners and losers in various industries will be decided by who took advantage of quantum computing systems early.”

Qudits: The Real Future of Quantum Computing?

Instead of creating quantum computers based on qubits that can each adopt only two possible options, scientists have now developed a microchip that can generate “qudits” that can each assume 10 or more states, potentially opening up a new way to creating incredibly powerful quantum computers, a new study finds.

Classical computers switch transistors either on or off to symbolize data as ones and zeroes. In contrast, quantum computers use quantum bits, or qubits that, because of the bizarre nature of quantum physics, can be in a state of superposition where they simultaneously act as both 1 and 0.

The superpositions that qubits can adopt let them each help perform two calculations at once. If two qubits are quantum-mechanically linked, or entangled, they can help perform four calculations simultaneously; three qubits, eight calculations; and so on. As a result, a quantum computer with 300 qubits could perform more calculations in an instant than there are atoms in the known universe, solving certain problems much faster than classical computers. However, superpositions are extraordinarily fragile, making it difficult to work with multiple qubits.

Most attempts at building practical quantum computers rely on particles that serve as qubits. However, scientists have long known that they could in principle use qudits with more than two states simultaneously. In principle, a quantum computer with two 32-state qudits, for example, would be able to perform as many operations as 10 qubits while skipping the challenges inherent with working with 10 qubits together.