Monthly Archives: February 2017

Righetti now 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.”

How to Bots Win Friends and Influence People

Every now and then sociologist Phil Howard writes messages to social media accounts accusing them of being bots. It’s like a Turing test of the state of online political propaganda. “Once in a while a human will come out and say, ‘I’m not a bot,’ and then we have a conversation,” he said at the European Conference for Science Journalists in Copenhagen on June 29.

In his academic writing, Howard calls bots “highly automated accounts.” By default, the accounts publish messages on Twitter, Facebook, or other social media sites at rates even a teenager couldn’t match. Human puppet-masters manage them, just like the Wizard of Oz, but with a wide variety of commercial aims and political repercussions. Howard and colleagues at theOxford Internet Institute in England published a working paper [PDF] last month examining the influence of these social media bots on politics in nine countries.

“Our goal is to produce large amounts of evidence, gathered systematically, so that we can make some safe, if not conservative, generalizations about where public life is going,” Howard says. The working paper, available ahead of peer-review in draft form, reports on countries with a mixture of different types of governments: Brazil, Canada, China, Germany, Poland, Russia, Taiwan, Ukraine, and the United States.

“My biggest surprise (maybe disappointment) is how it’s seemingly taken the 2016 U.S. election outcome to elevate the conversation and concerns related to this issue… because it’s not new,” says John F. Gray, co-founder ofMentionmapp, a social media analytics company in Vancouver, Canada. For years, bot companies have flooded protest movements’ hashtags with pro-government spam from Mexico [PDF] to Russia [PDF]. More sophisticated bots replicate real-life human networks and post or promote “fake news” and conspiracy theories seeking to sway voters. Indiana University researchers are building a taxonomy of social-network bots to simplify research (see “Taxonomy Goes Digital: Getting a Handle on Social Bots”, IEEE Spectrum, 9 June 2017).

Howard and colleagues have taken a social science approach: They found informants willing to provide access to programmers behind the botnets and have spent time with those programmers, getting to know their business models and motivations. One of their discoveries, Howard says, is that bot networks are, “not really bought and sold: they’re rented.” That’s because the older a profile is and the more varied its activity, the easier it is to evade detection by social networks’ security teams.

Private companies, not just governments and political parties, are major botnet users, Howard adds. The big business of renting botnets to influence public conversations may encourage firms to create ever-more realistic bots. The computation for spreading propaganda via bots, Howard says, isn’t that complicated. Instead, Gray says the sophistication of botnet design, their coordination, and how they manipulate social media has been “discouragingly impressive.”

Both Howard and Gray say they are pessimistic about the ability of regulations to keep up with the fast-changing social bot-verse. Howard and his team are instead trying to examine each country’s situation and in the working paper they call for social media firms to revise their designs to promote democracy.

Gray calls it a literacy problem. Humans must get better at evaluating the source of a message to help them decide how much to believe the message itself, he says.

Even Computer Users Can Now Access Quantum Computing Secret

You may not need a quantum computer of your own to securely use quantum computing in the future. For the first time, researchers have shown how even ordinary classical computer users could remotely access quantum computing resources online while keeping their quantum computations securely hidden from the quantum computer itself.

Tech giants such as Google and IBM are racing to build universal quantum computers that could someday analyze millions of possible solutions much faster than today’s most powerful classical supercomputers. Such companies have also begun offering online access to their early quantum processors as a glimpse of how anyone could tap the power of cloud-based quantum computing. Until recently, most researchers believed that there was no way for remote users to securely hide their quantum computations from prying eyes unless they too possessed quantum computers. That assumption is now being challenged by researchers in Singapore and Australia through a new paper published in the 11 July issue of the journal Physical Review X.

“Frankly, I think we are all quite surprised that this is possible,” says Joseph Fitzsimons, a theoretical physicist for the Centre for Quantum Technologies at the National University of Singapore and principal investigator on the study. “There had been a number of results showing that it was unlikely for a classical user to be able to hide [delegated quantum computations] perfectly, and I think many of us in the field had interpreted this as

In MOOC FutureLearn, Conversation Powers Learn Massive Scale

Personalized learning” is one of the hottest trends in education these days. The idea is to create software that tracks the progress of each student and then adapts the content, pace of instruction, and assessment to the individual’s performance. These systems succeed by providing immediate feedback that addresses the student’s misunderstandings and offers additional instruction and materials.

The Bill & Melinda Gates Foundation has reportedly spent more than US $300 million on personalized learning R&D, while the Chan Zuckerberg Initiative—the investment and philanthropic company created by Facebook CEO Mark Zuckerberg and his wife, Priscilla Chan—has also signalled its commitment to personalized learning (which Zuckerberg announced on Facebook, of course). Just last month, the two groups teamed up for the first time to jointly fund a $12 million program to promote personalized classroom instruction.

But personalized learning is hard to do. It requires breaking down a topic into its component parts in order to create different pathways through the material. It can be done, with difficulty, for well-structured and well-established topics, such as algebra and computer programming. But it really can’t be done for subjects that don’t form neat chunks, such as economics or psychology, nor for still-evolving areas, such as cybersecurity.

What’s more, this latest wave of personalized learning may have the unintended consequence of isolating students because it ignores the biggest advance in education of the past 50 years: learning through cooperation and conversation. It’s ironic that the inventor of the world’s leading social media platform is promoting education that’s the opposite of social.

Interestingly, one early proponent of personalized learning had a far more expansive view. In the 1960s, Gordon Pask, a deeply eccentric British scientist who pioneered the application of cybernetics to entertainment, architecture, and education, co-invented the first commercial adaptive teaching machine, which trained typists in keyboard skills and adjusted the training to their personal characteristics. A decade later, Pask extended personalized learning into a grand unified theory of learning as conversation.

For the layperson and even for a lot of experts, Pask’s Conversation Theory is impenetrable. But for those who manage to grasp it, it’s quite exciting. In essence, it explains how language-using systems, including people and artificial intelligences, can come to know things through well-structured conversation. He proposed that all human learning involves conversation. We converse with ourselves when we relate new experience to what we already know. We converse with teachers when we respond to their questions and they correct our misunderstandings. We converse with other learners to reach agreement.

This is more than an abstract theory of learning. It is a blueprint for designing educational technology. Pask himself developed teaching machines that conversed with students in a formalized language, represented as dynamic maps of interconnected concepts. He also introduced conversational teaching methods, such as Teachback, where the student explains to the teacher what has just been taught.

Pask’s theory still has relevance today. I know, because for the past four years, I’ve helped develop a new MOOC (Massive Open Online Course) platform based on his ideas. The platform is operated by FutureLearn, a company owned by The Open University, the UK’s 48-year-old public distance learning and research university.

As Academic Lead for FutureLearn, I was determined not to copy existing MOOC platforms, which primarily focus on delivering lectures at a distance. Instead, we designed FutureLearn for learning as conversation, and in such a way that learning would improve with scale, so that the more people who signed up, the better the learning experience would be.

Every course involves conversation as a core element. Each teaching step, whether video, text, or interactive exercise, has a flow of comments, questions, and replies from learners running alongside it. The steps make careful use of questions to prompt responses: What was the most important thing you learned from the video? Can you give an example from your own experience?

There are also dedicated discussions, in which learners reflect on the week’s activity, describe how they performed on assessments, or answer an open-ended question about the course. And online study groups allow learners to work together on a task and discuss their learning goals.

Even student assessment has a conversational component. Learners write short structured reviews of other students’ assignments, and in return they receive reviews of their assignments from their peers. Quizzes and tests are marked by computer, but the results come with pre-written responses from the educator.

When we began designing FutureLearn, previous research suggested that students don’t like to collaborate and converse online. Other online learning platforms that provide forums to discuss a course find these features are generally not well used. But that may be because these features are peripheral, whereas we put conversation at the heart of learning.

From the start, the conversations took off. In June 2015, the British Council ran the largest ever online MOOC, on preparing for the IELTS English language proficiency exam. Some 271,000 people joined the FutureLearn course, including many based in the Middle East and Asia. Just one video on that course attracted over 60,000 comments from learners. By then, we had realized that the scale of conversation needed to be tamed by using the social media techniques of liking and following. We also encouraged course facilitators to reply to the most-liked comments so that learners who were following the facilitators would see them.

We had expected to deal with abusive comments on courses like “Muslims in Britain” and “Climate Change.” That hasn’t happened, and we aren’t entirely sure why. The initial testers of FutureLearn were Open University alumni, so perhaps they modelled good practice. Comments are moderated to remove the occasional abusive remark, but most of the conversation streams are so overwhelmingly positive that dissenters get constructive responses rather than triggering flame wars.

To be clear, students aren’t required to take part in a discussion to complete a FutureLearn course, but the learning is definitely enriched when students read the responses of other learners and join in. On average, a third of learners on a FutureLearn course contribute comments and replies.

FutureLearn is now a worldwide MOOC platform, with more than six million total registrations. We’re continuing to consider new conversational features, such as reflective conversations where learners write and discuss annotations on the teaching material, and experiential learning where learners share their personal insights and experiences.

FutureLearn has taken the path of social learning and proven that it can work at scale. Going forward, the big challenge for FutureLearn and for educational technology in general will be to find ways of combining the individual pathways and adaptive content of personalized learning with the benefits of learning through conversation and collaboration.