Monthly Archives: May 2017

Careers at Emerging Technologies

For this ComputingEdge issue, we focus on emerging technologies as they relate to an increasing popular career transition for computing professionals—the shift from industry to academia. Prior to obtaining a full professorship in information systems at California State University Fullerton, Sorel Reisman held senior management positions at IBM, Toshiba, and EMI in the US and Canada. He served as 2011 IEEE Computer Society president and is currently a member of both the IEEE Publications Services and Products Board, as well as the IEEE Education Activities Board.
ComputingEdge: You spent considerable time in industry working for multinational companies, starting as an engineer and rising to vice president of development. Why did you leave for academia?
Reisman: When I finished graduate school, I fully intended to pursue an academic career. But academic positions were in short supply at the time, so I looked for a job in industry. And once there, I got caught up in the dynamic of raises and promotions. However, work in industry proved unstable, and unsuccessful company campaigns and projects encouraged me to change jobs several times.
Eventually, a friend who was a physics professor told me about a tenure-track position in Cal State Fullerton’s business school. Having had enough of the insecurity of everything related to the computer industry, I applied for and was hired as an associate professor. Seven years later, they university gave me tenure and promoted me to full professor.
ComputingEdge: What are the opportunities in academia for pursuing an interest in emerging technologies?
Reisman: An academic can pursue an interest in emerging technologies in three areas, which align with the three criteria used to assess a professor’s work performance: research, teaching, and service.
When I entered academia, I didn’t have a specific research agenda. However, I’d been involved with multimedia computing in industry, and it seemed reasonable to work in that area again as a professor. How much real research you can do in emerging technologies depends on the funding available for that kind of work. In industry, companies typically fund their own projects. In academia, on the other hand, external grants typically fund research. Your success in obtaining a grant determines your ability to pursue your interest in emerging technologies.
In terms of instruction, undergraduate courses are limited in opportunities to teach emerging technologies, but there’s more freedom to do this with graduate level courses. At the undergraduate level, accredited academic departments must adhere to a prescribed set of topics to provide students with foundational knowledge. You can introduced newer, advanced topics in some courses, but you don’t have time to deal with them in depth.
You can do as I did and propose optional courses related to an emerging technology, but there might not be enough interested students to justify the university offering the class. I created a course called “personal computer systems and architectures” that became very popular, just as PCs were being widely adopted. I also designed and a class on e-commerce systems—which was just starting to become an emerging topic of interest—that was popular with graduate students.
Service means volunteer personal or professional community work. I chose to invest my service time with the IEEE Computer Society, which has paid off immeasurably. I’ve learned about many new technologies, and my involvement with the IEEE and Computer Society digital libraries has enabled me to bring new ideas and concepts to the work I do it at the university.
ComputingEdge: Can you describe one or two major differences between working in industry versus working in an academic environment
Reisman: The environments are completely different. For example, they don’t work with the same decision-making time frames. Industry tends to make decisions and act on them much more quickly than academia.
Also, in industry, you usually work on common objectives as a team with others. As a professor, you typically don’t work in teams, and your objectives may be completely different from those of your colleagues.
ComputingEdge: Are you glad to have moved from industry to academia?
Reisman: Absolutely! Probably the best thing about academia is the freedom to pursue your own interests, whether personal or professional. Also, unlike industry, whose projects go on for a long time, academic work is divided into shorter segments: semesters. If you’re teaching a class you don’t like, it ends with the semester. And you can change courses over time to make the experience better. In addition, breaks between semesters and school years enable you to recharge your batteries.
ComputingEdge: What important advice would you give colleagues considering moving from industry to academia?
Reisman: Don’t assume that because you held senior management positions in industry that your academic colleagues will value your industry accomplishments. The kinds of skills that helped you achieve those positions are mostly irrelevant in academia, unless you take an administrative job.

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.

Problems With Current Ticketing Systems

Ticketing systems (or issue tracking systems) are a convenient way to help your customers with tough problems, and help your development team find and address bugs faster. For example, you may use an email ticketing system to automatically notify your team when a user submits a potential issue; from there, you can have an individual address the issue, and mark it as resolved in a central database, along with notes on what they fixed (if they fixed anything) and how.
However, like all modern technologies, current ticketing systems aren’t perfect and can cause headaches if you aren’t prepared for their potential downsides.
Biggest Problems With Modern Ticketing Systems
These are some of the most common issues that development teams and customer service representatives face:

Documenting the ticket flow. Let’s say you have a new issue tracking system in place, and it automatically notifies everyone on your development team when there’s a ticket. What happens then? Is someone supposed to log into the platform and claim the issue as their own? Should there be a discussion over chat? If your ticket flow process isn’t clear, you’ll likely end up duplicating efforts or you’ll have a host of unresolved tickets that never see any further action. To this end, you’ll need to create and document a standard operating procedure that everyone can follow. Documentation is important because it gives all members of the team a consistent resource to reference; that way, if there’s ever an argument or discrepancy, you can check the document for clarity. It’s also useful for training purposes.

Bad UI. Some ticketing systems’ user interfaces (UI) are downright abysmal. Once logged in, you’re left in a dashboard with dozens of unclear options, and no intuitive tools to tell you what to do next or how to do it. Obviously, you’ll need to train your employees on how to use the system the way you intend them to, but overall, it should be fairly intuitive. If nothing else, the system should be customizable enough for you to remove some of the features that you don’t immediately need, and/or add some of the features that aren’t already present.

Poor descriptions from customers. Most issue tracking systems only do the grunt work of bringing you the issues that customers are inventing—and sometimes, customers aren’t articulate or specific about what they’re noticing. If you want your issue tracking system to be more efficient, and worthwhile for your employees to use, you’ll need to prompt your customers for more specific information, and give your developers tools they can use to deal with tickets that don’t immediately make sense.

Inconsistent training. Another problem with ticketing systems comes into play when you have too many team members working on the same platform—and some newbies thrown into the mix. Different people will likely have different preferences and different intuitive drives, and on top of that, they’ll have different styles of training. Some might leave detailed notes with their tickets while others leave none at all. There are many feasibly effective approaches to ticket management, but you need to be consistent if you want yours to work—and that consistency can only come from consistent training.

Feedback holes. Do you have a plan in place to collect feedback from customers submitting issues? Are you listening tofeedback from your team? Chances are, your issue tracking management won’t be perfect on the first go; you’ll need to carefully and attentively listen to your customers and employees alike if you want to find the holes and patch them with alternative workflows and ongoing changes. You can do this by creating anonymous feedback submission forms, or simply by having open conversations with your team members. Don’t continue using a platform that continues to cause headaches for your team.

Is the Problem With Ticketing Systems? 
As you’ve undoubtedly noticed, the majority of the problems listed above aren’t inherent to ticketing systems; instead, they’re flaws in the way that companies implement and use ticketing systems. It’s important for you to take your time considering different issue tracking systems, and choose the best option for your team, but beyond that, you need to understand that no ticket system will be effective on its own. You need to have the right people and processes in place to make the most of that system, or its benefits will be minimal.

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 evidence that nothing useful could be hidden.”

The technique for helping classical computer users hide their quantum computations relies upon a particular approach known as measurement-based quantum computing. Quantum computing’s main promise relies upon leveraging quantum bits (qubits) of information that can exist as both 1s and 0s simultaneously—unlike classical computing bits that exist as either 1 or 0. That means qubits can simultaneously represent and process many more states of information than classical computing bits.

In measurement-based quantum computing, a quantum computer puts all its qubits into a particular state of quantum entanglement so that any changes to a single qubit affect all the qubits. Next, qubits are individually measured one by one in a certain order that specifies the program being run on the quantum computer. A remote user can provide step-by-step instructions for each qubit’s measurement that encode both the input data and the program being run. Crucially, each measurement depends on the outcome of previous measurements.

Fitzsimons and his colleagues figured out how to exploit this step-wise approach to quantum computing and achieve a new form of “blind quantum computation” security. They showed how remote users relying on classical computers can hide the meaning behind each step of the measurement sequence from the quantum computer performing the computation. That means the owner of the quantum computer cannot tell the role of each measurement step and which qubits were used for inputs, operations, or outputs.

The finding runs counter to previous assumptions that it was impossible to guarantee data privacy for users relying on ordinary classical computers to remotely access quantum computers. But Fitzsimons says that early feedback to the group’s work has been “very positive” because the proposed security mechanism—described as the “flow ambiguity effect”—is fairly straightforward.