System Bits: Dec. 19

Controlling qubits; quantum interactions; AI for underwater vehicles.

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Controlling qubits for quantum computing
In a major step toward making a quantum computer using everyday materials, a team led by researchers at Princeton University has reported they’ve constructed a key piece of silicon hardware capable of controlling quantum behavior between two electrons with extremely high precision.

The team said they have constructed a gate that controls interactions between the electrons in a way that allows them to act as the quantum bits of information, or qubits, necessary for quantum computing. The demonstration of this nearly error-free, two-qubit gate is an important early step in building a more complex quantum computing device from silicon, the same material used in conventional computers and smartphones.

Jason Petta, a professor of physics at Princeton University said, “We knew we needed to get this experiment to work if silicon-based technology was going to have a future in terms of scaling up and building a quantum computer. The creation of this high-fidelity two-qubit gate opens the door to larger scale experiments.”

A team led by Princeton University researchers has created an essential component for making quantum computers from an everyday material, silicon. The researchers demonstrated the ability to control the behavior of two silicon-based quantum bits, or qubits, paving the way for making complex, multi-qubit devices using technology that is less expensive and easier to manufacture than other approaches.
Source: Princeton University

It is believed that silicon-based devices are likely to be less expensive and easier to manufacture than other technologies for achieving a quantum computer, and although other research groups and companies have announced quantum devices containing 50 or more qubits, those systems require exotic materials such as superconductors or charged atoms held in place by lasers, the researchers pointed out.

In general, quantum computers can solve problems that are inaccessible with conventional computers, and the devices may be able to factor extremely large numbers or find the optimal solutions for complex problems. They could also help researchers understand the physical properties of extremely small particles such as atoms and molecules, leading to advances in areas such as materials science and drug discovery.

The two-qubit silicon-based gate consists of two electrons (blue balls with arrows) in a layer of silicon (Si). By applying voltages through aluminum oxide (Al2O3)wires (red and green), the researchers trapped the electrons and coaxed quantum behaviors that transform their spin properties into quantum bits of information, or qubits. The image on the left shows a scanning electron micrograph of the device, which is about 200 nanometers (nm) across. The image on the right is a diagram of the device from the side.
Source: Princeton University

Building a quantum computer requires researchers to create qubits and couple them to each other with high fidelity. Silicon-based quantum devices use a quantum property of electrons called “spin” to encode information. The spin can point either up or down in a manner analogous to the north and south poles of a magnet. In contrast, conventional computers work by manipulating the electron’s negative charge.

Achieving a high-performance, spin-based quantum device has been hampered by the fragility of spin states — they readily flip from up to down or vice versa unless they can be isolated in a very pure environment. By building the silicon quantum devices in Princeton’s Quantum Device Nanofabrication Laboratory, the researchers were able to keep the spins coherent — that is, in their quantum states — for relatively long periods of time.

Additional researchers on the team included Princeton graduate student Felix Borjans; Princeton associate research scholar Anthony Sigillito. The team included input on the theory aspects of the work by Jacob Taylor, a professor at the Joint Quantum Institute and Joint Center for Quantum Information and Computer Science at the National Institute of Standards and Technology and the University of Maryland, and Maximilian Russ and Guido Burkard at the University of Konstanz in Germany.

Quantum CMOS chip
University of New South Wales researchers have shown how a quantum computer can be manufactured using mostly standard silicon technology.

Artist’s impression of a silicon CMOS architecture for a spin-based quantum computer.
Source: University of New South Wales

Indeed, research teams all over the world are exploring different ways to design a working computing chip that can integrate quantum interactions. Now, these UNSW engineers believe they have cracked the problem, reimagining the silicon microprocessors we know to create a complete design for a quantum computer chip that can be manufactured using mostly standard industry processes and components.

The quantum chip design has a novel architecture that allows quantum calculations to be performed using existing CMOS semiconductor components, and was devised by Andrew Dzurak, director of the Australian National Fabrication Facility at the University of New South Wales (UNSW), and Menno Veldhorst, lead author of the paper who was a research fellow at UNSW when the conceptual work was done.

Dzurak, who is also a Program Leader at Australia’s Centre of Excellence for Quantum Computation and Communication Technology (CQC2T) said, “With quantum computing, we are on the verge of another technological leap that could be as deep and transformative. But a complete engineering design to realize this on a single chip has been elusive. I think what we have developed at UNSW now makes that possible. And most importantly, it can be made in a modern semiconductor manufacturing plant,” he added.

Veldhorst, now a team leader in quantum technology at QuTech – a collaboration between Delft University of Technology and TNO, the Netherlands Organisation for Applied Scientific Research – said the power of the new design is that, for the first time, it charts a conceivable engineering pathway toward creating millions of quantum bits, or qubits. “Remarkable as they are, today’s computer chips cannot harness the quantum effects needed to solve the really important problems that quantum computers will. To solve problems that address major global challenges – like climate change or complex diseases like cancer – it’s generally accepted we will need millions of qubits working in tandem. To do that, we will need to pack qubits together and integrate them, like we do with modern microprocessor chips. That’s what this new design aims to achieve.”

There are at least five major quantum computing approaches being explored worldwide: silicon spin qubits, ion traps, superconducting loops, diamond vacancies and topological qubits; UNSW’s design is based on silicon spin qubits. The main problem with all of these approaches is that there is no clear pathway to scaling the number of quantum bits up to the millions needed without the computer becoming huge a system requiring bulky supporting equipment and costly infrastructure.

That’s why UNSW’s new design is so exciting: relying on its silicon spin qubit approach – which already mimics much of the solid-state devices in silicon that are the heart of the US$380 billion global semiconductor industry – it shows how to dovetail spin qubit error correcting code into existing chip designs, enabling true universal quantum computation.

Unlike almost every other major group elsewhere, CQC2T’s quantum computing effort is obsessively focused on creating solid-state devices in silicon, from which all of the world’s computer chips are made. And they’re not just creating ornate designs to show off how many qubits can be packed together, but aiming to build qubits that could one day be easily fabricated – and scaled up.

AI unlocks marine mysteries
According to MIT researchers, each year the melting of the Charles River serves as a harbinger for warmer weather. Shortly thereafter is the return of budding trees, longer days, and flip-flops. For students of class 2.680 (Unmanned Marine Vehicle Autonomy, Sensing and Communications), the newly thawed river means it’s time to put months of hard work into practice, they said.

Aquatic environments like the Charles present challenges for robots because of the severely limited communication capabilities. “In underwater marine robotics, there is a unique need for artificial intelligence — it’s crucial,” says MIT Professor Henrik Schmidt, the course’s co-instructor. “And that is what we focus on in this class.”

Class 2.680 (Unmanned Marine Vehicle Autonomy, Sensing and Communications), which is offered during spring semester, is structured around the presence of ice on the Charles. While the river is covered by a thick sheet of ice in February and into March, students are taught to code and program a remotely-piloted marine vehicle for a given mission. 
Source: MIT

The class, which is offered during spring semester, is structured around the presence of ice on the Charles. While the river is covered by a thick sheet of ice in February and into March, students are taught to code and program a remotely-piloted marine vehicle for a given mission. Students program with MOOS-IvP, an autonomy software used widely for industry and naval applications.

“They’re not working with a toy,” says Schmidt’s co-instructor, Research Scientist Michael Benjamin. “We feel it’s important that they learn how to extend the software — write their own sensor processing models and AI behavior. And then we set them loose on the Charles.”

As the students learn basic programming and software skills, they also develop a deeper understanding of ocean engineering. “The way I look at it, we are trying to clone the oceanographer and put our understanding of how the ocean works into the robot,” Schmidt adds. This means students learn the specifics of ocean environments — studying topics like oceanography or underwater acoustics. 

Students develop code for several missions they will conduct on the Charles River by the end of the semester. These missions include finding hazardous objects in the water, receiving simulated temperature and acoustic data along the river, and communicating with other vehicles.

Augmenting robotic marine vehicles with artificial intelligence is useful in a number of fields. It can help researchers gather data on temperature changes in our ocean, inform strategies to reverse global warming, traverse the 95 percent of our oceans that has yet to be explored, map seabeds, and further our understanding of oceanography.

Students in 2.680 use their newly acquired coding skills to build such systems. Come spring, armed with the software they’ve spent months working on and a better understanding of ocean environments, they enter the MIT Sailing Pavilion prepared to test their artificial intelligence coding skills on the recently melted Charles River.

As marine vehicles glide along the Charles, executing missions based on the coding students have spent the better part of a semester perfecting, the mood is often one of exhilaration. “I’ve had students have big emotions when they see a bit of AI that they’ve created,” Benjamin recalls. “I’ve seen people call their parents from the dock.”

For this artificial intelligence to be effective in the water, students need to combine software skills with ocean engineering expertise. Schmidt and Benjamin have structured 2.680 to ensure students have a working knowledge of these twin pillars of robotic marine vehicle autonomy.

By combining these two research areas in their own research, Schmidt and Benjamin hope to create underwater robots that can go places humans simply cannot. “There are a lot of applications for better understanding and exploring our ocean if we can do it smartly with robots,” Benjamin added.



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