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#438925 Nanophotonics Could Be the ‘Dark ...
The race to build the first practical quantum computers looks like a two-horse contest between machines built from superconducting qubits and those that use trapped ions. But new research suggests a third contender—machines based on optical technology—could sneak up on the inside.
The most advanced quantum computers today are the ones built by Google and IBM, which rely on superconducting circuits to generate the qubits that form the basis of quantum calculations. They are now able to string together tens of qubits, and while controversial, Google claims its machines have achieved quantum supremacy—the ability to carry out a computation beyond normal computers.
Recently this approach has been challenged by a wave of companies looking to use trapped ion qubits, which are more stable and less error-prone than superconducting ones. While these devices are less developed, engineering giant Honeywell has already released a machine with 10 qubits, which it says is more powerful than a machine made of a greater number of superconducting qubits.
But despite this progress, both of these approaches have some major drawbacks. They require specialized fabrication methods, incredibly precise control mechanisms, and they need to be cooled to close to absolute zero to protect the qubits from any outside interference.
That’s why researchers at Canadian quantum computing hardware and software startup Xanadu are backing an alternative quantum computing approach based on optics, which was long discounted as impractical. In a paper published last week in Nature, they unveiled the first fully programmable and scalable optical chip that can run quantum algorithms. Not only does the system run at room temperature, but the company says it could scale to millions of qubits.
The idea isn’t exactly new. As Chris Lee notes in Ars Technica, people have been experimenting with optical approaches to quantum computing for decades, because encoding information in photons’ quantum states and manipulating those states is relatively easy. The biggest problem was that optical circuits were very large and not readily programmable, which meant you had to build a new computer for every new problem you wanted to solve.
That started to change thanks to the growing maturity of photonic integrated circuits. While early experiments with optical computing involved complex table-top arrangements of lasers, lenses, and detectors, today it’s possible to buy silicon chips not dissimilar to electronic ones that feature hundreds of tiny optical components.
In recent years, the reliability and performance of these devices has improved dramatically, and they’re now regularly used by the telecommunications industry. Some companies believe they could be the future of artificial intelligence too.
This allowed the Xanadu researchers to design a silicon chip that implements a complex optical network made up of beam splitters, waveguides, and devices called interferometers that cause light sources to interact with each other.
The chip can generate and manipulate up to eight qubits, but unlike conventional qubits, which can simultaneously be in two states, these qubits can be in any configuration of three states, which means they can carry more information.
Once the light has travelled through the network, it is then fed out to cutting-edge photon-counting detectors that provide the result. This is one of the potential limitations of the system, because currently these detectors need to be cryogenically cooled, although the rest of the chip does not.
But most importantly, the chip is easily re-programmable, which allows it to tackle a variety of problems. The computation can be controlled by adjusting the settings of these interferometers, but the researchers have also developed a software platform that hides the physical complexity from users and allows them to program it using fairly conventional code.
The company announced that its chips were available on the cloud in September of 2020, but the Nature paper is the first peer-reviewed test of their system. The researchers verified that the computations being done were genuinely quantum mechanical in nature, but they also implemented two more practical algorithms: one for simulating molecules and the other for judging how similar two graphs are, which has applications in a variety of pattern recognition problems.
In an accompanying opinion piece, Ulrik Andersen from the Technical University of Denmark says the quality of the qubits needs to be improved considerably and photon losses reduced if the technology is ever to scale to practical problems. But, he says, this breakthrough suggests optical approaches “could turn out to be the dark horse of quantum computing.”
Image Credit: Shahadat Rahman on Unsplash Continue reading
Posted in Human Robots
#438807 Visible Touch: How Cameras Can Help ...
The dawn of the robot revolution is already here, and it is not the dystopian nightmare we imagined. Instead, it comes in the form of social robots: Autonomous robots in homes and schools, offices and public spaces, able to interact with humans and other robots in a socially acceptable, human-perceptible way to resolve tasks related to core human needs.
To design social robots that “understand” humans, robotics scientists are delving into the psychology of human communication. Researchers from Cornell University posit that embedding the sense of touch in social robots could teach them to detect physical interactions and gestures. They describe a way of doing so by relying not on touch but on vision.
A USB camera inside the robot captures shadows of hand gestures on the robot’s surface and classifies them with machine-learning software. They call this method ShadowSense, which they define as a modality between vision and touch, bringing “the high resolution and low cost of vision-sensing to the close-up sensory experience of touch.”
Touch-sensing in social or interactive robots is usually achieved with force sensors or capacitive sensors, says study co-author Guy Hoffman of the Sibley School of Mechanical and Aerospace Engineering at Cornell University. The drawback to his group’s approach has been that, even to achieve coarse spatial resolution, many sensors are needed in a small area.
However, working with non-rigid, inflatable robots, Hoffman and his co-researchers installed a consumer-grade USB camera to which they attached a fisheye lens for a wider field of vision.
“Given that the robot is already hollow, and has a soft and translucent skin, we could do touch interaction by looking at the shadows created by people touching the robot,” says Hoffman. They used deep neural networks to interpret the shadows. “And we were able to do it with very high accuracy,” he says. The robot was able to interpret six different gestures, including one- or two-handed touch, pointing, hugging and punching, with an accuracy of 87.5 to 96 percent, depending on the lighting.
This is not the first time that computer vision has been used for tactile sensing, though the scale and application of ShadowSense is unique. “Photography has been used for touch mainly in robotic grasping,” says Hoffman. By contrast, Hoffman and collaborators wanted to develop a sense that could be “felt” across the whole of the device.
The potential applications for ShadowSense include mobile robot guidance using touch, and interactive screens on soft robots. A third concerns privacy, especially in home-based social robots. “We have another paper currently under review that looks specifically at the ability to detect gestures that are further away [from the robot’s skin],” says Hoffman. This way, users would be able to cover their robot’s camera with a translucent material and still allow it to interpret actions and gestures from shadows. Thus, even though it’s prevented from capturing a high-resolution image of the user or their surrounding environment, using the right kind of training datasets, the robot can continue to monitor some kinds of non-tactile activities.
In its current iteration, Hoffman says, ShadowSense doesn’t do well in low-light conditions. Environmental noise, or shadows from surrounding objects, also interfere with image classification. Relying on one camera also means a single point of failure. “I think if this were to become a commercial product, we would probably [have to] work a little bit better on image detection,” says Hoffman.
As it was, the researchers used transfer learning—reusing a pre-trained deep-learning model in a new problem—for image analysis. “One of the problems with multi-layered neural networks is that you need a lot of training data to make accurate predictions,” says Hoffman. “Obviously, we don’t have millions of examples of people touching a hollow, inflatable robot. But we can use pre-trained networks trained on general images, which we have billions of, and we only retrain the last layers of the network using our own dataset.” Continue reading
Posted in Human Robots