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#439628 How a Simple Crystal Could Help Pave the ...
Vaccine and drug development, artificial intelligence, transport and logistics, climate science—these are all areas that stand to be transformed by the development of a full-scale quantum computer. And there has been explosive growth in quantum computing investment over the past decade.
Yet current quantum processors are relatively small in scale, with fewer than 100 qubits— the basic building blocks of a quantum computer. Bits are the smallest unit of information in computing, and the term qubits stems from “quantum bits.”
While early quantum processors have been crucial for demonstrating the potential of quantum computing, realizing globally significant applications will likely require processors with upwards of a million qubits.
Our new research tackles a core problem at the heart of scaling up quantum computers: how do we go from controlling just a few qubits, to controlling millions? In research published today in Science Advances, we reveal a new technology that may offer a solution.
What Exactly Is a Quantum Computer?
Quantum computers use qubits to hold and process quantum information. Unlike the bits of information in classical computers, qubits make use of the quantum properties of nature, known as “superposition” and “entanglement,” to perform some calculations much faster than their classical counterparts.
Unlike a classical bit, which is represented by either 0 or 1, a qubit can exist in two states (that is, 0 and 1) at the same time. This is what we refer to as a superposition state.
Demonstrations by Google and others have shown even current, early-stage quantum computers can outperform the most powerful supercomputers on the planet for a highly specialized (albeit not particularly useful) task—reaching a milestone we call quantum supremacy.
Google’s quantum computer, built from superconducting electrical circuits, had just 53 qubits and was cooled to a temperature close to -273℃ in a high-tech refrigerator. This extreme temperature is needed to remove heat, which can introduce errors to the fragile qubits. While such demonstrations are important, the challenge now is to build quantum processors with many more qubits.
Major efforts are underway at UNSW Sydney to make quantum computers from the same material used in everyday computer chips: silicon. A conventional silicon chip is thumbnail-sized and packs in several billion bits, so the prospect of using this technology to build a quantum computer is compelling.
The Control Problem
In silicon quantum processors, information is stored in individual electrons, which are trapped beneath small electrodes at the chip’s surface. Specifically, the qubit is coded into the electron’s spin. It can be pictured as a small compass inside the electron. The needle of the compass can point north or south, which represents the 0 and 1 states.
To set a qubit in a superposition state (both 0 and 1), an operation that occurs in all quantum computations, a control signal must be directed to the desired qubit. For qubits in silicon, this control signal is in the form of a microwave field, much like the ones used to carry phone calls over a 5G network. The microwaves interact with the electron and cause its spin (compass needle) to rotate.
Currently, each qubit requires its own microwave control field. It is delivered to the quantum chip through a cable running from room temperature down to the bottom of the refrigerator at close to -273 degrees Celsius. Each cable brings heat with it, which must be removed before it reaches the quantum processor.
At around 50 qubits, which is state-of-the-art today, this is difficult but manageable. Current refrigerator technology can cope with the cable heat load. However, it represents a huge hurdle if we’re to use systems with a million qubits or more.
The Solution Is ‘Global’ Control
An elegant solution to the challenge of how to deliver control signals to millions of spin qubits was proposed in the late 1990s. The idea of “global control” was simple: broadcast a single microwave control field across the entire quantum processor.
Voltage pulses can be applied locally to qubit electrodes to make the individual qubits interact with the global field (and produce superposition states).
It’s much easier to generate such voltage pulses on-chip than it is to generate multiple microwave fields. The solution requires only a single control cable and removes obtrusive on-chip microwave control circuitry.
For more than two decades global control in quantum computers remained an idea. Researchers could not devise a suitable technology that could be integrated with a quantum chip and generate microwave fields at suitably low powers.
In our work we show that a component known as a dielectric resonator could finally allow this. The dielectric resonator is a small, transparent crystal which traps microwaves for a short period of time.
The trapping of microwaves, a phenomenon known as resonance, allows them to interact with the spin qubits longer and greatly reduces the power of microwaves needed to generate the control field. This was vital to operating the technology inside the refrigerator.
In our experiment, we used the dielectric resonator to generate a control field over an area that could contain up to four million qubits. The quantum chip used in this demonstration was a device with two qubits. We were able to show the microwaves produced by the crystal could flip the spin state of each one.
The Path to a Full-Scale Quantum Computer
There is still work to be done before this technology is up to the task of controlling a million qubits. For our study, we managed to flip the state of the qubits, but not yet produce arbitrary superposition states.
Experiments are ongoing to demonstrate this critical capability. We’ll also need to further study the impact of the dielectric resonator on other aspects of the quantum processor.
That said, we believe these engineering challenges will ultimately be surmountable— clearing one of the greatest hurdles to realizing a large-scale spin-based quantum computer.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Image Credit: Serwan Asaad/UNSW, Author provided Continue reading
Posted in Human Robots
#439252 The Cheetah’s Fluffy Tail Points ...
Almost but not quite a decade ago, researchers from UC Berkeley equipped a little robotic car with an actuated metal rod with a weight on the end and used it to show how lizards use their tails to stabilize themselves while jumping through the air. That research inspired a whole bunch of other tailed mobile robots, including a couple of nifty ones from Amir Patel at the University of Cape Town.
The robotic tails that we’ve seen are generally actuated inertial tails: a moving mass that goes one way causes the robot that it’s attached to to go the other way. This is how lizard tails work, and it’s a totally fine way to do things. In fact, people generally figured that many if not most other animals that use their tails to improve their agility leverage this inertial principle, including (most famously) the cheetah. But at least as far as the cheetah was concerned, nobody had actually bothered to check, until Patel took the tails from a collection of ex-cheetahs and showed that in fact cheetah tails are almost entirely fluff. So if it’s not the mass of its tail that helps a cheetah chase down prey, then it must be the aerodynamics.
The internet is full of wisdom on cheetah tails, and most of it describes “heavy” tails that “act as a counterbalance” to the rest of the cheetah’s body. This makes intuitive sense, but it’s also quite wrong, as Amir Patel figured out:
The aerodynamics of cheetah tails are super important, and actually something I discovered by accident! Towards the end of my PhD I was invited to a cheetah autopsy at the National Zoological Gardens here in South Africa. The idea was to weigh and measure the inertia of the cheetah tail because no such data existed. Based on what I’d seen in wildlife documentaries (and speaking to any game ranger in South Africa), the cheetah tail is often considered to be heavy, and used as a counterweight.
However, once we removed the fur and skin from the tail during the autopsy, it was surprisingly skinny! We measured it (and the tails of another 6 cheetahs) as being only about 2 percent of the body mass—much lower than my own robotic tails. But the fur made up a significant volume of the tail. So, I figured that there must be something to it: maybe the fur was making the tail appear like a larger object aerodynamically, without the weight penalty of an inertial tail.
A few years ago, Patel started to characterize tail aerodynamics in partnership with Aaron Johnson’s lab at CMU, and that work has lead to a recent paper published in IEEE Transactions on Robotics, exploring how aerodynamic drag on a lightweight tail can help robots perform dynamic behaviors more successfully.
The specific tail design that Minitaur is sporting in the video above doesn’t look particularly cheetah-like, being made out of carbon fiber and polyethylene film rather than floof, and only sporting an aerodynamic component at the end of the tail rather than tip to butt. This is explained by cheetahs in the wild not having easy access to either carbon fiber or polyethylene, and by a design that the researchers optimized to maximize drag while minimizing mass rather than for biomimicry. “We experimented with a whole array of furry tails to mimic cheetah fur, but found that the half cylinder shape had by far the most drag,” first author Joseph Norby told us in an email. “And the reduction of the drag component to just the end of the tail was a balance of effectiveness and rigidity—we could have made the drag component cover the entire length, but really the section near the tip produces most of the drag, and reducing the length of the drag component helps maintain the shape of the tail.”
Aerodynamic tails are potentially appealing because unlike inertial tails, the amount of torque that they can produce doesn't depend on how much they weigh, but rather with the velocity at which the robot is moving: the faster the robot goes, the more torque an aerodynamic tail can produce. We see this in animals, too, with fluffy tails commonly found on fast movers and jumpers like jerboas and flying squirrels. This offers some suggestion about what kind of robots could benefit most from tails like these, although as Norby points out, the greatest limitation of these tails is the large workspace required for the tail to move around safely.
Image: Norby et al
A variety of animals (and one robot) with aerodynamic drag tails, including a jerboa and giant Indian squirrel.
While this paper is focused on quantifying the effects of aerodynamic drag on robotic tails, it seems like there’s a lot of potential for some really creative designs—we were wondering about tails with adjustable floofitude, for example, and we asked Norby about some ways in which this research might be extended.
I think a foldable or retractable tail would greatly improve practicality by reducing the workspace when the tail is not needed. Essentially all of the animals we studied had some sort of flexibility to their tails, which I believe is a crucial property for improving both practicality and durability. In a similar vein, we've also thought about employing active or passive designs that could quickly modify the drag coefficient, whether by furling and unfurling, or simply rotating an asymmetric tail like our half cylinder. This could perhaps allow new forms of control similar to paddling and feathering a canoe: increasing drag when moving in one direction and reducing drag in the other could allow for more net control authority. This would be completely impossible with an inertial tail, which cannot do work on the environment.
Photo: Evan Ackerman/IEEE Spectrum
Gratuitous cheetah picture.
Even though animals had the idea for lightweight aerodynamic drag tails first, there’s no reason why we need to restrict ourselves to animal-like form factors when leveraging the advantages that tails like these offer, or indeed with the designs of the tails themselves. Without a mass penalty to worry about, why not put tails on any robot that has trouble keeping its balance, like pretty much every bipedal robot, right? Of course there are plenty of reasons not to do this, but still, it’s exciting to see this whole design space of aerodynamic drag tails potentially open up for any robot platform that needs a little bit of help with dynamic motion.
Enabling Dynamic Behaviors With Aerodynamic Drag in Lightweight Tails, by Joseph Norby, Jun Yang Li, Cameron Selby, Amir Patel, and Aaron M. Johnson from CMU and the University of Cape Town is published in IEEE Transactions on Robotics. Continue reading
Posted in Human Robots
#439110 Robotic Exoskeletons Could One Day Walk ...
Engineers, using artificial intelligence and wearable cameras, now aim to help robotic exoskeletons walk by themselves.
Increasingly, researchers around the world are developing lower-body exoskeletons to help people walk. These are essentially walking robots users can strap to their legs to help them move.
One problem with such exoskeletons: They often depend on manual controls to switch from one mode of locomotion to another, such as from sitting to standing, or standing to walking, or walking on the ground to walking up or down stairs. Relying on joysticks or smartphone apps every time you want to switch the way you want to move can prove awkward and mentally taxing, says Brokoslaw Laschowski, a robotics researcher at the University of Waterloo in Canada.
Scientists are working on automated ways to help exoskeletons recognize when to switch locomotion modes — for instance, using sensors attached to legs that can detect bioelectric signals sent from your brain to your muscles telling them to move. However, this approach comes with a number of challenges, such as how how skin conductivity can change as a person’s skin gets sweatier or dries off.
Now several research groups are experimenting with a new approach: fitting exoskeleton users with wearable cameras to provide the machines with vision data that will let them operate autonomously. Artificial intelligence (AI) software can analyze this data to recognize stairs, doors, and other features of the surrounding environment and calculate how best to respond.
Laschowski leads the ExoNet project, the first open-source database of high-resolution wearable camera images of human locomotion scenarios. It holds more than 5.6 million images of indoor and outdoor real-world walking environments. The team used this data to train deep-learning algorithms; their convolutional neural networks can already automatically recognize different walking environments with 73 percent accuracy “despite the large variance in different surfaces and objects sensed by the wearable camera,” Laschowski notes.
According to Laschowski, a potential limitation of their work their reliance on conventional 2-D images, whereas depth cameras could also capture potentially useful distance data. He and his collaborators ultimately chose not to rely on depth cameras for a number of reasons, including the fact that the accuracy of depth measurements typically degrades in outdoor lighting and with increasing distance, he says.
In similar work, researchers in North Carolina had volunteers with cameras either mounted on their eyeglasses or strapped onto their knees walk through a variety of indoor and outdoor settings to capture the kind of image data exoskeletons might use to see the world around them. The aim? “To automate motion,” says Edgar Lobaton an electrical engineering researcher at North Carolina State University. He says they are focusing on how AI software might reduce uncertainty due to factors such as motion blur or overexposed images “to ensure safe operation. We want to ensure that we can really rely on the vision and AI portion before integrating it into the hardware.”
In the future, Laschowski and his colleagues will focus on improving the accuracy of their environmental analysis software with low computational and memory storage requirements, which are important for onboard, real-time operations on robotic exoskeletons. Lobaton and his team also seek to account for uncertainty introduced into their visual systems by movements .
Ultimately, the ExoNet researchers want to explore how AI software can transmit commands to exoskeletons so they can perform tasks such as climbing stairs or avoiding obstacles based on a system’s analysis of a user's current movements and the upcoming terrain. With autonomous cars as inspiration, they are seeking to develop autonomous exoskeletons that can handle the walking task without human input, Laschowski says.
However, Laschowski adds, “User safety is of the utmost importance, especially considering that we're working with individuals with mobility impairments,” resulting perhaps from advanced age or physical disabilities.
“The exoskeleton user will always have the ability to override the system should the classification algorithm or controller make a wrong decision.” Continue reading
Posted in Human Robots
#439105 This Robot Taught Itself to Walk in a ...
Recently, in a Berkeley lab, a robot called Cassie taught itself to walk, a little like a toddler might. Through trial and error, it learned to move in a simulated world. Then its handlers sent it strolling through a minefield of real-world tests to see how it’d fare.
And, as it turns out, it fared pretty damn well. With no further fine-tuning, the robot—which is basically just a pair of legs—was able to walk in all directions, squat down while walking, right itself when pushed off balance, and adjust to different kinds of surfaces.
It’s the first time a machine learning approach known as reinforcement learning has been so successfully applied in two-legged robots.
This likely isn’t the first robot video you’ve seen, nor the most polished.
For years, the internet has been enthralled by videos of robots doing far more than walking and regaining their balance. All that is table stakes these days. Boston Dynamics, the heavyweight champ of robot videos, regularly releases mind-blowing footage of robots doing parkour, back flips, and complex dance routines. At times, it can seem the world of iRobot is just around the corner.
This sense of awe is well-earned. Boston Dynamics is one of the world’s top makers of advanced robots.
But they still have to meticulously hand program and choreograph the movements of the robots in their videos. This is a powerful approach, and the Boston Dynamics team has done incredible things with it.
In real-world situations, however, robots need to be robust and resilient. They need to regularly deal with the unexpected, and no amount of choreography will do. Which is how, it’s hoped, machine learning can help.
Reinforcement learning has been most famously exploited by Alphabet’s DeepMind to train algorithms that thrash humans at some the most difficult games. Simplistically, it’s modeled on the way we learn. Touch the stove, get burned, don’t touch the damn thing again; say please, get a jelly bean, politely ask for another.
In Cassie’s case, the Berkeley team used reinforcement learning to train an algorithm to walk in a simulation. It’s not the first AI to learn to walk in this manner. But going from simulation to the real world doesn’t always translate.
Subtle differences between the two can (literally) trip up a fledgling robot as it tries out its sim skills for the first time.
To overcome this challenge, the researchers used two simulations instead of one. The first simulation, an open source training environment called MuJoCo, was where the algorithm drew upon a large library of possible movements and, through trial and error, learned to apply them. The second simulation, called Matlab SimMechanics, served as a low-stakes testing ground that more precisely matched real-world conditions.
Once the algorithm was good enough, it graduated to Cassie.
And amazingly, it didn’t need further polishing. Said another way, when it was born into the physical world—it knew how to walk just fine. In addition, it was also quite robust. The researchers write that two motors in Cassie’s knee malfunctioned during the experiment, but the robot was able to adjust and keep on trucking.
Other labs have been hard at work applying machine learning to robotics.
Last year Google used reinforcement learning to train a (simpler) four-legged robot. And OpenAI has used it with robotic arms. Boston Dynamics, too, will likely explore ways to augment their robots with machine learning. New approaches—like this one aimed at training multi-skilled robots or this one offering continuous learning beyond training—may also move the dial. It’s early yet, however, and there’s no telling when machine learning will exceed more traditional methods.
And in the meantime, Boston Dynamics bots are testing the commercial waters.
Still, robotics researchers, who were not part of the Berkeley team, think the approach is promising. Edward Johns, head of Imperial College London’s Robot Learning Lab, told MIT Technology Review, “This is one of the most successful examples I have seen.”
The Berkeley team hopes to build on that success by trying out “more dynamic and agile behaviors.” So, might a self-taught parkour-Cassie be headed our way? We’ll see.
Image Credit: University of California Berkeley Hybrid Robotics via YouTube Continue reading
Posted in Human Robots