China is the greatest growth story in history. A true triumph of market-forces. But since the boom-bust cycles of capitalism is a feature and not a bug. China will have the greatest bust in history, to match its boom. As long the government doesn’t regress to the delusions of communism, capital will move from weak hands to strong hands at vastly discounted rates. Which is the only element of trickle-down economics that truly works. Ideally, they would become more democratic (think Singapore not India).
However, there are forces pushing the boom further. First and most importantly, the inclusion of China into the existing indices. I do not buy the argument that China is undervalued. Look at any nation with sketchy rule of law and you’ll see the markdown in valuation. Again, use a simple momentum filter and follow along.
Whoever figures this out is going to make a ridiculous amount of money. However we are a long way from knowing who the winners will be. Be prepared to momentum-ride the two fastest horses, changing mounts like a monkey-jock.
The Trump administration, determined to overhaul and modernize the nation’s infrastructure, is drafting plans to privatize some public assets such as airports, bridges, highway rest stops and other facilities, according to top officials and advisers.
In his proposed budget released Tuesday, President Trump called for spending $200 billion over 10 years to “incentivize” private, state and local spending on infrastructure.
Quantum computers have long held the promise of performing certain calculations that are impossible—or at least, entirely impractical—for even the most powerful conventional computers to perform. Now, researchers at a Google laboratory in Goleta, Calif., may finally be on the cusp of proving it, using the same kinds of quantum bits, or qubits, that one day could make up large-scale quantum machines.
By the end of this year, the team aims to increase the number of superconducting qubits it builds on integrated circuits to create a 7-by-7 array. With this quantum IC, the Google researchers aim to perform operations at the edge of what’s possible with even the best supercomputers, and so demonstrate “quantum supremacy.”
“We’ve been talking about, for many years now, how a quantum processor could be powerful because of the way that quantum mechanics works, but we want to specifically demonstrate it,” says team member John Martinis, a professor at the University of California, Santa Barbara, who joined Google in 2014.
A system size of 49 superconducting qubits is still far away from what physicists think will be needed to perform the sorts of computations that have long motivated quantum computing research. One of those is Shor’s algorithm, a computational scheme that would enable a quantum computer to quickly factor very large numbers and thus crack one of the foundational components of modern cryptography. In a recent commentary in Nature, Martinis and colleagues estimated that a 100-million-qubit system would be needed to factor a 2,000-bit number—a not-uncommon public key length—in one day. Most of those qubits would be used to create the special quantum states that would be needed to perform the computation and to correct errors, creating a mere thousand or so stable “logical qubits” from thousands of less stable physical components, Martinis says.
There will be no such extra infrastructure in this 49-qubit system, which means a different computation must be performed to establish supremacy. To demonstrate the chip’s superiority over conventional computers, the Google team will execute operations on the array that will cause it to evolve chaotically and produce what looks like a random output. Classical machines can simulate this output for smaller systems. In April, for example, Lawrence Berkeley National Laboratory reported that its 29-petaflop supercomputer, Cori, had simulated the output of 45 qubits. But 49 qubits would push—if not exceed—the limits of conventional supercomputers.
This computation does not as yet have a clear practical application. But Martinis says there are reasons beyond demonstrating quantum supremacy to pursue this approach. The qubits used to make the 49-qubit array can also be used to make larger “universal” quantum systems with error correction, the sort that could do things like decryption, so the chip should provide useful validation data.
Steps to Supremacy: Google’s quantum computing chip is a 2-by-3 array of qubits. The company hopes to make a 7-by-7 array later this year.
There may also be, the team suspects, untapped computational potential in systems with little or no error correction. “It would be wonderful if this were true, because then we could have useful products right away instead of waiting for a long time,” says Martinis. One potential application, the team suggests, could be in the simulation of chemical reactions and materials.
Google recently performed a dry run of the approach on a 9-by-1 array of qubits and tested out some fabrication technology on a 2-by-3 array. Scaling up the number of qubits will happen in stages. “This is a challenging system engineering problem,” Martinis says. “We have to scale it up, but the qubits still have to work well. We can’t have any loss in fidelity, any increase in error rates, and I would say error rates and scaling tend to kind of compete against each other.” Still, he says, the team thinks there could be a way to scale up systems well past 50 qubits even without error correction.
Google is not the only company working on building larger quantum systems without error correction. In March, IBM unveiled a plan to create such a superconducting qubit system in the next few years, also with roughly 50 qubits, and to make it accessible on the cloud. “Fifty is a magic number,” says Bob Sutor, IBM’s vice president for this area, because that’s around the point where quantum computers will start to outstrip classical computers for certain tasks.
The quality of superconducting qubits has advanced a lot over the years since D-Wave Systems began offering commercial quantum computers, says Scott Aaronson, a professor of computer science at the University of Texas at Austin. D-Wave, based in Burnaby, B.C., Canada, has claimed that its systems offer a speedup over conventional machines, but Aaronson says there has been no convincing demonstration of that. Google, he says, is clearly aiming for a demonstration of quantum supremacy that is “not something you’ll have to squint and argue about.”
It’s still unclear whether there are useful tasks a 50-or-so-qubit chip could perform, Aaronson says. Nor is it certain whether systems can be made bigger without error correction. But he says quantum supremacy will be an important milestone nonetheless, one that is a natural offshoot of the effort to make large-scale, universal quantum machines: “I think that it is absolutely worth just establishing as clearly as we can that the world does work this way. Certainly, if we can do it as a spin-off of technology that will be useful eventually in its own right, then why the hell not?”
Lidarland is buzzing with cheap, solid-state devices that are supposedly going to shoulder aside the buckets you see revolving atop today’s experimental driverless cars. Quanergy started this solid-state patter, a score of other startups continued it, and now Velodyne, the inventor of those rooftop towers, is talking the talk, too.
Not Luminar. This company, which emerged from stealth mode earlier this month, is fielding a 5-kilogram box with a window through which you can make out not microscopic MEMs mirrors, but two honking, macroscopic mirrors, each as big as an eye. Their movement—part of a secret-sauce optical arrangement—steers a pencil of laser light around a scene so that a single receiver can measure the distance to every detail.
“There’s nothing wrong with moving parts,” says Luminar founder and CEO Austin Russell. “There are a lot of moving parts in a car, and they last for a 100,000 miles or more.”
A key difference between Luminar and all the others is its reliance on home-made stuff rather than industry-standard parts. Most important is its use of indium gallium arsenide for the photodetector. This compound semiconductor is harder to manufacture and thus more expensive than silicon, but it can receive at a wavelength of 1550 nanometers, deep in the infrared part of the spectrum. That makes this wavelength much safer for human eyes than today’s standard wavelength, 905 nm. Luminar can thus pump out a beam with 40 times the power of rival sensors, increasing its resolution, particularly at 200 meters and beyond. That’s how far cars will have to see at highway speeds if they want to give themselves more than half a second to react to events.
The vast majority of companies in this space are integrating off-the-shelf components,” he says. “The same lasers, same receivers, same processors—and that’s why there have been no advances in lidar performance in a decade. Every couple of years a company says, ‘we have new lidar sensor, half the size, half the price, and oh, by the way, half the performance.’ The performance of the most expensive ones has stayed the same for practically a decade; all the newer ones are orders of magnitude worse.”
The XS-1 program envisions a fully reusable unmanned vehicle, roughly the size of a business jet, which would take off vertically like a rocket and fly to hypersonic speeds. The vehicle would be launched with no external boosters, powered solely by self-contained cryogenic propellants. Upon reaching a high suborbital altitude, the booster would release an expendable upper stage able to deploy a 3,000-pound satellite to polar orbit. The reusable first stage would then bank and return to Earth, landing horizontally like an aircraft, and be prepared for the next flight, potentially within hours.
The main issue with Trump is that he’s not good at being part of an organization. He used to own everything he runs and thought he would own the US government. But it is not designed to be owned. That is why he thinks any resistance is Deep State. It’s actually Free State.