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Quantum computing’s ‘Hello World’ moment – TechCrunch

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Does quantum computing really exist? It’s fitting that for decades this field has been haunted by the fundamental uncertainty of whether it would, eventually, prove to be a wild goose chase. But Google has collapsed this nagging superposition with research not just demonstrating what’s called “quantum supremacy,” but more importantly showing that this also is only the very beginning of what quantum computers will eventually be capable of.

This is by all indications an important point in computing, but it is also very esoteric and technical in many ways. Consider, however, that in the 60s, the decision to build computers with electronic transistors must have seemed rather an esoteric point as well. Yet that was in a way the catalyst for the entire Information Age.

Most of us were not lucky enough to be involved with that decision or to understand why it was important at the time. We are lucky enough to be here now — but understanding takes a bit of explanation. The best place to start is perhaps with computing and physics pioneers Alan Turing and Richard Feynman.

‘Because nature isn’t classical, dammit’

The universal computing machine envisioned by Turing and others of his generation was brought to fruition during and after World War II, progressing from vacuum tubes to hand-built transistors to the densely packed chips we have today. With it evolved an idea of computing that essentially said: If it can be represented by numbers, we can simulate it.

That meant that cloud formation, object recognition, voice synthesis, 3D geometry, complex mathematics — all that and more could, with enough computing power, be accomplished on the standard processor-RAM-storage machines that had become the standard.

But there were exceptions. And although some were obscure things like mathematical paradoxes, it became clear as the field of quantum physics evolved that it may be one of them. It was Feynman who proposed in the early 80s that if you want to simulate a quantum system, you’ll need a quantum system to do it with.

“I’m not happy with all the analyses that go with just the classical theory, because nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical,” he concluded, in his inimitable way. Classical computers, as he deemed what everyone else just called computers, were insufficient to the task.

Richard Feynman made the right call, it turns out.

The problem? There was no such thing as a quantum computer, and no one had the slightest idea how to build one. But the gauntlet had been thrown, and it was like catnip to theorists and computer scientists, who since then have vied over the idea.

Could it be that with enough ordinary computing power, power on a scale Feynman could hardly imagine — data centers with yottabytes of storage and exaflops of processing — we can in fact simulate nature down to its smallest, spookiest levels?

Or could it be that with some types of problems you hit a wall, and that you can put every computer on Earth to a task and the progress bar will only tick forward a percentage point in a million years, if that?

And, if that’s the case, is it even possible to create a working computer that can solve that problem in a reasonable amount of time?

In order to prove Feynman correct, you would have to answer all of these questions. You’d have to show that there exists a problem that is not merely difficult for ordinary computers, but that is effectively impossible for them to solve even at incredible levels of power. And you would have to not just theorize but create a new computer that not just can but does solve that same problem.

By doing so you would not just prove a theory, you would open up an entirely new class of problem-solving, of theories that can be tested. It would be a moment when an entirely new field of computing first successfully printed “hello world” and was opened up for everyone in the world to use. And that is what the researchers at Google and NASA claim to have accomplished.

In which we skip over how it all actually works

google quantum team

One of the quantum computers in question. I talked with that fellow in the shorts about microwave amps and attenuators for a while.

Much has already been written on how quantum computing differs from traditional computing, and I’ll be publishing another story soon detailing Google’s approach. But some basics bear mentioning here.

Classical computers are built around transistors that, by holding or vacating a charge, signify either a 1 or a 0. By linking these transistors together into more complex formations they can represent data, or transform and combine it through logic gates like AND and NOR. With a complex language specific to digital computers that has evolved for decades, we can make them do all kinds of interesting things.

Quantum computers are actually quite similar in that they have a base unit that they perform logic on to perform various tasks. The difference is that the unit is more complex: a qubit, which represents a much more complex mathematical space than simply 0 or 1. Instead you may think of their state may be thought of as a location on a sphere, a point in 3D space. The logic is also more complicated, but still relatively basic (and helpfully still called gates): That point can be adjusted, flipped, and so on. Yet the qubit when observed is also digital, providing what amounts to either a 0 or 1 value.

By virtue of representing a value in a richer mathematical space, these qubits and manipulations thereof can perform new and interesting tasks, including some which, as Google shows, we had no ability to do before.

A quantum of contrivance

In order to accomplish the tripartite task summarized above, first the team had to find a task that classical computers found difficult but that should be relatively easy for a quantum computer to do. The problem they settled on is in a way laughably contrived: Being a quantum computer.

In a way it makes you want to just stop reading, right? Of course a quantum computer is going to be better at being itself than an ordinary computer will be. But it’s not actually that simple.

Think of a cool old piece of electronics — an Atari 800. Sure, it’s very good at being itself and running its programs and so on. But any modern computer can simulate an Atari 800 so well that it could run those programs in orders of magnitude less time. For that matter, a modern computer can be simulated by a supercomputer in much the same way.

Furthermore, there are already ways of simulating quantum computers — they were developed in tandem with real quantum hardware so performance could be compared to theory. These simulators and the hardware they simulate differ widely, and have been greatly improved in recent years as quantum computing became more than a hobby for major companies and research institutions.

qubit lattice

This shows the “lattice” of qubits as they were connected during the experiment (colored by the amount of error they contributed, which you don’t need to know about.)

To be specific, the problem was simulating the output of a random sequence of gates and qubits in a quantum computer. Briefly stated, when a circuit of qubits does something, the result is, like other computers, a sequence of 0s and 1s. If it isn’t calculating something in particular, those numbers will be random — but crucially, they are “random” in a very specific, predictable way.

Think of a pachinko ball falling through its gauntlet of pins, holes and ramps. The path it takes is random in a way, but if you drop 10,000 balls from the exact same position into the exact same maze, there will be patterns in where they come out at the bottom — a spread of probabilities, perhaps more at the center and less at the edges. If you were to simulate that pachinko machine on a computer, you could test whether your simulation is accurate by comparing the output of 10,000 virtual drops with 10,000 real ones.

It’s the same with simulating a quantum computer, though of course rather more complex. Ultimately however the computer is doing the same thing: simulating a physical process and predicting the results. And like the pachinko simulator, its accuracy can be tested by running the real thing and comparing those results.

But just as it is easier to simulate a simple pachinko machine than a complex one, it’s easier to simulate a handful of qubits than a lot of them. After all, qubits are already complex. And when you get into questions of interference, slight errors and which direction they’d go, etc. — there are, in fact, so many factors that Feynman decided at some point you wouldn’t be able to account for them all. And at that point you would have entered the realm where only a quantum computer can do so — the realm of “quantum supremacy.”

Exponential please, and make it a double

After 1,400 words, there’s the phrase everyone else put right in the headline. Why? Because quantum supremacy may sound grand, but it’s only a small part of what was accomplished, and in fact this result in particular may not last forever as an example of having reached those lofty heights. But to continue.

Google’s setup, then, was simple. Set up randomly created circuits of qubits, both in its quantum computer and in the simulator. Start simple with a few qubits doing a handful of operational cycles and compare the time it takes to produce results.

Bear in mind that the simulator is not running on a laptop next to the fridge-sized quantum computer, but on Summit — a supercomputer at Oak Ridge National Lab currently rated as the most powerful single processing system in the world, and not by a little. It has 2.4 million processing cores, a little under 3 petabytes of memory, and hits about 150 petaflops.

At these early stages, the simulator and the quantum computer happily agreed — the numbers they spat out, the probability spreads, were the same, over and over.

But as more qubits and more complexity got added to the system, the time the simulator took to produce its prediction increased. That’s to be expected, just like a bigger pachinko machine. At first the times for actually executing the calculation and simulating it may have been comparable — a matter of seconds or minutes. But those numbers soon grew hour by hour as they worked their way up to 54 qubits.

When it got to the point where it took the simulator five hours to verify the quantum computer’s result, Google changed its tack. Because more qubits isn’t the only way quantum computing gets more complex (and besides, they couldn’t add any more to their current hardware). Instead, they started performing more rounds of operations with a given circuit, which adds all kinds of complexity to the simulation for a lot of reasons that I couldn’t possibly explain.

For the quantum computer, doing another round of calculations takes a fraction of a second, and even multiplied by thousands of times to get the required number of runs to produce usable probability numbers, it only ended up taking the machine several extra seconds.

schroed feyn chart

You know it’s real because there’s a chart. The dotted line (added by me) is the approximate path the team took, first adding qubits (x-axis) and then complexity (y-axis).

For the simulator, verifying these results took a week — a week, on the most powerful computer in the world.

At that point the team had to stop doing the actual simulator testing, since it was so time-consuming and expensive. Yet even so, no one really claimed that they had achieved “quantum supremacy.” After all, it may have taken the biggest classical computer ever created thousands of times longer, but it was still getting done.

So they cranked the dial up another couple notches. 54 qubits, doing 25 cycles, took Google’s Sycamore system 200 seconds. Extrapolating from its earlier results, the team estimated that it would take Summit 10,000 years.

What happened is what the team called double exponential increase. It turns out that adding qubits and cycles to a quantum computer adds a few microseconds or seconds every time — a linear increase. But every qubit you add to a simulated system makes that simulation exponentially more costly to run, and it’s the same story with cycles.

Imagine if you had to do whatever number of push-ups I did, squared, then squared again. If I did 1, you would do 1. If I did 2, you’d do 16. So far no problem. But by the time I get to 10, I’d be waiting for weeks while you finish your 10,000 push-ups. It’s not exactly analogous to Sycamore and Summit, since adding qubits and cycles had different and varying exponential difficulty increases, but you get the idea. At some point you can have to call it. And Google called it when the most powerful computer in the world would still be working on something when in all likelihood this planet will be a smoking ruin.

It’s worth mentioning here that this result does in a way depend on the current state of supercomputers and simulation techniques, which could very well improve. In fact IBM published a paper just before Google’s announcement suggesting that theoretically it could reduce the time necessary for the task described significantly. But it seems unlikely that they’re going to improve by multiple orders of magnitude and threaten quantum supremacy again. After all, if you add a few more qubits or cycles, it gets multiple orders of magnitude harder again. Even so, advances on the classical front are both welcome and necessary for further quantum development.

‘Sputnik didn’t do much, either’

So the quantum computer beat the classical one soundly on the most contrived, lopsided task imaginable, like pitting an apple versus an orange in a “best citrus” competition. So what?

Well, as founder of Google’s Quantum AI lab Hartmut Neven pointed out, “Sputnik didn’t do much either. It just circled the Earth and beeped.” And yet we always talk about an industry having its “Sputnik moment” — because that was when something went from theory to reality, and began the long march from reality to banality.

2019 SB Google 0781

The ritual passing of the quantum computing core.

That seemed to be the attitude of the others on the team I talked with at Google’s quantum computing ground zero near Santa Barbara. Quantum superiority is nice, they said, but it’s what they learned in the process that mattered, by confirming that what they were doing wasn’t pointless.

Basically it’s possible that a result like theirs could be achieved whether or not quantum computing really has a future. Pointing to one of the dozens of nearly incomprehensible graphs and diagrams I was treated to that day, hardware lead and longtime quantum theorist John Martines explained one crucial result: The quantum computer wasn’t doing anything weird and unexpected.

This is very important when doing something completely new. It was entirely possible that in the process of connecting dozens of qubits and forcing them to dance to the tune of the control systems, flipping, entangling, disengaging, and so on — well, something might happen.

Maybe it would turn out that systems with more than 14 entangled qubits in the circuit produce a large amount of interference that breaks the operation. Maybe some unknown force would cause sequential qubit photons to affect one another. Maybe sequential gates of certain types would cause the qubit to decohere and break the circuit. It’s these unknown unknowns that have caused so much doubt over whether, as asked at the beginning, quantum computing really exists as anything more than a parlor trick.

Imagine if they discovered that in digital computers, if you linked too many transistors together, they all spontaneously lost their charge and went to 0. That would put a huge limitation on what a transistor-based digital computer was capable of doing. Until now, no one knew if such a limitation existed for quantum computers.

“There’s no new physics out there that will cause this to fail. That’s a big takeaway,” said Martines. “We see the same errors whether we have a simple circuit or complex one, meaning the errors are not dependent on computational complexity or entanglement — which means the complex quantum computing going on doesn’t have fragility to it because you’re doing a complex computation.”

They operated a quantum computer at complexities higher than ever before, and nothing weird happens. And based on their observations and tests, they found that there’s no reason to believe they can’t take this same scheme up to, say, a thousand qubits and even greater complexity.

Hello world

That is the true accomplishment of the work the research team did. They found out, in the process of achieving the rather overhyped milestone of quantum superiority, that quantum computers are something that can continue to get better and to achieve more than simply an interesting experimental results.

This was by no means a given — like everything else in the world, quantum or classical, it’s all theoretical until you test it.

It means that sometime soonish, though no one can really say when, quantum computers will be something people will use to accomplish real tasks. From here on out, it’s a matter of getting better, not proving the possibility; of writing code, not theorizing whether code can be executed.

It’s going from Feynman’s proposal that a quantum computer will be needed to using a quantum computer for whatever you need it for. It’s the “hello world” moment for quantum computing.

Feynman, by the way, would probably not be surprised. He knew he was right.

Google’s paper describing their work was published in the journal Nature. You can read it here.



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Microsoft trackers run afoul of DuckDuckGo, get added to blocklist

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Aurich Lawson

DuckDuckGo, the privacy-minded search company, says it will block trackers from Microsoft in its desktop web browser, following revelations in May that certain scripts from Bing and LinkedIn were getting a pass.

In a blog post, DuckDuckGo founder Gabriel Weinberg says that he’s heard users’ concerns since security researcher Zach Edwards’ thread that “we didn’t meet their expectations around one of our browser’s web tracking protections.” Weinberg says that, over the next week, the company’s browser will add Microsoft to the list of third-party tracking scripts blocked by its mobile and desktop browsers, as well as extensions for other browsers.

“Previously, we were limited in how we could apply our 3rd-Party Tracker Loading Protection on Microsoft tracking scripts due to a policy requirement related to our use of Bing as a source for our private search results,” Weinberg writes. “We’re glad this is no longer the case. We have not had, and do not have, any similar limitation with any other company.”

There are a lot of pervasive, identifying things that load up on most modern webpages. At issue in DuckDuckGo’s apps was its default blocking of scripts from companies like Facebook and Google loading on third-party websites. DuckDuckGo, which uses Microsoft’s Bing as one of its sources for search results, had to allow some of Microsoft’s trackers to load “due to a policy requirement.” In a Reddit response at the time of the revelation, Weinberg noted that Microsoft’s trackers were still blocked in most ways, like utilizing third-party cookies for fingerprinting visitors.

There’s more to the delicate dance between DuckDuckGo and Microsoft than just trackers, however. Microsoft also provides ads that run on DuckDuckGo’s search results. To allow advertisers to see when someone has clicked an ad on DuckDuckGo and arrived at their page, the DuckDuckGo apps won’t block requests from bat.bing.com. Weinberg notes that you can avoid this by turning off ads in DuckDuckGo search entirely. The company is working on validating ads in ways that can be non-tracking, Weinberg writes, akin to similar efforts by Safari and Firefox.

Finally, DuckDuckGo aims to be more open about its tracker blocking. The company committed its tracker blocklist to a public GitHub repository yesterday and published a new help doc on its tracking protections.

It can look like a lot of work over two scripts, but then DuckDuckGo lives inside the tricky balance of trying to make its search product convenient and relevant while offering its users as much privacy as websites can stand before breaking. And the 15-year-old company from Paoli, Pennsylvania, can’t just leave Bing behind entirely. Weinberg noted in his May Reddit response that most of its traditional search results and images come from Bing. “Really only two companies (Google and Microsoft) have a high-quality global web link index” due to the billion-dollar cost, Weinberg wrote. Every company that wants to provide search to the world faces either a duopoly or a very long journey.

Microsoft, meanwhile, continues to expand its advertising markets, most recently to Netflix, and, potentially, into its own operating system. Its advertising revenue was $3 billion for the quarter ending June 30, an increase of 15 percent year over year but the lowest growth rate in more than a year.

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Sonic the Hedgehog doesn’t need easily legible legends on his mechanical keyboard

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Enlarge / Sonic the Hedgehog mechanical keyboard.

When you’re a beloved blue eulipotyphla with the speed of a race car, all the golden rings, a pal like Tails, and even a pair of hit feature films, you start feeling like you can do anything. That includes typing on a truncated mechanical keyboard without letters, numbers, or any other legends written on the top of the keycaps.

Higround, known for gaming gear, is releasing today a trio of 65 percent mechanical keyboards made in collaboration with Sega, as spotted by Nintendo Wire, as well as other Sega-focused gear, including keycaps and mousepads.

Dreamcast's <i>Sonic Adventure 2</i> mechanical keyboard.
Enlarge / Dreamcast’s Sonic Adventure 2 mechanical keyboard.

Pictures from the brand show a trio of keyboards coming to life with colorful details delivering nostalgic imagery anywhere from, depending on the keyboard, the loops and rings of the Green Hill Zone in 1991‘s Sonic the Hedgehog to the contrasting profiles of Sonic and Knuckles from 2001’s Sonic Adventure 2 to the rainbow-colored arrow keys mimicking console controller buttons in tribute to Sega’s last globally released console, the Dreamcast.

The PBT, dye-sublimated keycaps on the keyboards are 1.5 mm thick, according to Higround, and ditch informative legends on their topsides in favor of an artful appearance when viewing the keyboard from the top down. But from a typical seated position, you should be able to see legends side-printed on the front of the keycaps. You don’t have to be a touch typist to use the Sega keyboards, but if you’re not, they’ll be harder to use at first than the typical keyboard.

Sega Dreamcast mechanical keyboard.
Enlarge / Sega Dreamcast mechanical keyboard.

Sonic’s gotta go fast, so it’s fitting that the keyboards use Speed Silver linear mechanical switches from TTC. They’re specced for about 3.4 mm total travel, with a 1.08 mm actuation point and 45 grams of force to actuate (if you’re unsure of what that means, check out our mechanical keyboard guide). Those numbers make them a bit shorter to actuate and bottom out than the common Cherry MX Red switch (4mm / 2mm / 45gf); although, Higround could have gone shorter with low-profile mechanical switches to fit the speed theme even more.

The mechanical switches have Sonic-blue-like housing.

The mechanical switches have Sonic-blue-like housing.

If you’re looking for a speedy way to complete those spreadsheets, the Sega keyboards aren’t a winning fit since they lack a numpad.

Ultimately, you need a combination of Sega and linear typing fandom and the ability to work without a numpad (some touch-typing skills wouldn’t hurt either) in order for these keyboards to be something that can help you level up your productivity, rather than an interesting collector’s item.

But the keyboards aren’t as polarizing as they could be… at least they don’t make you type in Elvish.

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This 6-inch board turns a Raspberry Pi module into a DIY router

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Enlarge / Let your Pi do the work while the CM4 Router Board handles the connections.

If you’re intrigued by the prospect of building a DIY router, Seeed Studio has a board that’s just waiting to put a Raspberry Pi Computing Module 4 (CM4) to work. Assuming, of course, that you can find the Pi module.

Seeed’s CM4 Router Board adds two full-speed gigabit network ports, two USB 2.0 ports, a microSD slot, an HDMI out, a GPIO interface for Raspberry Pi HAT add-ons, and a 0.91-inch OLED display to your Pi CM4. Having the CM4 at the system’s core gives you 32 different options for RAM, storage, and wireless capabilities on your homebrew router. The Router Board comes with OpenWRT installed, but it could run Ubuntu, Raspberry OS, or any other Pi-friendly system.

Seeed notes that beyond DIY routers, the CM4 Router Board could also become a gateway, mini-NAS, wireless network bridge, or mini-server. You can buy a Pi CM4 with wireless capabilities, but you’ll likely need (or prefer) a separate Wi-Fi setup connected to your DIY router.

Why not just plug a USB-to-Ethernet adapter into the Pi you already have? Seeed says its board’s RTL8111E controller chip “offers better performance, lower CPU usage, and higher stability for a long time work [sic] compared with a USB network card.”

The CM4 Router Board should be available for around $55 soon at both Seeed and Mouser Electronics, though the latter cites an 11-week lead time beyond its initial stock.

Those hunting for a Raspberry Pi CM4 board might consider Ars commenter MightyPez’s advice to keep an eye on Pi stock monitor Rpilocator, which offers RSS feeds. You can even set up push notifications with Rpilocator’s official Python script.

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