26 October 2019

This Week in Apps: TikTok security check, app store cleanups, GameClub takes on Apple Arcade


Welcome back to This Week in Apps, the Extra Crunch series that recaps the latest OS news, the applications they support, and the money that flows through it all.

The app industry in 2018 saw 194 billion downloads and more than $100 billion in purchases. This past quarter, consumer spending exceeded $23 billion and installs topped 31 billion. And there’s no sign of the app economy slowing down.

But with app marketplaces growing this large and powerful, they’re also now coming under more scrutiny from government officials as this intersection between apps and politics can no longer be overlooked.

This week, U.S. Senators asked for a TikTok security check, Google hosted its Android Developer Summit, a whole bunch of malicious apps got booted off Google Play (and a few on the App Store, too.) Plus, a great alternative to Apple Arcade launched; it’s called GameClub and delivers some of the best App Store games for $5 per month.

Headlines

TikTok comes under more political pressure

The world’s most downloaded app, TikTok, continues to draw attention not for its fun skits and lip-synced songs, but for censorship issues and potential security risks. This week, Senate Democratic Leader Chuck Schumer (D-NY) and Senator Tom Cotton (R-AR) sent a letter (PDF) to Acting Director of National Intelligence Joseph Maguire, formally requesting that the Intelligence Community conduct an assessment of the national security risks posed by TikTok and other China-owned content platforms in the U.S.

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Their concerns revolved around the storage of U.S. TikTok user data (TikTok parent company ByteDance claims it’s in the U.S.), its data collection capabilities, censorship concerns, and the potential for the app to be a counterintelligence threat. As a Chinese-owned company, TikTok still has to adhere to Chinese law. That’s a potential problem. 

By the way, a press release circulated about the letter, which said the senators claimed TikTok was a “national security threat.” They actually did not write those words in the letter — and it’s a step beyond what they were claiming. The senators wanted a risk assessment performed.

The Office of the Director of National Intelligence declined to comment. TikTok said it was “carefully reviewing” the letter. Good thing they just hired those lawyers.

Apple CEO Tim Cook is now the top advisor to a business school called China’s Harvard

The issues around the App Store’s intersection with U.S. politics aren’t limited to TikTok.

Apple, already under scrutiny for removing a crowdsourced mapping app that showed police presence in Hong Kong, last week attracted a letter from a bipartisan group of U.S. lawmakers who urged to have the app reinstated. 

Now (with a lack of concern over the optics apparently), Apple CEO Tim Cook has been appointed as chairman of Tsinghua University’s business school advisory board. The university is known as “China’s Harvard,” and is one of the most country’s most elite institutions; Chinese President Xi Jinping is a noted alumnus. The university has a history of relationships with Western leaders — last year, Mark Zuckerberg, Elon Musk, and Satya Nadella were listed as board members, and its previous chairman was American VC Jim Breyer.

But given the issues around Apple’s capitulation to China’s demands to censor its App Store in the region — not to mention the U.S.-China trade war, or how Apple had told Apple TV+ showrunners not to anger China — everyone pretty much agrees it was not the best timing for this news.

Unfortunately for Apple, it can’t abandon China now, as it’s grown too dependent on its business there. As Vox recently reported:

Unlike tech companies that haven’t broken into the country or only do minor business in it, Apple is now so deep in China that leaving it could be catastrophic. Even if the company was willing to forgo the $44 billion a year in sales it makes in China, it can’t leave the deep network of suppliers and assemblers that build hundreds of millions of iPhones every year.

Millions of malicious apps get booted from Google Play…and malicious apps spotted on the App Store, too

Malicious apps were found on both Google Play and the App Store this week. But these stories are not at all the same.

Security researchers found dozens of Android apps in the Google Play store serving ads to unsuspecting victims as part of a money-making scheme. The 42 apps containing adware had been downloaded more than 8 million times since they first launched in July 2018. The apps were also sending back data about the user’s device, TechCrunch reported — including if certain apps are installed and if the device allows apps from non-app store sources — which could be used to install more malicious software.

Sadly, this kind of thing happens a lot on Google Play.

What’s less common, however, is to find malware on the App Store — which happened this week, when 17 malicious apps were removed.

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The Trello Cheat Sheet: Search, Navigation, and Markdown Essentials


Sample Trello board

Trello makes a fantastic Kanban-style digital organizer. You can use its intuitive features to maintain a shopping list, plan an event, monitor your job hunt, and to manage various other kinds of projects.

Once you start using Trello to track all the bits and pieces of your life, it’s a great idea to become familiar with Trello shortcuts to save yourself time. But given that there are so many shortcuts, it’s easy to miss quite a few of them, even crucial ones, or forget about them altogether.

That’s why we’ve come up with this cheat sheet. It lists various keyboard shortcuts, search operators, bookmarks, and Markdown elements that work in Trello. Dip into the cheat sheet anytime to discover or relearn a Trello trick or two!

FREE DOWNLOAD: This cheat sheet is available as a downloadable PDF from our distribution partner, TradePub. You will have to complete a short form to access it for the first time only. Download The Trello Cheat Sheet.

The Trello Cheat Sheet: Search, Navigation, and Markdown Essentials

Shortcut Action
Keyboard Shortcuts
Before pressing card-specific keyboard shortcuts, hover over card to select it.
Left Arrow Select card on left
Right Arrow Select card on right
Up Arrow Select card above
Down Arrow Select card below
k Select card above
j Select card below
Enter Open card
Esc Close card
¹n Open popover dialog to insert new card
@ (Shift + 2) Select member(s) from autocomplete list while creating card
# (Shift + 3) Select label(s) from autocomplete list while creating card
^ (Shift + 6) Select list name or position from autocomplete list while creating card
^top Send card to top of list while creating card
^bottom Send card to bottom of list while creating card
Shift + Enter Open card immediately after creating it
e Enter quick-edit mode for card
l Open Labels popover menu for card
1 Green label
2 Yellow label
3 Orange label
4 Red label
5 Purple label
6 Blue label
; (Semicolon) Toggle visibility of label names on all boards
, (Comma) Move card to bottom of list on left
. (Period) Move card to bottom of list on right
< (Shift + Comma) Move card to top of list on left
> (Shift + Period) Move card to top of list on right
a Open Members popover menu for card
m Open Members popover menu for card
Space Add yourself to (or remove yourself from) card
d Open due date picker for card
c Archive card
q View cards assigned to you on board
t Edit card title (rename card)
s Watch/unwatch card
v Add your vote to (or remove your vote from) card when Voting Power-Up is enabled
²Ctrl + x Copy card link, OR
Copy card to move
²Ctrl + c Copy card link, OR
Copy card to clone
²Ctrl + v Paste card link, OR
Paste card to list on any board to move or clone it
x Clear all active card filters
b Open Boards menu (header menu with list of boards)
w Toggle board menu (flyout sidebar menu)
f Focus Search Cards box (card filter menu) in sidebar
³Esc Close menu or cancel editing
? (Shift + Forward Slash) Open Shortcuts page
Search Operators
Search filters return matching cards across all boards. (For board-specific searches, card filtering also works.)
/ Bring search box into focus
member:Person Cards assigned to Person
@Person Cards assigned to Person
@me Cards assigned to you
label:Label_Name Cards with label Label_Name
label:Label_Color Cards with label Label_Color
board:Board_Name Cards on board Board_Name
list:List_Name Cards in list List_Name on any board
name:keyword(s) Cards with keyword(s) in name
description:keyword(s) Cards with keyword(s) in description
checklist:keyword(s) Cards with keyword(s) in checklist name or contents
comment:keyword(s) Cards with keyword(s) in comment
is:starred Cards on starred boards
is:open Open or active cards
is:archived Archived cards
has:attachments Cards with attachments
has:description Cards with a description
has:cover Cards with a cover
has:members Cards with members assigned
has:stickers Cards with stickers
due:day Cards due in the next 24 hours
due:week Cards due in the next 7 days
due:month Cards due in the next 28 days
due:overdue Cards that are past due
due:complete Cards that are incomplete
due:incomplete Cards that have due dates marked complete
created:day Cards created in the last 24 hours
created:week Cards created in the last 7 days
created:month Cards created in the last 28 days
created:X Cards created in the last X days
edited:day Cards edited in the last 24 hours
edited:week Cards edited in the last 7 days
edited:month Cards edited in the last 28 days
edited:X Cards edited in the last X days
⁴sort:created Sorts cards by date created
⁴sort:edited Sorts cards by date edited
⁴sort:due Sorts cards by due date
-operator Exclude results matching operator
Markdown Syntax - I
Works in card descriptions, comments, checklists, and your Trello bio.
**text** Emphasize text in bold letters
*text* Italicize text
~~text~~ Strike through text
[Anchor_Text][URL] Create hyperlink
`text` Insert code inline
\text Ignore Markdown formatting for text
⁵![Alt_Text](/path/to/img.jpg) Embed image
Markdown Syntax - II
Works in card descriptions and comments.
--- Insert horizontal line
```text``` Insert formatted code
>text Indent line of text (or insert block quote)
#text Format as H1 header
##text Format as H2 header
###text Format as H2 header
text
---
Format as H2 header
Bookmarks
Your Home Feed trello.com
Your Boards trello.com/Your_Username/boards
Profile Settings trello.com/Your_Username
Account Activity trello.com/Your_Username/activity
Assigned Cards trello.com/Your_Username/cards
Account Settings trello.com/Your_Username/account
Trello Guide trello.com/guide
Trello Templates trello.com/inspiration
Trello Help help.trello.com
¹Hover over card to insert new card below it. Hover over list name to insert card at bottom of list.

²On Mac keyboards, use Cmd instead of Ctrl.

³Works with select menus only.

⁴Works only when used with regular search operators.

⁵Works in card descriptions only.

Make Trello Even More Powerful

From the most basic lists to complex projects with several moving parts and collaborators, Trello can handle anything you throw at it. You can also extend the app’s capabilities with these top Power-Ups and these browser extensions.

Hungry for more of Trello’s secrets? Discover a few of them with these tips for a faster workflow.

Read the full article: The Trello Cheat Sheet: Search, Navigation, and Markdown Essentials


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Anycubic Photon S: The Best Resin 3D Printer? (And Less Than $500)


Our verdict of the Anycubic Photon S:
A stunning 3D printer, capable of producing outstanding 3D prints. Liquid resin plastic means this isn't for everyone.
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The Photon S is the follow-up to Anycubic’s wildly popular Photon, SLA 3D printer. This fully-enclosed 3D printer is perfect for producing highly detailed small models such as tabletop gaming miniatures. Retailing for $489 (but on sale for $100 less until 28th October!), is it worth your money? Are the upgrades worthy of this new price tag when compared to its predecessor, and what makes SLA 3D printers better than traditional Fused Deposition Modelling (FDM) designs?

Let’s find out.

At the end of this review, we’ve got a brand new Photon S to giveaway thanks to our friends at Anycubic. Read on to find out how to win, and be sure to watch all of the review video for some bonus entries.

How the Photon S Works

While FDM 3D printers cost as little as $100, SLA technology is still relatively new for consumers, so is only starting to trickle down to an affordable price range. SLA 3D printing uses a vat of liquid plastic, which gets cured through the use of an LCD screen and a series of UV LEDs. When exposed to a specific wavelength of UV light, the resin solidifies. By using an LCD screen (like you’d find in your laptop), it’s possible to mask a UV LED to produce specific shapes. Stack enough of these cured layers together and the end result is a 3D printed part.

Anycubic Photon S

Contrast this with FDM 3D printing, which heats up spaghetti-like plastic into molten goop, and draws shapes with it, much like piping icing onto a cake. SLA 3D printing provides several benefits over FDM. SLA 3D printing can cure a whole layer at once, so if you’re printing ten objects at once, it takes no longer than producing one part. They have less moving parts, and are capable of far higher precision than FDM, with almost invisible layer lines at times.

Naturally, SLA printers are more expensive than FDM, and the plastic resin can be as much as 4-5x more expensive than FDM filament. You also have to keep uncured resin out of direct light, and it can be a messy product to work with.

Both SLA and FDM printers are tools to solve a problem, and there’s no definitive “best” method of 3D printing. SLA 3D printers are capable of stunning print quality, with relatively little effort when compared to FDM printers. FDM printers can produce very high-quality prints, but they often involve far more mechanical and software components.

Specifications and Design

The Photon S is a sleek, compact unit. It’s small enough to fit on most desks and the futuristic door opens to reveal your printed parts like something from the Blade Runner universe. It sports a color touchscreen on the front panel and operates from the included USB drive. It’s not possible to operate this machine over the network, or tethered to a computer–you must use a flash drive.

Anycubic Photon S touchscreen

This printer features a dual linear rail for its Z-axis, as this is the only moving part. Other models (including the original Photon) only use a single linear rail, so this significant upgrade should result in less Z wobble when printing, leading to more accurate prints.

The Photon S features:

  • 0.78in (20mm) per hour printing speed
  • 13lbs (5.9kg) weight
  • 2560 x 1440 pixel LCD display
  • 25-100 micron layer resolution
  • 4.5in x 2.6in x 6in (115mm x 65mm x 165mm) build volume
  • 50W UV output
  • 9in x 7.9in x 15.8in (230mm x 200mm x 400mm) total dimensions

On the surface, these specifications appear rather pathetic, especially the small build volume. This is typical of an SLA printer, and due to the nature of liquid plastic and resin vats, it could be expensive to use a large-format SLA printer.

The maximum printing speed of 0.78in/hour refers to the Z-axis and is about on par with other SLA printers. On average, prints take between 5-6 hours, with taller models taking between 10-15 hours. Remember though, that you can increase the dimensions or number of models in both the X and Y axis with no impact on the print speed. A 50W UV output is excellent and is a step-up from the 30 or 40W bulbs found on similar models. More power here can cure resin faster, theoretically resulting in faster print times.

Anycubic Photon S

This machine features dual fans to extract fumes from the print chamber, but these use an activated charcoal filter to reduce the strong plastic smell associated with liquid resin. I don’t mind the smell, but family members often comment on it. I print with a window open which reduces the fumes (and helps to reduce any potential health issues). You may not want to sleep with this printer running in the same room, not least because of the noise it makes.

Inside the box, you’ll find a selection of tools, 250ml of resin, several dust masks, several coffee filters (for straining resin when emptying the tank), a few pairs of rubber gloves, some spare parts, a plastic scraper (for removing prints), and an instruction manual.

First Prints with the Photon S

As an owner of the original Photon, I knew the configuration required to get a machine working. While it’s not too difficult to get the Photon S up and running, the process can be confusing for a beginner, and the sometimes incoherent instructions in broken English don’t help the process.

Before starting any prints you must level the bed. Unlike most FDM printers, SLA printers pull the bed up out of a pool of resin, gradually exposing the print. They still work from the bottom up, but generally, are upside down. The bed must be parallel to the LCD surface, and it needs calibrating to a very precise distance.

Anycubic Photon S bed

This process is simple enough in practice — unscrew the two retaining bolts holding the resin vat in and place the vat in a safe location. After this, use the included tools to loosen the bed screw, and use the touchscreen to home the Z-axis. Next, place a sheet of paper between the bed and the LCD, and adjust the distance until you feel friction on the paper. Hold the bed square and tighten the screws again. This is a simple process in theory, but the required pressure on the paper is not clear until you have repeated the process several times, and scoured the internet for tutorial videos.

Anycubic Photon S

You don’t need to level the bed often, fortunately. Once leveled, you can reinstall the vat, pour some resin in, and get ready to print. Using the supplied USB drive, you can print a test model. It’s fascinating to see the bed dunk itself into the liquid plastic again and again. In a few short hours, you’ll have a 3D printed model, ready to clean up.

Post-Processing Prints

As prints get submerged in a bath of liquid resin, they need some cleaning up after printing. This is something not required with FDM printers. You need to use strong alcohol such as 99.9% Isopropyl (rubbing) alcohol to clean off any uncured resin. After this, you need to clean off any remaining alcohol. Finally, you need to allow the prints to finish curing, either by sitting in direct sunlight for several hours (or less depending on your location), or by using a UV curing station such as those found at nail salons.

Anycubic Photon S print cleanup

This process isn’t complicated and after one or two attempts you’ll soon get the hang of it, but it’s all extra work and requires equipment besides the printer itself. You’ll need to wear gloves during all this, as the resin is sticky stuff, which can make a mess of anything you transfer it to. It’s not recommended to let the resin touch your bare skin or eyes.

Slicing Your Own Models

The Photon S comes with a software package to convert your 3D models into printer instructions. This tool lets you configure the layer height, exposure time, placement of models and support structures, and more. It’s basic but gets the job done.

Anycubic Photon S slicing software

Unlike FDM printing, SLA prints do not have a hollow support structure inside — they remain completely solid. Because of this, it can be expensive to print large objects. Many people adapt their models by hollowing them out, but this presents other challenges. Not only do you need to learn how to do this, but you need to produce port holes to allow the resin to drain out, otherwise you’ll have trapped, uncured resin inside your sealed model.

Stunning Print Quality

The Photon S produces stunning prints. It’s almost impossible to see any layer lines at all, even on the coarsest setting of 100-microns (0.0039in/0.1mm) layer height. This is the biggest selling point of the Photon S, and it’s so worth it. If you’re frustrated with FDM print quality or want the absolute best quality from a machine, then this is where resin and SLA shines.

Anycubic Photon S prints

Moving up to 25-micron layer heights (0.00098in/0.025mm) produces jaw-dropping prints but at the expense of print time. You can expect to spend close to 20 hours of machine time on a 1-inch figure printed at 25 microns. While the quality is outstanding at this level, it’s not worth the time investment for 99% of models, as coarser settings are still mind-blowing.

Anycubic Photon S prints

Prints are easy to paint, and you can buy a variety of different resins, from brittle to flexible, and those suitable for casting metal. Different resins need different curing times, however. Translucent resins let more light pass through and so on.

Anycubic Photon S prints

The Photon S is perfect for printing minis for wargaming, or other small yet detailed parts. This machine is almost a new era of 3D printing, whereby machines are closer to plug-and-play than ever before, and the quality starts to approach commercial manufacturing levels. Take a look at these fantasy RPG models for some miniature inspiration.

Should You Buy the Photon S?

The Photon S is a stunning 3D printer. The quality of prints produced by this machine far exceeds any other style of printer. That said, the price of $489 is on the high side when compared to other entry-level SLA printers, and SLA printing is not suitable for everyone. The resin can smell, and you need to be careful around powerful lights, lest you cure your resin. Prints need cleaning up afterward, and large parts are difficult to print.

Anycubic Photon S

If you’re prepared for the cleanup process, and the Photon S suits your style of 3D printing, then you will be very happy with this machine. The print quality alone is the biggest selling point. While the instruction manual could be clearer, you’ll have a print up and running within an hour, and there is a large online community for this little machine should you encounter any problems.

Don’t forget to read our beginner’s guide to 3D printing to ensure you don’t miss a step, or if you prefer a more in-depth guide, then our comprehensive ultimate 3D printing guide will answer all your questions.

Thanks to our friends at Anycubic, we have a brand new Photon S to giveaway. All you have to do is enter our giveaway contest below, and make sure you read the instructions for the chance to enter more than once. If you can’t wait for our contest to end, then use the discount code to get off the price of a new machine.

Enter the Competition!

Anycubic Photon S Giveaway

Read the full article: Anycubic Photon S: The Best Resin 3D Printer? (And Less Than $500)


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5 Apps Better Than Browser Bookmarks to Manage Tabs and Save Links for Later


manage-tabs-apps

Bookmarks are great to save links for later, but they do tend to pile up into clutter. These smart apps will help manage bookmarks better than the built-in browser feature and even get you through your read-it-later list.

There are still several dedicated apps that do a great job of syncing bookmarks, like Pocket for saving links to read. What we’re focusing on here is managing and organizing those bookmarks, or even the tabs open in your window.

1. Qlearly (Chrome, Firefox, Opera): Trello-Like Bookmarks Organizer

Qlearly organizes bookmarks in columns and cards like Trello

Qlearly is one of the most powerful bookmark organizers out there. If you have a large number of bookmarks to sort through, and regularly use bookmarks for research or study, this is the extension to get.

It turns folders into Kanban-type boards, displayed as columns next to one another like in Trello. You can move any link from one to the other, or open an entire board in one click. Each board offers plenty of options, like bulk-editing the saved links, moving them around, and even adding all open tabs to a board.

Qlearly is looking to be more than just a bookmark organizer. On any board, you can add notes or tasks, which can remind you why you saved a page or tell you what you need to do next.

Grab the Qlearly extension as well to instantly save the current tab or all open tabs to any board of your choice. It’s fantastic! The free version has a maximum of 15 boards.

Download: Qlearly for Chrome | Firefox | Opera (Free)

2. Freezetab (Chrome): Choose Which Open Tabs to Bookmark

FreezeTab lets you choose which open tabs to bookmark in a quick and easy way

When you have a lot of tabs open but want to bookmark only a few of them, you need to manually go to each tab and bookmark it. Freezetab makes this process much faster and easier.

The extension gives quick options to bookmark all tabs, current tab, all tabs right of the current tab, all tabs left of the current tab, or to select multiple tabs manually. You can also use the above options to close multiple tabs without saving them.

Saved tabs become groups which you can then rename. FreezeTab has similarities to OneTab, one of our favorite Chrome extensions to manage tab overload. But where OneTab works for all open tabs, FreezeTab gives you more customization and choices.

Download: FreezeTab for Chrome (Free)

3. 30DayBookmarks (Chrome): Temporary Bookmarks That Are Auto-Deleted

Most of us have a bad habit of bookmarking a link but never getting around to deleting it. And that’s why, over time, we build up a cluttered bookmarks folder. If this sounds all too familiar, you’re in luck. 30DayBookmarks will auto-delete those saved links after 30 days.

The extension is available for Chrome only. It looks much like the “favorite” icon, but you’ll be able to distinguish it as black when inactive and red when clicked. You’ll need to get accustomed to clicking this instead of the regular bookmark icon, and there’s no keyboard shortcut either.

The links you save in 30DayBookmarks aren’t added to your Chrome bookmarks, so you won’t be able to access them on your mobile or other devices. Right-click the extension icon and click “Show bookmarks” to find your temporary list. Each link shows when it was saved, so that you know when it’s going to expire.

Download: 30DayBookmarks for Chrome (Free)

4. Mailist (Chrome, Firefox): Pocket Alternative for Read-It-Later Bookmarks

Mailist sends a weekly newsletter with five random links from your read it later list

Do you also keep bookmarking links to your “read-it-later” list and never get around to it? You’re not alone, there are millions of us. Mailist believes it has a good solution to make you go through that list.

You can import existing bookmarks from your browser, and get the Mailist extension to add new links. Once the list is populated, the app sends a weekly newsletter to your inbox. The developers say the reason you don’t get around to that “Read it later” list is that there isn’t a reminder to read it. That’s what this email does.

In the email, you will find five random links from anything you’ve saved. If you click a link to open it, that counts as it being “read” and it won’t show up ever again in emails. The link is still stored in the “Read” folder of your Mailist account. Next week, you’ll get another five random links, with none of them repeating.

The free version of Mailist is good enough already, while the paid version gives you more control by creating custom folders and choosing which ones to get newsletters about. It’s a great alternative for Pocket and other services like it.

Download: Mailist for Chrome | Firefox (Free)

5. Prism (Chrome): Visual Bookmarks With Auto-Updated Screenshots

Prism turns bookmarks into visual thumbnails with auto-updated screenshots

Prism has a unique take on bookmarks. You won’t see a long list of all your links here. Instead, you see screenshots of the article in little windows, much like what many New Tab pages are. Because of that, Prism can turn into a much more useful tool than a regular bookmark organizer.

You could bookmark the homepage of your favorite websites, set them to auto-update screenshots, and thus get a God’s eye view of their updates without visiting the page. You can organize them in different boards too. So you could have one board for news websites, and in one glance, you can see what everyone is talking about.

Admittedly, Prism’s thumbnail-style view makes it less useful for a folder with hundreds of bookmarks. But there is a robust search engine to quickly find something you’re looking for.

Download: Prism for Chrome (Free)

How to Clean Up and Organize Bookmarks

Over the years, you have probably saved so many bookmarks that it’s difficult to manage them all now. You probably aren’t even sure why you saved many of them in the first place. So now it lies as a humongous cluttered pile in your browser.

Well, there’s a way you can finally sort it out. Check out our guide featuring five steps to clean up years of bookmarks so that it’s tidy, manageable, and organized. You’ll be done with the whole process in less than two hours.

Read the full article: 5 Apps Better Than Browser Bookmarks to Manage Tabs and Save Links for Later


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The 5 Best Free VPNs for the iPhone


iphone-vpn

A virtual private network (VPN) can help you stay safe on untrusted networks, which are increasingly common. For example, while the Wi-Fi in your favorite coffee shop or shopping center may be free, you certainly can’t trust it. The problem is that good VPNs don’t come cheap.

Whether you’re not sure if a VPN is right for you, or you’d rather not spend the money, you may be looking for a free VPN for your iPhone. This can especially come in handy if you only need to use a VPN on rare occasions. Let’s look at the best free VPNs for iPhone to see what you should download.

What Do You Need in a VPN?

Most people use VPNs for security reasons, but that’s not the only benefit. These can also help you access TV shows or sports programming that isn’t available due to geographical restrictions.

A VPN can also help you save money. Online retailers, including airlines, are known to charge more or less depending on your location. Spoofing a location other than your own can help you save significantly, depending on what you’re shopping for.

In order to choose the best free VPN for your iPhone, you need to know how you plan to use it. To help figure out exactly what you need, we’ve gathered some of the best reasons to use a VPN. Review that before browsing the below options.

1. Hotspot Shield

Hotspot Shield on different devices

It’s hard to call the absolute best free VPN for iPhone, but Hotspot Shield is definitely a contender. This VPN is fast, private, and has one of the most generous limits for free users of any of the services we’ve found.

Using the completely free version, you can connect up to five different devices to the VPN. It has a bandwidth limit of 500MB per day, or 15GB per month. This probably isn’t enough to do all your browsing over the VPN, but it will cover you in plenty of cases.

On the downside, you’re limited to connecting to just a few VPN server locations that Hotspot Shield chooses for you. You also need to put up with ads and a seven-day trial of the Premium version before you can use the free version exclusively.

Download: Hotspot Shield (Free, subscription available)

2. TunnelBear

The TunnelBear logo

TunnelBear is another popular option when it comes to free VPNs for iPhone users. That said, after using it for a bit, you may find that you’d rather opt for the paid version. It’s relatively affordable and offers some significant upgrades over the free version.

The main issue with the free version is that you’re limited to 500MB of traffic per month. This contrasts sharply with Hotspot Shield, mentioned above; with TunnelBear you get 500MB per month, not per day. That said, if you just need to protect yourself on public Wi-Fi occasionally, that may be all you need.

TunnelBear tracks very little data about its users, which is reassuring if you’re privacy-minded. You don’t even need to supply your first name to sign up.

Download: TunnelBear (Free, subscription available)

3. Speedify

Hide.me uses multiple connections for speed

A notorious side effect of VPNs is that they slow down your browsing speed. Much of this reputation is actually from the old days, when people primarily used VPNs to remote into their corporate network. Still, the encryption today’s VPNs use can slow down your connection at times.

As you may guess from the name, Speedify aims to ensure that your VPN connection is as fast as possible. Speedify will use your Wi-Fi connection as well as your phone’s LTE, working together in tandem to maximize speed. The service also provides up to 5GB of data transfer per month using the free version, though that will eventually drop down to 1GB as you use the service.

If you’re after privacy and performance, Speedify may be the best VPN app for iPhone, at least for you. On the other hand, if you’re looking to bypass geographical restrictions for streaming services, you should look elsewhere.

Download: Speedify (Free, subscription available)

4. ProtonVPN

ProtonVPN is from the ProtoMail people

If you’re looking for the best free unlimited VPN for iPhone, ProtonVPN is a strong contender. Unlike some of the other options on this list, you’re not restricted on how much data you can use. That’s not to say there are zero restrictions, however.

Instead of limiting how much data you can consume, ProtonVPN limits free users to a single device. If you’re looking for a free VPN app only for your iPhone, that’s fine. Bu if you need something to use across multiple devices, this could be a problem.

ProtonVPN comes from the same people that bring you secure email service ProtonMail, so you can sure that security is a major focus of this app. That said, its free version isn’t the best for getting around geo-restrictions, since you can only choose from three locations.

Download: ProtonVPN (Free, subscription available)

5. Hide.me

Hide.me emphasizes privacy

Like ProtonVPN, Hide.me restricts you to a single device instead of putting limits on how much data you’re allotted. This means it’s another contender for the best free unlimited VPN for iPhone users.

As the name implies, Hide.me is meant for privacy-conscious folks. It’s not just available for iPhone users either; you can download apps for Windows, Mac, and Android as well. That said, as the free version limits you to a single device, this probably won’t do you much good.

While it’s appreciated that there are no ads or speed throttling, the service imposes some other restrictions. For your location, you’re restricted to Singapore, Canada, or the Netherlands. Paid users, on the other hand, have the option to choose from 30 different countries for their virtualized location.

Download: Hide.me (Free, subscription available)

What About Other VPN Providers?

You might find that you connect to untrusted networks with your iPhone often. But you shouldn’t forget about protecting your traffic on other devices, too. Most of these free iPhone VPN apps are cross-platform, but as some limit you to a single device, they aren’t the best for those who use multiple platforms.

Remember that if you can afford it, we recommend paid VPNs over free options because they offer greater security and more features. Take a look at ExpressVPN, which we recommend for its performance and privacy. It’s also available on all major OSes.

Read the full article: The 5 Best Free VPNs for the iPhone


Read Full Article

Quantum computing’s ‘Hello World’ moment


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.

GettyImages feynman

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 Martinez 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 Martinez. “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.


Read Full Article

Quantum computing’s ‘Hello World’ moment


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.

GettyImages feynman

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 Martinez 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 Martinez. “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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