The 3 Types of Quantum Computers and Their Applications
It’s an exciting time in computing.
Just days ago, Google’s AlphaGo AI took an insurmountable lead in the 3,000 year-old game of Go against the reigning world champion, Lee Sedol. In a five-game series, the score is now 3-1 for the machine with one game left on March 15, 2016 in Seoul, South Korea.
While IBM’s Deep Blue beat reigning chess champion Garry Kasparov in 1997 by using brute force, Go is a game with more possible moves than atoms in the known universe (literally). Therefore, the technology doesn’t yet exist to make such calculations in short amounts of time.
Google had to take a different approach: to beat the grand master, it needed to enable AlphaGo to self-improve through deep learning.
AlphaGo’s historical decision is a milestone for artificial intelligence, and now the technology community is anxiously waiting to see what’s next for AI. Some say that it is beating a human world champion at a real-time strategy game such as Starcraft, while others look to quantum computing – technology that could raise the potential power of AI exponentially.
What is Quantum Computing?
While everyday analog computing is limited to having a single value of either 0 or 1 for each bit, quantum computing uses quantum bits (qubits) that are simultaneously in both states (0 and 1) at the same time.
The consequence of this superposition, as it’s called, is that quantum computers are able to test every solution of a problem at once. Further, because of this exponential relationship, such computers should be able to double their quantum computing power with each additional qubit.
Image credit: Universe Review
Types of Quantum Computers
There are three types of quantum computers that are considered to be possible by IBM. Shown in the above infographic, they range from a quantum annealer to a universal quantum.
The quantum annealer has been successfully developed by Canadian company D-Wave, but it is difficult to tell whether it actually has any real “quantumness” thus far. Google added credibility to this notion in December 2015, when it revealed tests showing that its D-Wave quantum computer was 3,600 times faster than a supercomputer at solving specific, complex problems.
Expert opinion, however, is still skeptical on these claims. Such criticisms also shed light on the major limitation of quantum annealers, which is that they may only be engineered to solve very specific optimization problems, and have limited general practicality.
The holy grail of quantum computing is the universal quantum, which could allow for exponentially faster calculations with more generality.
However, building such a device ends up posing a number of important technical challenges. Quantum particles turn out to be quite fickle, and the smallest interference from light or sound can create errors in the computing process.
Doing calculations at exponential speeds is not very useful when those calculations are incorrect.
The Market and Applications
IBM highlights just some of the possibilities around universal quantum computers in a recent press release:
A universal quantum computer uses quantum mechanics to process massive amounts of data and perform computations in powerful new ways not possible with today’s conventional computers. This type of leap forward in computing could one day shorten the time to discovery for life-saving cancer drugs to a fraction of what it is today; unlock new facets of artificial intelligence by vastly accelerating machine learning; or safeguard cloud computing systems to be impregnable from cyber-attack.
This means that quantum computing could be a trillion dollar market, touching massive future markets such as artificial intelligence, robotics, defense, cryptography, and pharmaceuticals.
However, until a universal quantum can be built, the market remains fairly limited in size and focused on R&D. Quantum computing is expected to surpass a market of $5 billion market by 2020.
As a final note: its worth seeing where quantum computing sits on Gartner’s emerging technology hype cycle:
Gartner still describes it as being “10 years or more” away from reaching the plateau.
The Game of Life: Visualizing China’s Social Credit System
This infographic explores how China’s proposed social credit system will monitor and surveil citizens, and how it’ll be used to reward or punish them.
The Game of Life: Visualizing China’s Social Credit System
In an attempt to imbue trust, China has announced a plan to implement a national ranking system for its citizens and companies. Currently in pilot mode, the new system will be rolled out in 2020, and go through numerous iterations before becoming official.
While the system may be a useful tool for China to manage its growing 1.4 billion population, it has triggered global concerns around the ethics of big data, and whether the system is a breach of fundamental human rights.
Today’s infographic looks at how China’s proposed social credit system could work, and what the implications might be.
The Government is Always Watching
Currently, the pilot system varies from place to place, whereas the new system is envisioned as a unified system. Although the pilot program may be more of an experiment than a precursor, it gives a good indication of what to expect.
In the pilot system, each citizen is assigned 1,000 points and is consistently monitored and rated on how they behave. Points are earned through good deeds, and lost for bad behavior. Users increase points by donating blood or money, praising the government on social media, and helping the poor. Rewards for such behavior can range from getting a promotion at work fast-tracked, to receiving priority status for children’s school admissions.
In contrast, not visiting one’s aging parents regularly, spreading rumors on the internet, and cheating in online games are considered antisocial behaviors. Punishments include public shaming, exclusion from booking flights or train tickets, and restricted access to public services.
Big Data Goes Right to the Source
The perpetual surveillance that comes with the new system is expected to draw on huge amounts of data from a variety of traditional and digital sources.
Police officers have used AI-powered smart glasses and drones to effectively monitor citizens. Footage from these devices showing antisocial behavior can be broadcast to the public to shame the offenders, and deter others from behaving similarly.
For more serious offenders, some cities in China force people to repay debts by switching the person’s ringtone without their permission. The ringtone begins with the sound of a police siren, followed by a message such as:
“The person you are calling has been listed as a discredited person by the local court. Please urge this person to fulfill his or her legal obligations.”
Two of the largest companies in China, Tencent and Alibaba, were enlisted by the People’s Bank of China to play an important role in the credit system, raising the issue of third-party data security. WeChat—China’s largest social media platform, owned by Tencent—tracked behavior and ranked users accordingly, while displaying their location in real-time.
Following data concerns, these tech companies—and six others—were not awarded any licenses by the government. However, social media giants are still involved in orchestrating the public shaming of citizens who misbehave.
The Digital Dang’an
The social credit system may not be an entirely new initiative in China. The dang’an (English: record) is a paper file containing an individual’s school reports, information on physical characteristics, employment records, and photographs.
These dossiers, which were first used in the Maoist years, helped the government in maintaining control of its citizens. This gathering of citizen’s data for China’s social credit system may in fact be seen as a revival of the principle of dang’an in the digital era, with the system providing a powerful tool to monitor citizens whose data is more difficult to capture.
Is the System Working?
In 2018, people with a low score were prohibited from buying plane tickets almost 18 million times, while high-speed train ticket transactions were blocked 5.5 million times. A further 128 people were prohibited from leaving China, due to unpaid taxes.
The system could have major implications for foreign business practices—as preference could be given to companies already ranked in the system. Companies with higher scores will be rewarded with incentives which include lower tax rates and better credit conditions, with their behavior being judged in areas such as:
- Paid taxes
- Customs regulation
- Environmental protection
Despite the complexities of gathering vast amounts of data, the system is certainly making an impact. While there are benefits to having a standardardized scoring system, and encouraging positive behavior—will it be worth the social cost of gamifying human life?
How Many Music Streams Does it Take to Earn a Dollar?
Streaming has breathed new life into the music business, but as new data shows, these services pay out wildly different rates per stream.
How Many Music Streams Does it Take to Earn a Dollar?
A decade ago, the music industry was headed for a protracted fade-out.
The disruptive effects of peer-to-peer file sharing had slashed music revenues in half, casting serious doubts over the future of the industry.
Ringtones provided a brief earnings bump, but it was the growing popularity of premium streaming services that proved to be the savior of record labels and artists. For the first time since the mid-90s, the music industry saw back-to-back years of growth, and revenues grew a brisk 12% in 2018 – nearly reaching $10 billion. In short, people showed they were still willing to pay for music.
Although most forecasts show streaming services like Spotify and Apple Music contributing an increasingly large share of revenue going forward, recent data from The Trichordist reveals that these services pay out wildly different rates per stream.
Note: Due to the lack of publicly available data, calculating payouts from streaming services is not an exact science. This data set is based on revenue from an indie label with a ~150 album catalogue generating over 115 million streams.
Full Stream Ahead
One would expect streaming services to have fairly similar payout rates every time a track is played, but this is not the case. In reality, the streaming rates of major players in the market – which have very similar catalogs – are all over the map. Below is a full breakdown of how many streams it takes to earn a dollar on various platforms:
|Streaming service||Avg. payout per stream||# of streams to earn one dollar||# of streams to earn minimum wage*|
|Google Play Music||$0.00676||147||217,751|
*U.S. monthly minimum wage of $1,472 **Premium tier
Napster, once public enemy number one in the music business, has some of the most generous streaming rates in the industry. On the downside, the brand currently has a market share of less than 1%, so getting a high volume of plays on an album isn’t likely to happen for most artists.
On the flip side of the equation, YouTube has the highest number of plays per song, but the lowest payout per stream by far. It takes almost 1,500 plays to earn a single dollar on the Google-owned video platform.
Spotify, which is now the biggest player in the streaming market, is on the mid-to-low end of the compensation spectrum.
The Payment Pipeline
How do companies like Spotify calculate the amount paid out to license holders? Here’s a look at their payout process:
As this chart reveals, dollars earned from streaming still don’t tell the full story of how much artists receive at the end of the line. This amount is influenced by whether or not the performer has a record deal, and if other contributors have a stake in the recorded work.
The Pressure is Heating Up
When Spotify was a scrappy startup providing a much needed revenue stream to the music industry, labels were temporarily willing to accept lower streaming rates.
But now that Spotify is a public company, and tech giants like Apple and Amazon are in the picture, a growing chorus of industry players will likely dial up the pressure to increase compensation rates.
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