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The 3 Types of Quantum Computers and Their Applications

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The 3 Types of Quantum Computers and Their Applications

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.

Qubits explained
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:

Tech hype cycle

Gartner still describes it as being “10 years or more” away from reaching the plateau.

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Bitcoin

Mapping the Major Bitcoin Forks

Bitcoin forks play a key role in Bitcoin’s evolution as a blockchain. While some have sparked controversy, most Bitcoin forks have been a sign of growth.

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Mapping the Major Bitcoin Forks

The emergence of Bitcoin took the world by storm through its simplicity and innovation. Yet, plenty of confusion remains around the term itself.

The Bitcoin blockchain—not to be confused with the bitcoin cryptocurrency—involves a vast global network of computers operating on the same distributed database to process massive volumes of data every second.

These transactions tell the network how to alter this distributed database in real-time, which makes it crucial for everyone to agree on how these changes should be applied. When the community can’t come to a mutual agreement on what changes, or when such rule changes should take effect, it results in a blockchain fork.

Today’s unique subway-style map by Bitcoin Magazine shows the dramatic and major forks that have occurred for Bitcoin. But what exactly is a Blockchain fork?

Types of Blockchain Forks

Forks are common practice in the software industry and happen for one of two reasons:

  • Split consensus within the community
    These forks are generally disregarded by the community because they are temporary, except in extreme cases. The longer of the two chains is used to continue building the blockchain.
  • Changes to the underlying rules of the blockchain
    A permanent fork which requires an upgrade to the current software in order to continue participating in the network.

There are four major types of forks that can occur:

1. Soft Forks

Soft forks are like gradual software upgrades—bug fixes, security checks, and new features—for those that upgrade right away.

These forks are “backwards compatible” with the older software; users who haven’t upgraded still have access to the network but may not be able to use all functionality in the current version.

2. Hard Forks

Hard forks are like a new OS release—upgrading is mandatory to continue using the software. Because of this, hard forks aren’t compatible with older versions of the network.

Hard forks are a permanent division of the blockchain. As long as enough people support both chains, however, they will both continue to exist.

The three types of hard forks are:

  • Planned
    Scheduled upgrades to the network, giving users a chance to prepare. These forks typically involve abandoning the old chain.
  • Contentious
    Caused by disagreements in the community, forming a new chain. This usually involves major changes to the code.
  • Spin-off Coins
    Changes to Bitcoin’s code that create new coins. Litecoin is an example of this—key changes included reducing mining time from 10 minutes to 2.5 minutes, and increasing the coin supply from 21 million to 84 million.

3. Codebase Forks

Codebase forks copy the Bitcoin code, allowing developers to make minor tweaks without having to develop the entire blockchain code from scratch. Codebase forks can create a new cryptocurrency or cause unintentional blockchain forks.

4. Blockchain Forks

Blockchain forks involve branching or splitting a blockchain’s whole transaction history. Outcomes range from “orphan” blocks to new cryptocurrencies.

Splitting off the Bitcoin network to form a new currency is much like a religious schism—while most of the characteristics and history are preserved, a fork causes the new network to develop a distinct identity.

Summarizing Major Bitcoin Forks

Descriptions of major forks that have occurred in the Bitcoin blockchain:

  • Bitcoin / Bitcoin Core
    The first iteration of Bitcoin was launched by Satoshi Nakamoto in 2009. Future generations of Bitcoin (aka Bitcoin 0.1.0) were renamed Bitcoin Core, or Bitcore, as other blockchains and codebases formed.
  • BTC1
    A codebase fork of Bitcoin. Developers released a hard fork protocol called Segwit2x, with the intention of having all Bitcoin users eventually migrate to the Segwit2x protocol. However, it failed to gain traction and is now considered defunct.
  • Bitcoin ABC
    Also a codebase fork of Bitcoin, Bitcoin ABC was intentionally designed to be incompatible with all Bitcoin iterations at some point. ABC branched off to form Bitcoin Cash in 2017.
  • Bitcoin Gold, Bitcoin Diamond, Other Fork Coins
    After the successful yet contentious launch of Bitcoin Cash, other fork coins began to emerge. Unlike the disagreement surrounding Bitcoin Cash, most were simply regarded as a way to create new coins.

Some of the above forks were largely driven by ideology (BTC1), some because of mixed consensus on which direction to take a hard fork (Bitcoin ABC), while others were mainly profit-driven (Bitcoin Clashic)—or a mix of all three.

Where’s the Next Fork in the Road?

Forks are considered an inevitability in the blockchain community. Many believe that forks help ensure that everyone involved—developers, miners, and investors—all have a say when disagreements occur.

Bitcoin has seen its fair share of ups and downs. Crypto investors should be aware that Bitcoin, as both a protocol and a currency, is complex and always evolving. Even among experts, there is disagreement on what constitutes a soft or hard fork, and how certain geopolitical events have played a role in Bitcoin’s evolution.

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Technology

Why Big Data Keeps Getting Bigger

Visualizing the vast amount of data produced every single minute, and why it’s still early days in the big data era of technology.

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Why Big Data Keeps Getting Bigger

The sun never sets on the creation of new data.

Yes, the rate of generation may slow down at night as people send fewer emails and watch fewer videos. But for every person hitting the hay, there is another person on the opposite side of the world that is turning their smartphone on for the day.

As a result, the scale of data being generated—even when we look at it through a limited lens of one minute at a time—is quite mind-boggling to behold.

The Data Explosion, by Source

Today’s infographic comes to us from Domo, and it shows the amount of new data generated each minute through several different platforms and technologies.

Let’s start by looking at what happens every minute from a broad perspective:

  • Americans use 4,416,720 GB of internet data
  • There are 188,000,000 emails sent
  • There are 18,100,000 texts sent
  • There are 390,030 apps downloaded

Now lets look at platform-specific data on a per minute basis:

  • Giphy serves up 4,800,000 gifs
  • Netflix users stream 694,444 hours of video
  • Instagram users post 277,777 stories
  • Youtube users watch 4,500,000 videos
  • Twitter users send 511,200 tweets
  • Skype users make 231,840 calls
  • Airbnb books 1,389 reservations
  • Uber users take 9,772 rides
  • Tinder users swipe 1,400,000 times
  • Google conducts 4,497,420 searches
  • Twitch users view 1,000,000 videos

Imagine being given the task to build a server infrastructure capable of handling any of the above items. It’s a level of scale that’s hard to comprehend.

Also, imagine how difficult it is to make sense of this swath of data. How does one even process insights from the many billions of Youtube videos watched per day?

Why Big Data is Going to Get Even Bigger

The above statistics are already mind-bending, but consider that the global total of internet users is still growing at roughly a 9% clip. This means the current rate of data creation is still just scratching the surface of its ultimate potential.

In fact, as We Are Social’s recent report on internet usage reveals, a staggering 367 million new internet users were added in between January 2018 and January 2019:

Internet user growth

Global internet penetration sits at 57% in 2019, meaning that billions of more people are going to be using the above same services—including many others that don’t even exist yet.

Combine this with more time spent on the internet per user and technologies like 5G, and we are only at the beginning of the big data era.

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