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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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Charted: The Jobs Most Impacted by AI

We visualized the results of an analysis by the World Economic Forum, which uncovered the jobs most impacted by AI.

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Charted: The Jobs Most Impacted by AI

This was originally posted on our Voronoi app. Download the app for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.

Large language models (LLMs) and other generative AI tools haven’t been around for very long, but they’re expected to have far-reaching impacts on the way people do their jobs. With this in mind, researchers have already begun studying the potential impacts of this transformative technology.

In this graphic, we’ve visualized the results of a World Economic Forum report, which estimated how different job departments will be exposed to AI disruption.

Data and Methodology

To identify the job departments most impacted by AI, researchers assessed over 19,000 occupational tasks (e.g. reading documents) to determine if they relied on language. If a task was deemed language-based, it was then determined how much human involvement was needed to complete that task.

With this analysis, researchers were then able to estimate how AI would impact different occupational groups.

DepartmentLarge impact (%)Small impact (%)No impact (%)
IT73261
Finance70219
Customer Sales671617
Operations651817
HR57412
Marketing56413
Legal46504
Supply Chain431839

In our graphic, large impact refers to tasks that will be fully automated or significantly altered by AI technologies. Small impact refers to tasks that have a lesser potential for disruption.

Where AI will make the biggest impact

Jobs in information technology (IT) and finance have the highest share of tasks expected to be largely impacted by AI.

Within IT, tasks that are expected to be automated include software quality assurance and customer support. On the finance side, researchers believe that AI could be significantly useful for bookkeeping, accounting, and auditing.

Still interested in AI? Check out this graphic which ranked the most commonly used AI tools in 2023.

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