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The 3 Types of Quantum Computers and Their Applications



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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Nvidia Joins the Trillion Dollar Club

America’s biggest chipmaker Nvidia has joined the trillion dollar club as advancements in AI move at lightning speed.



Nvidia Joins the Trillion Dollar Club

Chipmaker Nvidia is now worth nearly as much as Amazon.

America’s largest semiconductor company has vaulted past the $1 trillion market capitalization mark, a milestone reached by just a handful of companies including Apple, Amazon, and Microsoft. While many of these are household names, Nvidia has only recently gained widespread attention amid the AI boom.

The above graphic compares Nvidia to the seven companies that have reached the trillion dollar club.

Riding the AI Wave

Nvidia’s market cap has more than doubled in 2023 to over $1 trillion.

The company designs semiconductor chips that are made of silicon slices that contain specific patterns. Just like you flip an electrical switch by turning on a light at home, these chips have billions of switches that process complex information simultaneously.

Today, they are integral to many AI functions—from OpenAI’s ChatGPT to image generation. Here’s how Nvidia stands up against companies that have achieved the trillion dollar milestone:

Joined ClubMarket Cap
in trillions
Peak Market Cap
in trillions
AppleAug 2018$2.78$2.94
MicrosoftApr 2019$2.47$2.58
AramcoDec 2019$2.06$2.45
AlphabetJul 2020$1.58$1.98
AmazonApr 2020$1.25$1.88
MetaJun 2021$0.68$1.07
TeslaOct 2021$0.63$1.23
NvidiaMay 2023$1.02$1.02

Note: Market caps as of May 30th, 2023

After posting record sales, the company added $184 billion to its market value in one day. Only two other companies have exceeded this number: Amazon ($191 billion), and Apple ($191 billion).

As Nvidia’s market cap reaches new heights, many are wondering if its explosive growth will continue—or if the AI craze is merely temporary. There are cases to be made on both sides.

Bull Case Scenario

Big tech companies are racing to develop capabilities like OpenAI. These types of generative AI require vastly higher amounts of computing power, especially as they become more sophisticated.

Many tech giants, including Google and Microsoft use Nvidia chips to power their AI operations. Consider how Google plans to use generative AI in six products in the future. Each of these have over 2 billion users.

Nvidia has also launched new products days since its stratospheric rise, spanning from robotics to gaming. Leading the way is the A100, a powerful graphics processing unit (GPU) well-suited for machine learning. Additionally, it announced a new supercomputer platform that Google, Microsoft, and Meta are first in line for. Overall, 65,000 companies globally use the company’s chips for a wide range of functions.

Bear Case Scenario

While extreme investor optimism has launched Nvidia to record highs, how do some of its fundamental valuations stack up to other giants?

As the table below shows, its price to earnings (P/E) ratio is second-only to Amazon, at 214.4. This shows how much a shareholder pays compared to the earnings of a company. Here, the company’s share price is over 200 times its earnings on a per share basis.

P/E RatioNet Profit Margin (Annual)

Consider how this looks for revenue of Nvidia compared to other big tech names:

For some, Nvidia’s valuation seems unrealistic even in spite of the prospects of AI. While Nvidia has $11 billion in projected revenue for the next quarter, it would still mean significantly higher multiples than its big tech peers. This suggests the company is overvalued at current prices.

Nvidia’s Growth: Will it Last?

This is not the first time Nvidia’s market cap has rocketed up.

During the crypto rally of 2021, its share price skyrocketed over 100% as demand for its GPUs increased. These specialist chips help mine cryptocurrency, and a jump in demand led to a shortage of chips at the time.

As cryptocurrencies lost their lustre, Nvidia’s share price sank over 46% the following year.

By comparison, AI advancements could have more transformative power. Big tech is rushing to partner with Nvidia, potentially reshaping everything from search to advertising.

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