The Quantum Computing Era

Category Science

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Quantum computing is the new field of computing in which specialized computers use light and matter to quickly solve tasks such as cryptography, material science, optimization, and machine learning. Australia is the first country to develop silicon-based quantum computers, ushering in the quantum computing era.


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In June, an IBM computing executive claimed quantum computers were entering the "utility" phase, in which high-tech experimental devices become useful. In September, Australia’s chief scientist Cathy Foley went so far as to declare "the dawn of the quantum era." .

This week, Australian physicist Michelle Simmons won the nation’s top science award for her work on developing silicon-based quantum computers.

Australia is the first country to have a quantum machine capable of performing commercial calculations

Obviously, quantum computers are having a moment. But—to step back a little—what exactly are they? .

What Is a Quantum Computer? .

One way to think about computers is in terms of the kinds of numbers they work with.

The digital computers we use every day rely on whole numbers (or integers), representing information as strings of zeroes and ones which they rearrange according to complicated rules. There are also analog computers, which represent information as continuously varying numbers (or real numbers), manipulated via electrical circuits or spinning rotors or moving fluids.

The first use of quantum computing would be in cryptography to develop secure encryption

In the 16th century, the Italian mathematician Girolamo Cardano invented another kind of number called complex numbers to solve seemingly impossible tasks such as finding the square root of a negative number. In the 20th century, with the advent of quantum physics, it turned out complex numbers also naturally describe the fine details of light and matter.

In the 1990s, physics and computer science collided when it was discovered that some problems could be solved much faster with algorithms that work directly with complex numbers as encoded in quantum physics.

Quantum computers can simulate molecular structures at the atomic scale

The next logical step was to build devices that work with light and matter to do those calculations for us automatically. This was the birth of quantum computing.

Why Does Quantum Computing Matter? .

We usually think of the things our computers do in terms that mean something to us— balance my spreadsheet, transmit my live video, find my ride to the airport. However, all of these are ultimately computational problems, phrased in mathematical language.

Quantum computing can speed up optimization problems for logistics, finance, and weather forecasting

As quantum computing is still a nascent field, most of the problems we know quantum computers will solve are phrased in abstract mathematics. Some of these will have "real world" applications we can’t yet foresee, but others will find a more immediate impact.

One early application will be cryptography. Quantum computers will be able to crack today’s internet encryption algorithms, so we will need quantum-resistant cryptographic technology. Provably secure cryptography and a fully quantum internet would use quantum computing technology.

Quantum computers can be used in machine learning algorithms to create more advanced models

In materials science, quantum computers will be able to simulate molecular structures at the atomic scale, making it faster and easier to discover new and interesting materials. This may have significant applications in batteries, pharmaceuticals, fertilizers, and other chemistry-based domains.

Quantum computers will also speed up many difficult optimization problems, where we want to find the "best" way to do something. This will allow us to tackle larger-scale problems in areas such as logistics, finance, and weather forecasting.

Google's Sycamore machine is believed to be the first machine to achieve 'quantum supremacy' in 2019

Machine learning is another area where quantum computers may accelearte already impressive progress. Most machine-learning algorithms rely on probability theory; with a quantum probabilistic engine, these algorithms may take on whole new layers of complexity.


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