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Scalable Quantum Computer Designed and Built


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Quantum computers are an amazing technology that have existed within science fiction and scientific theories for a long time, but over recent years have actually started becoming reality. By leveraging the curious phenomena of quantum mechanics, these computers can quickly perform operations modern classical computers could require millennia to finish. One potential use for quantum computers is factoring large numbers, like those used to encrypt data, and now researchers at MIT and the University of Innsbruck have developed a quantum computer that should be scalable.

First developed in 1994, Shor's algorithm is capable of very efficiently factoring large numbers, and for that reason it grabbed the attention of many researchers. It is also the most complex quantum algorithm to date. Previous attempts to build quantum computers that can run it have typically required 12 qubits, or quantum bits to successfully factor 15. This new design, however, is able to do it with just five qubits. These qubits are each single atoms that are held in a superposition of different energy states simultaneously. By removing an electron from each, giving the atoms a charge, and holding them in an electric field, the researchers are able to track where they are located and use laser pulses to perform operations on them. Four of the atoms actually have the operations run on them, while the fifth is used to store, forward, extract, and recycle the results, which is what allows so fewer qubits to be used.

The scalability of this design comes from how the atoms are held close enough together that they can interact with each other, but do not cause their quantum states to collapse. As technology improves, allowing more atoms to be held in this trap and more laser pulses to be used simultaneously, the computer can grow in size and power. It will still be a long time before a quantum computer will be able to factor the large numbers we use today for encryption, but that day is a bit closer now than it was.

Source: MIT

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