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First Demonstration of Artificial Intelligence On a Quantum Computer
KentuckyFC writes: Machine learning algorithms use a training dataset to learn how to recognize features in images and use this 'knowledge' to spot the same features in new images. The computational complexity of this task is such that the time required to solve it increases in polynomial time with the number of images in the training set and the complexity of the "learned" feature. So it's no surprise that quantum computers ought to be able to rapidly speed up this process. Indeed, a group of theoretical physicists last year designed a quantum algorithm that solves this problem in logarithmic time rather than polynomial, a significant improvement.
Now, a Chinese team has successfully implemented this artificial intelligence algorithm on a working quantum computer, for the first time. The information processor is a standard nuclear magnetic resonance quantum computer capable of handling 4 qubits. The team trained it to recognize the difference between the characters '6' and '9' and then asked it to classify a set of handwritten 6s and 9s accordingly, which it did successfully. The team says this is the first time that this kind of artificial intelligence has ever been demonstrated on a quantum computer and opens the way to the more rapid processing of other big data sets — provided, of course, that physicists can build more powerful quantum computers.
Now, a Chinese team has successfully implemented this artificial intelligence algorithm on a working quantum computer, for the first time. The information processor is a standard nuclear magnetic resonance quantum computer capable of handling 4 qubits. The team trained it to recognize the difference between the characters '6' and '9' and then asked it to classify a set of handwritten 6s and 9s accordingly, which it did successfully. The team says this is the first time that this kind of artificial intelligence has ever been demonstrated on a quantum computer and opens the way to the more rapid processing of other big data sets — provided, of course, that physicists can build more powerful quantum computers.
And also no working quantum computer, except for very small toys. Pattern recognition is not AI.
Most ACs are not even worth the keystrokes to insult them. Be generically insulted by this and ignored otherwise.
What they actually did if you read the paper is:
1) Encode a 6 or 9 image into 2 numbers, based on the number of excess pixels in the left vs right, and top vs bottom quadrants. From the article: After these preprocessing, the two printed image with standard font can be represented by ~x1= (0:9872;0:1595) for character "6" and ~x2= (0:3544;0:9351) for character "9"
2) Use a training algorithm to find the appropriate pulse sequence to give a up result from the molecule's NMR C13 spectra from a 6, and a down signal from a 9.
3) Run the NMR spectrum, feed in pulses based on the parameters produced from pixels encoded in a vector form like 1), get the result of "up" for a 6 and "down" for a 9.
It's certainly neat experimental NMR work, but I don't really see how it's quantum computing. But then maybe that's the NMR spectroscopist in me talking....