The machine learning software enables a computer to “learn” the quantum state of a complex physical system based on experimental observations and predict the outcomes of hypothetical measurements, according to Guiseppe Carleo, a physicist at ETH Zurich who led the international team of researchers. Conventional methods would require around one million measurements to achieve such a desired accuracy.
“What we do, in a nutshell, is like teaching the computer to imitate my handwriting. We will show it a bunch of written samples, and step-by-step it then learns to replicate all my a’s, l’s and so forth,” said Carleo.
The ETH software has important implications for future quantum technologies, because being able to study quantum systems with a large number of components – known as qubits – makes it possible to test the accuracy of quantum computers.
“If we want to test quantum computers with more than a handful of qubits, that won’t be possible with conventional means because of the exponential scaling. Our machine learning approach, however, should put us in a position to test quantum computers with as many as 100 qubits,” said Carleo.
The machine learning software can also help experimental physicists perform virtual measurements that would be hard to do in the laboratory.
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