In industry, neural networks are often used to mine big data. “The idea is to build a general and reliable tool to assist the development of the next generation of quantum simulators and noisy intermediate-scale quantum technologies.” RETOOLING AI “Our algorithm only requires raw data obtained from simple measurements accessible in experiments,” Torlai said. Unsupervised learning of the quantum wavefunction
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The AI learned how to combine the measurements of the quantum hardware to create its complete quantum mechanical description.įigure 1. They embraced unsupervised machine learning – essentially, an AI that learns for itself. In the new paper, “ Neural-network quantum state tomography,” Perimeter Associate graduate student Giacomo Torlai, Perimeter Associate Faculty member Roger Melko, and collaborators applied the same process, but had a cutting-edge AI neural network do the heavy lifting. It is a significant challenge because of the huge number of incomplete snapshots required to perform the reconstruction. This “reverse-engineering” approach is performed with complex algorithms that require a considerable amount of data input and manipulation.
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Using imperfect snapshots of the system as a starting point, researchers mathematically backtrack until they can ascertain the full quantum state at the moment the measurements were taken. Until we can do that, our ability to scale the small quantum devices that we do have, or manufacture more robust quantum computing hardware, remains limited.Ĭurrently, we ascertain the state of quantum devices using a process called “quantum state tomography,” or QST. Why is it worth the hassle? Because this ability – to know, and therefore exploit, a quantum state – is crucial to quantum computing. (“This is partly due to the uncertainty principle and mostly just due to the nature of quantum mechanics itself,” noted Perimeter PhD student Barak Shoshany in an answer on Quora.) However, you can only extract some information at any one time. The state of a quantum system contains all the information about that system. The team applied an artificial intelligence (AI) to a particular problem in quantum computing: working out the state of a quantum device using only snapshots of data gleaned from experimental measurements.
This pas de deux recently took one more step thanks to a new paper published in Nature Physics by researchers at Perimeter Institute and the University of Waterloo, with collaborators at ETH Zurich, Microsoft Station Q, D-Wave Systems, and the Vector Institute for Artificial Intelligence. It takes the best in classical computing to advance quantum capabilities, yet each leap in quantum computation demands more of its classical partner. The effort to advance these different technologies – which both fuel and rely on each other – resembles something like a dance. We use classical computation and simulations to develop and design today’s nascent quantum devices, which are then nested within a much larger framework of classical computers. In reality, though, the two are inextricably linked. In popular culture, quantum computing is often painted as an ultra-powerful technology that will first outrace, and then replace, its classical counterpart. This article originally appeared in “ Inside the Perimeter”
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