AI News, Physics boosts artificial intelligence methods

Physics boosts artificial intelligence methods

Despite the central role of physics in quantum computing, until now, no problem of interest for physics researchers has been resolved by quantum computing techniques.

In this new work, the researchers successfully extracted meaningful information about Higgs particles by programming a quantum annealer -- a type of quantum computer capable of only running optimization tasks -- to sort through particle-measurement data littered with errors.

popular computing technique for classifying data is the neural network method, known for its efficiency in extracting obscure patterns within a dataset.

'Some people in high-energy physics are getting ahead of themselves about neural nets, but neural nets aren't easily interpretable to a physicist,' says USC's physics graduate student Joshua Job, co-author of the paper and guest student at Caltech.

The new quantum program is 'a simple machine learning model that achieves a result comparable to more complicated models without losing robustness or interpretability,' says Job.

With prior techniques, the accuracy of classification depends strongly on the size and quality of a training set, which is a manually sorted portion of the dataset.

Modeling the problem in a way that a quantum annealer can understand proved to be a substantial challenge that was successfully tackled by Spiropulu's former graduate student at Caltech, Alex Mott (PhD '15), who is now at DeepMind.

'It's because we're comparing a thousand qubits -- quantum bits of information -- to a billion transistors,' says Jean-Roch Vlimant, a postdoctoral scholar in high energy physics at Caltech.

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