Researchers from Caltech and the University Of Southern California report the first application of quantum computing to a physics problem. By employing quantum-compatible machine learning techniques, they developed a method of extracting a rare Higgs boson signal from copious noise data.
In this new work, the researcher’s successfully extracted meaningful information about Higgs particles by programming a quantum annealed – a type of quantum computer capable of only running optimization tasks — to sort through particle-measurement data littered with errors. Caltech’s Maria Spiropulu, the Shang-Yi Ch’in Professor of Physics, conceived the project and collaborated with Daniel Lieder, pioneer of the quantum machine learning methodology and Viterbi Professor of Engineering at USC who is also a Distinguished Moore Scholar in Caltech’s division of Physics, Mathematics and Astronomy.
The quantum program seeks patterns within a dataset to tell meaningful data from junk. The details of the program as well as comparisons to existing techniques are detailed in a paper published on October 19 in the journal Nature.
The patterns identified by neural networks are difficult to interpret, as the classification process does not reveal how they were discovered. Techniques that lead to better interpretability are often more error prone and less efficient.
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.
This is problematic for high-energy physics research, which revolves around rare events buried in large amount of noise data.
The Large Hadron Collider generates a huge number of events, and the particle physicists have to look at small packets of data to figure out which are interesting,” says Job. The new quantum program “is simpler, takes very little training data, and could even be faster.
Programming quantum computers is fundamentally different from programming classical computers. It’s like coding bits directly. The entire problem has to be encoded at once, and then it runs just once as programmed, says Mott.
The ones currently available are simply not big enough to even encode physics problems difficult enough to demonstrate any advantage, says Spiropulu. The complexity of simulated annealing will explode at some point, and we hope that quantum annealing will also offer speedup,” says Valiant.
We were able to demonstrate a very similar result in a completely different application domain by applying the same methodology to a problem in computational biology, say Lieder. There is another project on particle-tracking improvements using such methods, and we’re looking for new ways to examine charged particles, says Valiant.