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Title    Machine Learning Algorithm Helps Unravel the Physics Underlying Quantum Systems
Date    Monday May 03, @02:31PM
Author    Fnord666
from the dept.

upstart writes in with an IRC submission for c0lo:

Machine learning algorithm helps unravel the physics underlying quantum systems:

Scientists from the University's Quantum Engineering Technology Labs (QETLabs) have developed an algorithm that provides valuable insights into the physics underlying quantum systems - paving the way for significant advances in quantum computation and sensing, and potentially turning a new page in scientific investigation.

[...] In the paper, Learning models of quantum systems from experiments,published in Nature Physics, quantum mechanics from Bristol's QET Labs describe an algorithm which overcomes these challenges by acting as an autonomous agent, using machine learning to reverse engineer Hamiltonian models.

The team developed a new protocol to formulate and validate approximate models for quantum systems of interest. Their algorithm works autonomously, designing and performing experiments on the targeted quantum system, with the resultant data being fed back into the algorithm. It proposes candidate Hamiltonian models to describe the target system, and distinguishes between them using statistical metrics, namely Bayes factors.

[...] "Combining the power of today's supercomputers with machine learning, we were able to automatically discover structure in quantum systems. As new quantum computers/simulators become available, the algorithm becomes more exciting: first it can help to verify the performance of the device itself, then exploit those devices to understand ever-larger systems," said Brian Flynn from the University of Bristol's QETLabs and Quantum Engineering Centre for Doctoral Training.

"This level of automation makes it possible to entertain myriads of hypothetical models before selecting an optimal one, a task that would be otherwise daunting for systems whose complexity is ever increasing," said Andreas Gentile, formerly of Bristol's QETLabs, now at Qu & Co.

[...] The next step for the research is to extend the algorithm to explore larger systems, and different classes of quantum models which represent different physical regimes or underlying structures.

Journal Reference:
Antonio A. Gentile, Brian Flynn, Sebastian Knauer, et al. Learning models of quantum systems from experiments, Nature Physics (DOI: 10.1038/s41567-021-01201-7)

Original Submission


  1. "upstart" -
  2. "Machine learning algorithm helps unravel the physics underlying quantum systems" -
  3. "Learning models of quantum systems from experiments" -
  4. "Nature Physics" -
  5. "Bristol's QET Labs" -
  6. "QETLabs" -
  7. "Quantum Engineering Centre for Doctoral Training" -
  8. "10.1038/s41567-021-01201-7" -
  9. "Original Submission" -

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