Machine Learning Algorithm Helps Unravel the Physics Underlying Quantum Systems

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.

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