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University of Bristol’s Quantum Engineering Technology Labs (QETLabs) researchers have developed a machine learning algorithm that provides insights into the physics underlying quantum systems.
It is noted that the algorithm uses machine learning to reverse engineer the Hamiltonian model, which is a mathematic model to describe how systems of particles interact with each other at the quantum mechanical level.
A new protocol has been developed by researchers to formulate and validate approximate models for quantum systems of interest.
It autonomously designs and performs experiments on the targeted quantum system and feeds the resulting data back into the algorithm.
The algorithm proposes Hamiltonian models to describe the target system and distinguishes between them using statistical metrics in the form of Bayes factors.
Scientists have successfully demonstrated the process using a real-life quantum experiment on defect centres in a diamond.
University of Bristol’s QETLabs participant Brian Flynn said: “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.”
Bristol’s QETLabs co-director Anthony Laing said: “In the past we have relied on the genius and hard work of scientists to uncover new physics. Here the team have potentially turned a new page in scientific investigation by bestowing machines with the capability to learn from experiments and discover new physics. The consequences could be far reaching indeed.”
QETLabs’ scientists stated that the algorithm can be used to support automated characterisation of new devices including quantum sensors.
Furthermore, their next step is to apply this algorithm to larger systems and different classes of quantum models.
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