Inside Quantum Technology

UofBristol’s QET Labs Develops Algorithm that Leverages Machine Learning that Leverages Quantum Systems

(UofBristolNews) Researchers from the University of Bristol’s Quantum Engineering Technology Labs (QETLabs) have developed an algorithm that leverages machine learning to help characterize quantum systems. Building models of quantum systems has long been computationally challenging. By feeding experiment results to the ML-informed simulation, the researchers were able develop increasingly accurate models of the system. This should prove useful when applied to a variety of quantum systems and also provide insight for using such models on real-world quantum computers and sensing devices.
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.
The team was able to successfully demonstrate the algorithm’s ability on a real-life quantum experiment involving defect centres in a diamond, a well-studied platform for quantum information processing and quantum sensing.
The algorithm could be used to aid automated characterisation of new devices, such as quantum sensors. This development therefore represents a significant breakthrough in the development of quantum technologies.
“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.

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