‘As Hamiltonian models underpin the study and analysis of physical and chemical processes, it is
‘When using experimental data, 74% of protocol instances retrieve models that are deemed plausible. Simulated multi-spin systems, characterized by a space of 1010 possible models, are also investigated by incorporating a genetic algorithm in our protocol, which identifies the target model in 85% of instances.’
‘The development of automated agents, capable of formulating and testing modelling hypotheses from limited prior assumptions, represents a fundamental step towards the characterization of large quantum systems.’
The algorithm could be used to aid automated characterization of new devices, such as quantum sensors.
“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,” says researcher Brian Flynn.