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Accurate models of real quantum systems are important for investigating theirbehaviour, yet are difficult to distill empirically. Here, we report analgorithm -- the Quantum Model Learning Agent (QMLA) -- to reverse engineerHamiltonian descriptions of a target system. We test the performance of QMLA ona number of simulated experiments, demonstrating several mechanisms for thedesign of candidate Hamiltonian models and simultaneously entertaining numeroushypotheses about the nature of the physical interactions governing the systemunder study. QMLA is shown to identify the true model in the majority ofinstances, when provided with limited a priori information, and control of theexperimental setup. Our protocol can explore Ising, Heisenberg and Hubbardfamilies of models in parallel, reliably identifying the family which bestdescribes the system dynamics. We demonstrate QMLA operating on large modelspaces by incorporating a genetic algorithm to formulate new hypotheticalmodels. The selection of models whose features propagate to the next generationis based upon an objective function inspired by the Elo rating scheme,typically used to rate competitors in games such as chess and football. In allinstances, our protocol finds models that exhibit $F_1$-score $\geq 0.88$ whencompared with the true model, and it precisely identifies the true model in 72%of cases, whilst exploring a space of over $250,000$ potential models. Bytesting which interactions actually occur in the target system, QMLA is aviable tool for both the exploration of fundamental physics and thecharacterisation and calibration of quantum devices.

Quantum Model Learning Agent: characterisation of quantum systems through machine learning