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Advancements in the implementation of quantum hardware have enabled theacquisition of data that are intractable for emulation with classicalcomputers. The integration of classical machine learning (ML) algorithms withthese data holds potential for unveiling obscure patterns. Although this hybridapproach extends the class of efficiently solvable problems compared to usingonly classical computers, this approach has been realized for solvingrestricted problems because of the prevalence of noise in current quantumcomputers. Here, we extend the applicability of the hybrid approach to problemsof interest in many-body physics, such as predicting the properties of theground state of a given Hamiltonian and classifying quantum phases. Byperforming experiments with various error-reducing procedures onsuperconducting quantum hardware with 127 qubits, we managed to acquire refineddata from the quantum computer. This enabled us to demonstrate the successfulimplementation of classical ML algorithms for systems with up to 44 qubits. Ourresults verify the scalability and effectiveness of the classical ML algorithmsfor processing quantum experimental data.

Machine learning on quantum experimental data toward solving quantum many-body problems