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

Abstract

Advancements in the implementation of quantum hardware have enabled the acquisition of data that are intractable for emulation with classical computers. The integration of classical machine learning (ML) algorithms with these data holds potential for unveiling obscure patterns. Although this hybrid approach extends the class of efficiently solvable problems compared to using only classical computers, this approach has been realized for solving restricted problems because of the prevalence of noise in current quantum computers. Here, we extend the applicability of the hybrid approach to problems of interest in many-body physics, such as predicting the properties of the ground state of a given Hamiltonian and classifying quantum phases. By performing experiments with various error-reducing procedures on superconducting quantum hardware with 127 qubits, we managed to acquire refined data from the quantum computer. This enabled us to demonstrate the successful implementation of classical ML algorithms for systems with up to 44 qubits. Our results verify the scalability and effectiveness of the classical ML algorithms for processing quantum experimental data.

arXiv comment25 pages, 5 figures; supplementary information 35 pages, 17 figures, 1 table

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Cited by in corpus (8)

• Unveiling quantum phase transitions from traps in variational quantum algorithms
• Unsupervised Detection of Topological Phase Transitions with a Quantum Reservoir
• Accelerating Quantum Eigensolver Algorithms With Machine Learning
• Coarse-grained dynamics in quantum many-body systems using the maximum entropy principle
• Artificial intelligence for representing and characterizing quantum systems
• A Unsupervised Framework for Identifying Diverse Quantum Phase Transitions Using Classical Shadow Tomography
• Quantum feature-map learning with reduced resource overhead
• Physics-informed neural networks for solving saddle-point equations in strong-field physics with tailored fields