首页|Study Results from Seoul National University Broaden Understanding of Machine Le arning (Machine learning on quantum experimental data toward solving quantum man y-body problems)
Study Results from Seoul National University Broaden Understanding of Machine Le arning (Machine learning on quantum experimental data toward solving quantum man y-body problems)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on artificial in telligence. According to news reporting from Seoul National University by NewsRx journalists, research stated, “Advancements in the implementation of quantum ha rdware have enabled the acquisition of data that are intractable for emulation w ith classical computers.” Funders for this research include National Research Foundation of Korea; Korea B asic Science Institute. The news journalists obtained a quote from the research from Seoul National Univ ersity: “The integration of classical machine learning (ML) algorithms with thes e data holds potential for unveiling obscure patterns. Although this hybrid appr oach extends the class of efficiently solvable problems compared to using only c lassical computers, this approach has been only 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 ma ny-body physics, such as predicting the properties of the ground state of a give n Hamiltonian and classifying quantum phases. By performing experiments with var ious error-reducing procedures on superconducting quantum hardware with 127 qubi ts, we managed to acquire refined data from the quantum computer. This enabled u s to demonstrate the successful implementation of theoretically suggested classi cal ML algorithms for systems with up to 44 qubits.”
Seoul National UniversityAlgorithmsC omputersCyborgsEmerging TechnologiesMachine LearningPhysicsQuantum Com puteQuantum Physics
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