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Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms
In this work, we introduce two approaches that achieve a constant sample complexity, independent of system size $n$, for learning ground state properties.
Learning quantum states and unitaries of bounded gate complexity
While quantum state tomography is notoriously hard, most states hold little interest to practically-minded tomographers.
Improved machine learning algorithm for predicting ground state properties
Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics.
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