6 papers
Learning the closest Slater determinant
Nisarga Paul, Haimeng Zhao, David D. Dai
Learning compact, interpretable descriptions of quantum many-body states is an important task in quantum science. We study the task of learning the Slater determinant with maximum…
Random Stinespring superchannel: converting channel queries into dilation isometry queries
Filippo Girardi, Francesco Anna Mele, Haimeng Zhao +2
The recently introduced random purification channel, which converts copies of an arbitrary mixed quantum state into copies of the same uniformly random purification, has em…
Exponential quantum advantage in processing massive classical data
Haimeng Zhao, Alexander Zlokapa, Hartmut Neven +4
Broadly applicable quantum advantage, particularly in classical data processing and machine learning, has been a fundamental open problem. In this work, we prove that a small quant…
Learning to erase quantum states: thermodynamic implications of quantum learning theory
Haimeng Zhao, Yuzhen Zhang, John Preskill
The energy cost of erasing quantum states depends on our knowledge of the states. We show that learning algorithms can acquire such knowledge to erase many copies of an unknown sta…
Entanglement-induced provable and robust quantum learning advantages
Haimeng Zhao, Dong-Ling Deng
Quantum computing holds unparalleled potentials to enhance machine learning. However, a demonstration of quantum learning advantage has not been achieved so far. We make a step for…
Learning quantum states and unitaries of bounded gate complexity
Haimeng Zhao, Laura Lewis, Ishaan Kannan +3
While quantum state tomography is notoriously hard, most states hold little interest to practically-minded tomographers. Given that states and unitaries appearing in Nature are of…