A sample-optimal quantum state tomography algorithm that is memory-efficient by using unitary Schur sampling with streaming access to samples.
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Tensor cross interpolation learns entanglement features of quantum states with polynomial samples assuming finite MPS bond dimension.
Geometric partitioning of lattice Hamiltonians into local patches enables energy measurements in patch eigenbases, producing lower-variance estimators than Pauli grouping for eigenstates with rigorous guarantees even under depolarizing noise.
The paper proves sample complexity bounds showing that any efficiently representable unitary can be learned incoherently with arbitrary measurements, but only low-entangling unitaries with shallow-depth measurements, and demonstrates this on a 16-qubit hardware device.
citing papers explorer
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Sample Optimal and Memory Efficient Quantum State Tomography
A sample-optimal quantum state tomography algorithm that is memory-efficient by using unitary Schur sampling with streaming access to samples.
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Tensor Cross Interpolation of Purities in Quantum Many-Body Systems
Tensor cross interpolation learns entanglement features of quantum states with polynomial samples assuming finite MPS bond dimension.
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Shot-noise reduction for lattice Hamiltonians
Geometric partitioning of lattice Hamiltonians into local patches enables energy measurements in patch eigenbases, producing lower-variance estimators than Pauli grouping for eigenstates with rigorous guarantees even under depolarizing noise.
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The power and limitations of learning quantum dynamics incoherently
The paper proves sample complexity bounds showing that any efficiently representable unitary can be learned incoherently with arbitrary measurements, but only low-entangling unitaries with shallow-depth measurements, and demonstrates this on a 16-qubit hardware device.