An Improved Sample Complexity Lower Bound for (Fidelity) Quantum State Tomography
arXiv:2206.11185 · doi:10.22331/q-2023-01-03-890
Abstract
We show that copies of an unknown rank-, dimension- quantum mixed state are necessary in order to learn a classical description with fidelity. This improves upon the tomography lower bounds obtained by Haah, et al. and Wright (when closeness is measured with respect to the fidelity function).
3 pages. Comments welcome
References in corpus (1)
Cited by in corpus (13)
- Quantum circuits for measuring weak values, Kirkwood--Dirac quasiprobability distributions, and state spectra
- Learning quantum states and unitaries of bounded gate complexity
- Transition Role of Entangled Data in Quantum Machine Learning
- Quantum Neural Estimation of Entropies
- Quantum chi-squared tomography and mutual information testing
- Hückel Molecular Orbital Theory on a Quantum Computer: A Scalable System-Agnostic Variational Implementation with Compact Encoding
- Empirical Sample Complexity of Neural Network Mixed State Reconstruction
- Quantum Tensor Product Decomposition from Choi State Tomography
- Hardness of observing strong-to-weak symmetry breaking
- Certifying nonstabilizerness in quantum processors
- The additivity of states uniquely determined by marginals
- Sample Optimal and Memory Efficient Quantum State Tomography
- Performance Guarantees for Quantum Neural Estimation of Entropies