Fast state tomography with optimal error bounds
Projected least squares is an intuitive and numerically cheap technique for quantum state
tomography: compute the least-squares estimator and project it onto the space of states. The …
tomography: compute the least-squares estimator and project it onto the space of states. The …
ROP: Matrix recovery via rank-one projections
ROP: Matrix recovery via rank-one projections Page 1 The Annals of Statistics 2015, Vol. 43, No.
1, 102–138 DOI: 10.1214/14-AOS1267 © Institute of Mathematical Statistics, 2015 ROP: MATRIX …
1, 102–138 DOI: 10.1214/14-AOS1267 © Institute of Mathematical Statistics, 2015 ROP: MATRIX …
Optimal large-scale quantum state tomography with Pauli measurements
Quantum state tomography aims to determine the state of a quantum system as represented
by a density matrix. It is a fundamental task in modern scientific studies involving quantum …
by a density matrix. It is a fundamental task in modern scientific studies involving quantum …
Pseudo-Bayesian quantum tomography with rank-adaptation
Quantum state tomography, an important task in quantum information processing, aims at
reconstructing a state from prepared measurement data. Bayesian methods are recognized …
reconstructing a state from prepared measurement data. Bayesian methods are recognized …
Rank-based model selection for multiple ions quantum tomography
The statistical analysis of measurement data has become a key component of many
quantum engineering experiments. As standard full state tomography becomes unfeasible …
quantum engineering experiments. As standard full state tomography becomes unfeasible …
Bayesian methods for low-rank matrix estimation: short survey and theoretical study
P Alquier - … Learning Theory: 24th International Conference, ALT …, 2013 - Springer
The problem of low-rank matrix estimation recently received a lot of attention due to
challenging applications. A lot of work has been done on rank-penalized methods [1] and …
challenging applications. A lot of work has been done on rank-penalized methods [1] and …
Quantum tomography by regularized linear regressions
In this paper, we study extended linear regression approaches for quantum state
tomography based on regularization techniques. For unknown quantum states represented …
tomography based on regularization techniques. For unknown quantum states represented …
Spectral thresholding quantum tomography for low rank states
The estimation of high dimensional quantum states is an important statistical problem arising
in current quantum technology applications. A key example is the tomography of multiple …
in current quantum technology applications. A key example is the tomography of multiple …
Estimation of low rank density matrices: bounds in schatten norms and other distances
D Xia, V Koltchinskii - 2016 - projecteuclid.org
Let S_m be the set of all m*m density matrices (Hermitian positively semi-definite matrices of
unit trace). Consider a problem of estimation of an unknown density matrix ρ∈S_m based …
unit trace). Consider a problem of estimation of an unknown density matrix ρ∈S_m based …
Low-rank approximation pursuit for matrix completion
AB Xu, D Xie - Mechanical Systems and Signal Processing, 2017 - Elsevier
We consider the matrix completion problem that aims to construct a low rank matrix X that
approximates a given large matrix Y from partially known sample data in Y. In this paper we …
approximates a given large matrix Y from partially known sample data in Y. In this paper we …