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 systems. In this paper, we study estimation of high-dimensional density matrices based on Pauli measurements. In particular, under appropriate notion of sparsity, we establish the minimax optimal rates of convergence for estimation of the density matrix under both the spectral and Frobenius norm losses; and show how these rates can be achieved by a common thresholding approach. Numerical performance of the proposed estimator is also investigated
The estimation of the density matrix of a k-level quantum system is studied when the parametrization...
The construction of physically relevant low dimensional state models, and the design of appropriate ...
The recovery of an unknown density matrix of large size requires huge computational resources. The r...
Quantum state tomography aims to determine the state of a quantum system as represented by a density...
Projected least squares is an intuitive and numerically cheap technique for quantum state tomography...
Projected least squares is an intuitive and numerically cheap technique for quantum state tomography...
Intuitively, if a density operator has small rank, then it should be easier to estimate from experim...
We establish methods for quantum state tomography based on compressed sensing. These methods are spe...
© 2015 IOP Publishing Ltd and Deutsche Physikalische Gesellschaft. We give bounds on the average fid...
We construct minimax optimal non-asymptotic confidence sets for low rank matrix recovery algorithms ...
The goal of tomography is to reconstruct the density matrix of a physical system through a series of...
5+10 pages, 2+1 figuresInternational audienceProjected least squares (PLS) is an intuitive and numer...
Quantum state tomography is a powerful but resource-intensive, general solution for numerous quantum...
We present a universal technique for quantum state estimation based on the maximum-likelihood method...
Projected least squares is an intuitive and numerically cheap technique for quantum state tomography...
The estimation of the density matrix of a k-level quantum system is studied when the parametrization...
The construction of physically relevant low dimensional state models, and the design of appropriate ...
The recovery of an unknown density matrix of large size requires huge computational resources. The r...
Quantum state tomography aims to determine the state of a quantum system as represented by a density...
Projected least squares is an intuitive and numerically cheap technique for quantum state tomography...
Projected least squares is an intuitive and numerically cheap technique for quantum state tomography...
Intuitively, if a density operator has small rank, then it should be easier to estimate from experim...
We establish methods for quantum state tomography based on compressed sensing. These methods are spe...
© 2015 IOP Publishing Ltd and Deutsche Physikalische Gesellschaft. We give bounds on the average fid...
We construct minimax optimal non-asymptotic confidence sets for low rank matrix recovery algorithms ...
The goal of tomography is to reconstruct the density matrix of a physical system through a series of...
5+10 pages, 2+1 figuresInternational audienceProjected least squares (PLS) is an intuitive and numer...
Quantum state tomography is a powerful but resource-intensive, general solution for numerous quantum...
We present a universal technique for quantum state estimation based on the maximum-likelihood method...
Projected least squares is an intuitive and numerically cheap technique for quantum state tomography...
The estimation of the density matrix of a k-level quantum system is studied when the parametrization...
The construction of physically relevant low dimensional state models, and the design of appropriate ...
The recovery of an unknown density matrix of large size requires huge computational resources. The r...