The density matrices are positively semi-definite Hermitian matrices of unit trace that describe the state of a quantum system. The goal of the paper is to develop minimax lower bounds on error rates of estimation of low rank density matrices in trace regression models used in quantum state tomography (in particular, in the case of Pauli measurements) with explicit dependence of the bounds on the rank and other complexity parameters. Such bounds are established for several statistically relevant distances, including quantum versions of Kullback-Leibler divergence (relative entropy distance) and of Hellinger distance (so called Bures distance), and Schatten $p$ -norm distances. Sharp upper bounds and oracle inequalities for least squares estimator with von Neumann entropy penalization are obtained showing that minimax lower bounds are attained (up to logarithmic factors) for these distances.The density matrices are positively semi-definite Hermitian matrices of unit trace that describe the state of a quantum system. The goal of the paper is to develop minimax lower bounds on error rates of estimation of low rank density matrices in trace regression models used in quantum state tomography (in particular, in the case of Pauli measurements) with explicit dependence of the bounds on the rank and other complexity parameters. Such bounds are established for several statistically relevant distances, including quantum versions of Kullback-Leibler divergence (relative entropy distance) and of Hellinger distance (so called Bures distance), and Schatten $p$-norm distances. Sharp upper bounds and oracle inequalities for least squares estimator with von Neumann entropy penalization are obtained showing that minimax lower bounds are attained (up to logarithmic factors) for these distances.

arXiv:1507.05131Jul 17, 201526 citations

Estimation of low rank density matrices: Bounds in Schatten norms and other distances

Dong Xia, Vladimir I. Koltchinskii

Let $\mathcal{S}_{m}$ be the set of all $m\times m$ density matrices (Hermitian positively semi-definite matrices of unit trace). Consider a problem of estimation of an unknown density matrix $\rho\in\mathcal{S}_{m}$ based on outcomes of $n$ measurements of observables $X_{1},\dots,X_{n}\in\mathbb{H}_{m}$ ( $\mathbb{H}_{m}$ being the space of $m\times m$ Hermitian matrices) for a quantum system identically prepared $n$ times in state $\rho.$ Outcomes $Y_{1},\dots,Y_{n}$ of such measurements could be described by a trace regression model in which $\mathbb{E}_{\rho}(Y_{j}|X_{j})={\rm tr}(\rho X_{j}),j=1,\dots,n.$ The design variables $X_{1},\dots,X_{n}$ are often sampled at random from the uniform distribution in an orthonormal basis $\{E_{1},\dots,E_{m^{2}}\}$ of $\mathbb{H}_{m}$ (such as Pauli basis). The goal is to estimate the unknown density matrix $\rho$ based on the data $(X_{1},Y_{1}),\dots,(X_{n},Y_{n}).$ Let $\hat{Z}:=\frac{m^{2}}{n}\sum_{j=1}^{n}Y_{j}X_{j}$ and let $\check{\rho}$ be the projection of $\hat{Z}$ onto the convex set $\mathcal{S}_{m}$ of density matrices. It is shown that for estimator $\check{\rho}$ the minimax lower bounds in classes of low rank density matrices (established earlier) are attained up logarithmic factors for all Schatten $p$ -norm distances, $p\in[1,\infty]$ and for Bures version of quantum Hellinger distance. Moreover, for a slightly modified version of estimator $\check{\rho}$ the same property holds also for quantum relative entropy (Kullback-Leibler) distance between density matrices.Let $\mathcal{S}_{m}$ be the set of all $m\times m$ density matrices (Hermitian positively semi-definite matrices of unit trace). Consider a problem of estimation of an unknown density matrix $\rho\in\mathcal{S}_{m}$ based on outcomes of $n$ measurements of observables $X_{1},\dots,X_{n}\in\mathbb{H}_{m}$ ($\mathbb{H}_{m}$ being the space of $m\times m$ Hermitian matrices) for a quantum system identically prepared $n$ times in state $\rho.$ Outcomes $Y_{1},\dots,Y_{n}$ of such measurements could be described by a trace regression model in which $\mathbb{E}_{\rho}(Y_{j}|X_{j})={\rm tr}(\rho X_{j}),j=1,\dots,n.$ The design variables $X_{1},\dots,X_{n}$ are often sampled at random from the uniform distribution in an orthonormal basis $\{E_{1},\dots,E_{m^{2}}\}$ of $\mathbb{H}_{m}$ (such as Pauli basis). The goal is to estimate the unknown density matrix $\rho$ based on the data $(X_{1},Y_{1}),\dots,(X_{n},Y_{n}).$ Let \[\hat{Z}:=\frac{m^{2}}{n}\sum_{j=1}^{n}Y_{j}X_{j}\] and let $\check{\rho}$ be the projection of $\hat{Z}$ onto the convex set $\mathcal{S}_{m}$ of density matrices. It is shown that for estimator $\check{\rho}$ the minimax lower bounds in classes of low rank density matrices (established earlier) are attained up logarithmic factors for all Schatten $p$-norm distances, $p\in[1,\infty]$ and for Bures version of quantum Hellinger distance. Moreover, for a slightly modified version of estimator $\check{\rho}$ the same property holds also for quantum relative entropy (Kullback-Leibler) distance between density matrices.

arXiv:1604.04600Electronic Journal of StatisticsJan 1, 201616 citations

Estimation of low rank density matrices by Pauli measurements

Dong Xia

Density matrices are positively semi-definite Hermitian matrices with unit trace that describe the states of quantum systems. Many quantum systems of physical interest can be represented as high-dimensional low rank density matrices. A popular problem in quantum state tomography (QST) is to estimate the unknown low rank density matrix of a quantum system by conducting Pauli measurements. Our main contribution is twofold. First, we establish the minimax lower bounds in Schatten $p$ -norms with $1\leq p\leq+\infty$ for low rank density matrices estimation by Pauli measurements. In our previous paper [14], these minimax lower bounds are proved under the trace regression model with Gaussian noise and the noise is assumed to have common variance. In this paper, we prove these bounds under the Binomial observation model which meets the actual model in QST. Second, we study the Dantzig estimator (DE) for estimating the unknown low rank density matrix under the Binomial observation model by using Pauli measurements. In our previous papers [14] and [25], we studied the least squares estimator and the projection estimator, where we proved the optimal convergence rates for the least squares estimator in Schatten $p$ -norms with $1\leq p\leq2$ and, under a stronger condition, the optimal convergence rates for the projection estimator in Schatten $p$ -norms with $1\leq p\leq+\infty$ . In this paper, we show that the results of these two distinct estimators can be simultaneously obtained by the Dantzig estimator. Moreover, better convergence rates in Schatten norm distances can be proved for Dantzig estimator under conditions weaker than those needed in [14] and [25]. When the objective function of DE is replaced by the negative von Neumann entropy, we obtain sharp convergence rate in Kullback-Leibler divergence.Density matrices are positively semi-definite Hermitian matrices with unit trace that describe the states of quantum systems. Many quantum systems of physical interest can be represented as high-dimensional low rank density matrices. A popular problem in quantum state tomography (QST) is to estimate the unknown low rank density matrix of a quantum system by conducting Pauli measurements. Our main contribution is twofold. First, we establish the minimax lower bounds in Schatten $p$-norms with $1\leq p\leq+\infty$ for low rank density matrices estimation by Pauli measurements. In our previous paper [14], these minimax lower bounds are proved under the trace regression model with Gaussian noise and the noise is assumed to have common variance. In this paper, we prove these bounds under the Binomial observation model which meets the actual model in QST. Second, we study the Dantzig estimator (DE) for estimating the unknown low rank density matrix under the Binomial observation model by using Pauli measurements. In our previous papers [14] and [25], we studied the least squares estimator and the projection estimator, where we proved the optimal convergence rates for the least squares estimator in Schatten $p$-norms with $1\leq p\leq2$ and, under a stronger condition, the optimal convergence rates for the projection estimator in Schatten $p$-norms with $1\leq p\leq+\infty$. In this paper, we show that the results of these two distinct estimators can be simultaneously obtained by the Dantzig estimator. Moreover, better convergence rates in Schatten norm distances can be proved for Dantzig estimator under conditions weaker than those needed in [14] and [25]. When the objective function of DE is replaced by the negative von Neumann entropy, we obtain sharp convergence rate in Kullback-Leibler divergence.

arXiv:1610.04811Electronic Journal of StatisticsJan 1, 20174 citations

A projected gradient method for optimization over density matrices

Douglas Soares Gonçalves, Márcia A. Gomes-Ruggiero, Carlile Lavor

An ensemble of quantum states can be described by a Hermitian, positive semidefinite and unit trace matrix called density matrix. Thus, the study of methods for optimizing a certain function (energy, entropy) over the set of density matrices has a direct application to important problems in quantum information and computation. We propose a projected gradient method for solving such problems. By exploiting the geometry of the feasible set, which is the intersection of the cone of Hermitian positive semidefinite matrices with the hyperplane defined by the unit trace constraint, we describe an efficient procedure to compute the projection onto this set using the Frobenius norm. Some important applications, such as quantum state tomography, are described and numerical experiments illustrate the effectiveness of the method when compared to previous methods based on fixed-point iterations or semidefinite programming.An ensemble of quantum states can be described by a Hermitian, positive semidefinite and unit trace matrix called density matrix. Thus, the study of methods for optimizing a certain function (energy, entropy) over the set of density matrices has a direct application to important problems in quantum information and computation. We propose a projected gradient method for solving such problems. By exploiting the geometry of the feasible set, which is the intersection of the cone of Hermitian positive semidefinite matrices with the hyperplane defined by the unit trace constraint, we describe an efficient procedure to compute the projection onto this set using the Frobenius norm. Some important applications, such as quantum state tomography, are described and numerical experiments illustrate the effectiveness of the method when compared to previous methods based on fixed-point iterations or semidefinite programming.

Optimization methods & softwareSep 23, 201533 citations

Approximating quantum states by states of low rank

Nathaniel Johnston, Chi-Kwong Li

Given a positive integer k , it is natural to ask for a formula for the distance between a given density matrix (i.e., mixed quantum state) and the set of density matrices of rank at most k . This problem has already been solved when “distance” is measured in the trace or Frobenius norm. We solve it for all other unitary similarity invariant norms. We also present some consequences of our formula. For example, in the trace and Frobenius norms, the density matrix that is farthest from the set of low-rank density matrices is the maximally-mixed state, but this is not true in many other unitary similarity invariant norms.Given a positive integer k , it is natural to ask for a formula for the distance between a given density matrix (i.e., mixed quantum state) and the set of density matrices of rank at most k . This problem has already been solved when “distance” is measured in the trace or Frobenius norm. We solve it for all other unitary similarity invariant norms. We also present some consequences of our formula. For example, in the trace and Frobenius norms, the density matrix that is farthest from the set of low-rank density matrices is the maximally-mixed state, but this is not true in many other unitary similarity invariant norms.

arXiv:2510.08463Linear Algebra and its ApplicationsJan 1, 20260 citations

Maximum-likelihood estimation of the density matrix

Konrad Banaszek, Giacomo Mauro D’Ariano, Matteo G. A. Paris +1 more

We present a universal technique for quantum-state estimation based on the maximum-likelihood method. This approach provides a positive-definite estimate for the density matrix from a sequence of measurements performed on identically prepared copies of the system. The method is versatile and can be applied to multimode radiation fields as well as to spin systems. The incorporation of physical constraints, which is natural in the maximum-likelihood strategy, leads to a substantial reduction of statistical errors. Numerical implementation of the method is based on a particular form of the Gauss decomposition for positive-definite Hermitian matrices.We present a universal technique for quantum-state estimation based on the maximum-likelihood method. This approach provides a positive-definite estimate for the density matrix from a sequence of measurements performed on identically prepared copies of the system. The method is versatile and can be applied to multimode radiation fields as well as to spin systems. The incorporation of physical constraints, which is natural in the maximum-likelihood strategy, leads to a substantial reduction of statistical errors. Numerical implementation of the method is based on a particular form of the Gauss decomposition for positive-definite Hermitian matrices.

arXiv:quant-ph/9909052Physical Review ADec 8, 1999341 citations

p

-norm distances. Sharp upper bounds and oracle inequalities for least squares estimator with von Neumann entropy penalization are obtained showing that minimax lower bounds are attained (up to logarithmic factors) for these distances.

Let

\mathcal{S}_{m}

be the set of all

m\times m

density matrices (Hermitian positively semi-definite matrices of unit trace). Consider a problem of estimation of an unknown density matrix

\rho\in\mathcal{S}_{m}

based on outcomes of

n

measurements of observables

X_{1},\dots,X_{n}\in\mathbb{H}_{m}

(

\mathbb{H}_{m}

being the space of

m\times m

Hermitian matrices) for a quantum system identically prepared

n

times in state

\rho.

Outcomes

Y_{1},\dots,Y_{n}

of such measurements could be described by a trace regression model in which

\mathbb{E}_{\rho}(Y_{j}|X_{j})={\rm tr}(\rho X_{j}),j=1,\dots,n.

The design variables

X_{1},\dots,X_{n}

are often sampled at random from the uniform distribution in an orthonormal basis

\{E_{1},\dots,E_{m^{2}}\}

\mathbb{H}_{m}

(such as Pauli basis). The goal is to estimate the unknown density matrix

\rho

based on the data

(X_{1},Y_{1}),\dots,(X_{n},Y_{n}).

Let

\hat{Z}:=\frac{m^{2}}{n}\sum_{j=1}^{n}Y_{j}X_{j}

and let

\check{\rho}

be the projection of

\hat{Z}

onto the convex set

\mathcal{S}_{m}

of density matrices. It is shown that for estimator

\check{\rho}

the minimax lower bounds in classes of low rank density matrices (established earlier) are attained up logarithmic factors for all Schatten

p

-norm distances,

p\in[1,\infty]

and for Bures version of quantum Hellinger distance. Moreover, for a slightly modified version of estimator

\check{\rho}

the same property holds also for quantum relative entropy (Kullback-Leibler) distance between density matrices.

p

-norms with

1\leq p\leq+\infty

for low rank density matrices estimation by Pauli measurements. In our previous paper [14], these minimax lower bounds are proved under the trace regression model with Gaussian noise and the noise is assumed to have common variance. In this paper, we prove these bounds under the Binomial observation model which meets the actual model in QST. Second, we study the Dantzig estimator (DE) for estimating the unknown low rank density matrix under the Binomial observation model by using Pauli measurements. In our previous papers [14] and [25], we studied the least squares estimator and the projection estimator, where we proved the optimal convergence rates for the least squares estimator in Schatten

p

-norms with

1\leq p\leq2

and, under a stronger condition, the optimal convergence rates for the projection estimator in Schatten

p

-norms with

1\leq p\leq+\infty

. In this paper, we show that the results of these two distinct estimators can be simultaneously obtained by the Dantzig estimator. Moreover, better convergence rates in Schatten norm distances can be proved for Dantzig estimator under conditions weaker than those needed in [14] and [25]. When the objective function of DE is replaced by the negative von Neumann entropy, we obtain sharp convergence rate in Kullback-Leibler divergence.