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arXiv:1510.02736 · Physical Review A

Strictly-complete measurements for bounded-rank quantum-state tomography

Charles H. Baldwin, Ivan Deutsch, Amir Kalev

Published May 6, 2016• 50 citations
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Abstract

We consider the problem of quantum-state tomography under the assumption that the state is pure, and more generally that its rank is bounded by a given value $r$. In this scenario two notions of informationally complete measurements emerge: rank-$r$-complete measurements and rank-$r$ strictly-complete measurements. Whereas in the first notion, a rank-$r$ state is uniquely identified from within the set of rank-$r$ states, in the second notion the same state is uniquely identified from within the set of all physical states, of any rank. We argue, therefore, that strictly-complete measurements are compatible with convex optimization, and we prove that they allow robust quantum-state estimation in the presence of experimental noise. We also show that rank-$r$ strictly-complete measurements are as efficient as rank-$r$-complete measurements. We construct examples of strictly-complete measurements and give a complete description of their structure in the context of matrix completion. Moreover, we numerically show that a few random bases form such measurements. We demonstrate the efficiency-robustness property for different strictly-complete measurements with numerical experiments. We thus conclude that only strictly-complete measurements are useful for practical tomography.

DOI
10.1103/physreva.93.052105

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We consider the problem of quantum-state tomography under the assumption that the state is pure, and more generally that its rank is bounded by a given value rrr. In this scenario two notions of informationally complete measurements emerge: rank-rrr-complete measurements and rank-rrr strictly-complete measurements. Whereas in the first notion, a rank-rrr state is uniquely identified from within the set of rank-rrr states, in the second notion the same state is uniquely identified from within the set of all physical states, of any rank. We argue, therefore, that strictly-complete measurements are compatible with convex optimization, and we prove that they allow robust quantum-state estimation in the presence of experimental noise. We also show that rank-rrr strictly-complete measurements are as efficient as rank-rrr-complete measurements. We construct examples of strictly-complete measurements and give a complete description of their structure in the context of matrix completion. Moreover, we numerically show that a few random bases form such measurements. We demonstrate the efficiency-robustness property for different strictly-complete measurements with numerical experiments. We thus conclude that only strictly-complete measurements are useful for practical tomography.
We study the problem of quantum-state tomography under the assumption that the state of the system is close to pure. In this context, an efficient measurements that one typically formulates uniquely identify a pure state from within the set of other pure states. In general such measurements are not robust in the presence of measurement noise and other imperfections, and therefore are less practical for tomography. We argue here that state tomography experiments should instead be done using measurements that can distinguish a pure state from {\em any} other quantum state, of any rank. We show that such nontrivial measurements follows from the physical constraint that the density matrix is positive semidefinite and prove that these measurements yield a robust estimation of the state. We assert that one can implement such tomography relatively simply by measuring only a few random orthonormal bases; our conjecture is supported by numerical evidence. These results are generalized for estimation of states close to bounded-rank.
We introduce a class of informationally complete positive-operator-valued measures which are, in analogy with a tight frame, ‘as close as possible’ to orthonormal bases for the space of quantum states. These measures are distinguished by an exceptionally simple state-reconstruction formula which allows ‘painless’ quantum state tomography. Complete sets of mutually unbiased bases and symmetric informationally complete positive-operator-valued measures are both members of this class, the latter being the unique minimal rank-one members. Recast as ensembles of pure quantum states, the rank-one members are in fact equivalent to weighted 2-designs in complex projective space. These measures are shown to be optimal for quantum cloning and linear quantum state tomography.
Prior to a measurement in a quantum-state tomography experiment, it is important to evaluate the performance of this measurement with respect to the average accuracy in state estimation. We propose a fast and reliable numerical certification of measurement performance that is applicable to any known quantum measurement. This numerical method is based on the statistical theory of unbiased estimation that is valid for any physically accessible quantum state that is necessarily full rank in the limit of a large number of measurement copies, and the Hoeffding inequality that applies to bounded statistical quantities in the quantum state space. We present the use of this straightforward certification procedure by illustrating the convergence to optimal pure-state tomography with an increasing number of overcomplete measurement outcomes. Furthermore, we demonstrate that the performances of symmetric informationally complete measurements and mutually unbiased bases, which are commonly regarded as optimal measurements, can be easily beaten in tomographic performance with randomly generated measurements that are only slightly more informationally overcomplete. Two important classes of random measurements are also discussed with the help of our numerical machinery.
Intuitively, if a density operator has small rank, then it should be easier to estimate from experimental data, since in this case only a few eigenvectors need to be learned. We prove two complementary results that confirm this intuition. Firstly, we show that a low-rank density matrix can be estimated using fewer copies of the state, i.e. the sample complexity of tomography decreases with the rank. Secondly, we show that unknown low-rank states can be reconstructed from an incomplete set of measurements, using techniques from compressed sensing and matrix completion. These techniques use simple Pauli measurements, and their output can be certified without making any assumptions about the unknown state. In this paper, we present a new theoretical analysis of compressed tomography, based on the restricted isometry property for low-rank matrices. Using these tools, we obtain near-optimal error bounds for the realistic situation where the data contain noise due to finite statistics, and the density matrix is full-rank with decaying eigenvalues. We also obtain upper bounds on the sample complexity of compressed tomography, and almost-matching lower bounds on the sample complexity of any procedure using adaptive sequences of Pauli measurements. Using numerical simulations, we compare the performance of two compressed sensing estimators—the matrix Dantzig selector and the matrix Lasso—with standard maximum-likelihood estimation (MLE). We find that, given comparable experimental resources, the compressed sensing estimators consistently produce higher fidelity state reconstructions than MLE. In addition, the use of an incomplete set of measurements leads to faster classical processing with no loss of accuracy. Finally, we show how to certify the accuracy of a low-rank estimate using direct fidelity estimation, and describe a method for compressed quantum process tomography that works for processes with small Kraus rank and requires only Pauli eigenstate preparations and Pauli measurements.
Quantum state tomography seeks to reconstruct an unknown state from measurement statistics. A finite measurement (POVM) is \emph{pure-state informationally complete} (PSI-Complete) if the outcome probabilities determine any pure state up to a global phase. We study \emph{rank-one} POVMs that are minimally sufficient for this task. We call such a POVM \emph{vital} if it is PSI-Complete but every proper subcollection is not PSI-Complete. We prove sharp upper bounds on the size of vital rank-one POVMs in dimension nnn: the size is at most (n+12)\binom{n+1}{2}(2n+1​) over R\mathbb{R}R and at most n2n^{2}n2 over C\mathbb{C}C, and we give constructions that attain these bounds. In the real case, we further exhibit a connection to block designs: whenever w∣n(n−1)w \mid n(n-1)w∣n(n−1), an (n,w,w−1)(n,w,w-1)(n,w,w−1) design produces a vital rank-one POVM with n+n(n−1)/wn + n(n-1)/wn+n(n−1)/w outcomes. We provide explicit constructions for w=2,n−1w=2,n-1w=2,n−1, and nnn.
We establish methods for quantum state tomography based on compressed sensing. These methods are specialized for quantum states that are fairly pure, and they offer a significant performance improvement on large quantum systems. In particular, they are able to reconstruct an unknown density matrix of dimension d and rank r using O(rdlog²d) measurement settings, compared to standard methods that require d² settings. Our methods have several features that make them amenable to experimental implementation: they require only simple Pauli measurements, use fast convex optimization, are stable against noise, and can be applied to states that are only approximately low rank. The acquired data can be used to certify that the state is indeed close to pure, so no a priori assumptions are needed.