Adaptive Quantum State Tomography Research Articles

Adaptive Quantum State Tomography with Active Learning

Recently, tremendous progress has been made in the field of quantum science and technologies: different platforms for quantum simulation as well as quantum computing, ranging from superconducting qubits to neutral atoms, are starting to reach unprecedentedly large systems. In order to benchmark these systems and gain physical insights, the need for efficient tools to characterize quantum states arises. The exponential growth of the Hilbert space with system size renders a full reconstruction of the quantum state prohibitively demanding in terms of the number of necessary measurements. Here we propose and implement an efficient scheme for quantum state tomography using active learning. Based on a few initial measurements, the active learning protocol proposes the next measurement basis, designed to yield the maximum information gain. We apply the active learning quantum state tomography scheme to reconstruct different multi-qubit states with varying degree of entanglement as well as to ground states of the XXZ model in 1D and a kinetically constrained spin chain. In all cases, we obtain a significantly improved reconstruction as compared to a reconstruction based on the exact same number of measurements and measurement configurations, but with randomly chosen basis configurations. Our scheme is highly relevant to gain physical insights in quantum many-body systems as well as for benchmarking and characterizing quantum devices, e.g. for quantum simulation, and paves the way for scalable adaptive protocols to probe, prepare, and manipulate quantum systems.

Adaptive quantum state tomography via weak value

Quantum state tomography strategy via weak value is valid for arbitrary dimensional systems, and can be easily realized. However, the efficiency of quantum state tomography strategy via weak value is lower than that of the conventional quantum state tomography (QST), especially for high-dimensional systems. To improve the efficiency of the scheme, we propose a two-step adaptive quantum state tomography via weak value for pure states (AQSTW). In AQSTW, by using appropriate measurement strength, the efficiency of AQSTW is significantly larger than that of the conventional QST, especially for high-dimensional systems. And AQSTW can be easily implemented. These results demonstrate AQSTW may be useful in the measurements of the unknown quantum states.

Adaptive quantum state tomography with iterative particle filtering

Several Bayesian estimation-based heuristics have been developed to perform quantum state tomography (QST). Their ability to quantify uncertainties using region estimators and include a priori knowledge of the experimentalists makes this family of methods an attractive choice for QST. However, specialized techniques for pure states do not work well for mixed states and vice versa. In this paper, we present an adaptive particle filter (PF)-based QST protocol which improves the scaling of fidelity compared to nonadaptive Bayesian schemes for arbitrary multi-qubit states. This is due to the protocol’s unabating perseverance to find the states’ diagonal bases and more systematic handling of enduring problems in popular PF methods relating to the subjectivity of informative priors and the invalidity of particles produced by resamplers. Numerical examples and implementation on IBM quantum devices demonstrate improved performance for arbitrary quantum states and the application readiness of our proposed scheme.

Adaptive quantum state tomography with neural networks

Current algorithms for quantum state tomography (QST) are costly both on the experimental front, requiring measurement of many copies of the state, and on the classical computational front, needing a long time to analyze the gathered data. Here, we introduce neural adaptive quantum state tomography (NAQT), a fast, flexible machine-learning-based algorithm for QST that adapts measurements and provides orders of magnitude faster processing while retaining state-of-the-art reconstruction accuracy. As in other adaptive QST schemes, measurement adaptation makes use of the information gathered from previous measured copies of the state to perform a targeted sensing of the next copy, maximizing the information gathered from that next copy. Our NAQT approach allows for a rapid and seamless integration of measurement adaptation and statistical inference, using a neural-network replacement of the standard Bayes’ update, to obtain the best estimate of the state. Our algorithm, which falls into the machine learning subfield of “meta-learning” (in effect “learning to learn” about quantum states), does not require any ansatz about the form of the state to be estimated. Despite this generality, it can be retrained within hours on a single laptop for a two-qubit situation, which suggests a feasible time-cost when extended to larger systems and potential speed-ups if provided with additional structure, such as a state ansatz.

Experimental adaptive quantum state tomography based on rank-preserving transformations

Quantum tomography is a process of quantum state recognition with multiple measurements. An essential goal for the quantum tomography algorithm is to find measurements that will maximize the useful information about an unknown quantum state obtained through measurements. One of the recently proposed methods of quantum tomography is the algorithm based on rank-preserving transformations. The main idea is to transform a basic measurement set in the way to provide a situation that is equivalent to measuring the maximally mixed state. As long as the tomography of the fully mixed state has the fastest convergence comparing to other states, this method is expected to be highly accurate. We present numerical and experimental comparisons of rank-preserving tomography with another adaptive method, which includes measurements in the estimator eigenbasis and with random-basis tomography. We also study ways to improve the efficiency of the rank-preserving transformations method using the transformation unitary freedom and a measurement set complementation.

Enhancing multi-step quantum state tomography by PhaseLift

Multi-photon system has been studied by many groups, however the biggest challenge faced is the number of copies of an unknown state are limited and far from detecting quantum entanglement. The difficulty to prepare copies of the state is even more serious for the quantum state tomography. One possible way to solve this problem is to use adaptive quantum state tomography, which means to get a preliminary density matrix in the first step and revise it in the second step. In order to improve the performance of adaptive quantum state tomography, we develop a new distribution scheme of samples and extend it to three steps, that is to correct it once again based on the density matrix obtained in the traditional adaptive quantum state tomography. Our numerical results show that the mean square error of the reconstructed density matrix by our new method is improved to the level from 10−4 to 10−9 for several tested states. In addition, PhaseLift is also applied to reduce the required storage space of measurement operator.

Adaptive quantum state tomography via linear regression estimation: Theory and two-qubit experiment

Adaptive techniques have great potential for wide application in enhancing the precision of quantum parameter estimation. We present an adaptive quantum state tomography protocol for finite dimensional quantum systems and experimentally implement the adaptive tomography protocol on two-qubit systems. In this adaptive quantum state tomography protocol, an adaptive measurement strategy and a recursive linear regression estimation algorithm are performed. Numerical results show that our adaptive quantum state tomography protocol can outperform tomography protocols using mutually unbiased bases and the two-stage mutually unbiased bases adaptive strategy, even with the simplest product measurements. When nonlocal measurements are available, our adaptive quantum state tomography can beat the Gill–Massar bound for a wide range of quantum states with a modest number of copies. We use only the simplest product measurements to implement two-qubit tomography experiments. In the experiments, we use error-compensation techniques to tackle systematic error due to misalignments and imperfection of wave plates, and achieve about a 100-fold reduction of the systematic error. The experimental results demonstrate that the improvement of adaptive quantum state tomography over nonadaptive tomography is significant for states with a high level of purity. Our results also show that this adaptive tomography method is particularly effective for the reconstruction of maximally entangled states, which are important resources in quantum information.

Achieving quantum precision limit in adaptive qubit state tomography

AbstractThe precision limit in quantum state tomography is of great interest not only to practical applications but also to foundational studies. However, little is known about this subject in the multiparameter setting even theoretically due to the subtle information trade-off among incompatible observables. In the case of a qubit, the theoretic precision limit was determined by Hayashi as well as Gill and Massar, but attaining the precision limit in experiments has remained a challenging task. Here we report the first experiment that achieves this precision limit in adaptive quantum state tomography on optical polarisation qubits. The two-step adaptive strategy used in our experiment is very easy to implement in practice. Yet it is surprisingly powerful in optimising most figures of merit of practical interest. Our study may have significant implications for multiparameter quantum estimation problems, such as quantum metrology. Meanwhile, it may promote our understanding about the complementarity principle and uncertainty relations from the information theoretic perspective.

Experimental adaptive quantum tomography of two-qubit states

We report an experimental realization of adaptive Bayesian quantum state tomography for two-qubit states. Our implementation is based on the adaptive experimental design strategy proposed in [F.Husz\'ar and N.M.T.Houlsby, Phys.Rev.A 85, 052120 (2012)] and provides an optimal measurement approach in terms of the information gain. We address the practical questions, which one faces in any experimental application: the influence of technical noise, and behavior of the tomographic algorithm for an easy to implement class of factorized measurements. In an experiment with polarization states of entangled photon pairs we observe a lower instrumental noise floor and superior reconstruction accuracy for nearly-pure states of the adaptive protocol compared to a non-adaptive. At the same time we show, that for the mixed states the restriction to factorized measurements results in no advantage for adaptive measurements, so general measurements have to be used.

Experimental Adaptive Bayesian Tomography

We discuss an experimental realization of an adaptive quantum state tomography protocol. The method we suggested and tested takes advantage of a Bayesian approach to statistical inference and is naturally tailored for adaptive strategies. For pure states we observe close to 1/N scaling of infidelity with overall number of registered events N, while best non-adaptive protocols allow for 1 N scaling only. This is the theoretical limit for any tomographic protocol, and further improvement may only affect pre-factors in this power law. Also we consider particular strategies of the state reconstruction based on adequate and inadequate models and compare their scaling. Experiments have been performed for polarization qubits and ququarts, but the approach is readily adapted to any dimension. Our method does not take into account systematic errors caused, for example, by inaccuracies in retardant plates rotation. However for the reached values of infidelities of on the order of 10 – 10 we did not observe any deviations from expected behavior and were not able to identify the influence of systematic errors.

Quantum state estimation with informationally overcomplete measurements

We study informationally overcomplete measurements for quantum state estimation so as to clarify their tomographic significance as compared with minimal informationally complete measurements. We show that informationally overcomplete measurements can improve the tomographic efficiency significantly over minimal measurements when the states of interest have high purities. Nevertheless, the efficiency is still too limited to be satisfactory with respect to figures of merit based on monotone Riemannian metrics, such as the Bures metric and quantum Chernoff metric. In this way, we also pinpoint the limitation of nonadaptive measurements and motivate the study of more sophisticated measurement schemes. In the course of our study, we introduce the best linear unbiased estimator and show that it is equally efficient as the maximum likelihood estimator in the large-sample limit. This estimator may significantly outperform the canonical linear estimator for states with high purities. It is expected to play an important role in experimental designs and adaptive quantum state tomography besides its significance to the current study.

Adaptive Quantum State Tomography Improves Accuracy Quadratically

We introduce a simple protocol for adaptive quantum state tomography, which reduces the worst-case infidelity [1-F(ρ,ρ)] between the estimate and the true state from O(1/sqrt[N]) to O(1/N). It uses a single adaptation step and just one extra measurement setting. In a linear optical qubit experiment, we demonstrate a full order of magnitude reduction in infidelity (from 0.1% to 0.01%) for a modest number of samples (N ≈ 3 × 10(4)).

Experimental adaptive Bayesian tomography

We report an experimental realization of an adaptive quantum state tomography protocol. Our method takes advantage of a Bayesian approach to statistical inference and is naturally tailored for adaptive strategies. For pure states, we observe close to ${N}^{\ensuremath{-}1}$ scaling of infidelity with overall number of registered events, while the best nonadaptive protocols allow for ${N}^{\ensuremath{-}1/2}$ scaling only. Experiments are performed for polarization qubits, but the approach is readily adapted to any dimension.