搜索结果：tomography

A longstanding belief in quantum tomography is that estimating a mixed state is far harder than estimating a pure state. This is borne out in the mathematics, where mixed state algorithms have always required more sophisticated techniques to design and analyze than pure state algorithms. We present a new approach to tomography demonstrating that, contrary to this belief, state-of-the-art mixed state tomography follows easily and naturally from pure state algorithms. We analyze the following strategy: given $n$ copies of an unknown state $ρ$, convert them into copies of a purification $|ρ\rangle$; run a pure state tomography algorithm to produce an estimate of $|ρ\rangle$; and output the resulting estimate of $ρ$. The purification subroutine was recently discovered via the "acorn trick" of Tang, Wright, and Zhandry. With this strategy, we obtain the first tomography algorithm which is sample-optimal in all parameters. For a rank-$r$ $d$-dimensional state, it uses $n = O((rd + \log(1/δ))/\varepsilon)$ samples to output an estimate which is $\varepsilon$-close in fidelity with probability at least $1-δ$. This algorithm also uses poly$(n)$ gates, making it the first gate-efficient tomo

Characterizing quantum systems is a fundamental task that enables the development of quantum technologies. Various approaches, ranging from full tomography to instances of classical shadows, have been proposed to this end. However, quantum states that are being prepared in practice often involve families of quantum states characterized by continuous parameters, such as the time evolution of a quantum state. In this work, we extend the foundations of quantum state tomography to parametrized quantum states. We introduce a framework that unifies different notions of tomography and use it to establish a natural figure of merit for tomography of parametrized quantum states. Building on this, we provide an explicit algorithm that combines signal processing techniques with a tomography scheme to recover an approximation to the parametrized quantum state equipped with explicit guarantees. Our algorithm uses techniques from compressed sensing to exploit structure in the parameter dependence and operates with a plug and play nature, using the underlying tomography scheme as a black box. In an analogous fashion, we derive a figure of merit that applies to parametrized quantum channels. Substi

Partial tomography, which focuses on reconstructing reduced density matrices (RDMs), has emerged as a promising approach for characterizing complex quantum systems, particularly when full state tomography is impractical. Recently, overlapping tomography has been proposed as an efficient method for determining all $k$-qubit RDMs using logarithmic polynomial measurements, though it has not yet reached the ultimate limit. Here, we introduce a unified framework for optimal quantum overlapping tomography by mapping the problem to the clique cover model. This framework provides the most efficient and experimentally feasible measurement schemes to date, significantly reducing the measurement costs. Our approach is also applicable to determining RDMs with different topological structures. Moreover, we experimentally validate the feasibility of our schemes on practical nuclear spin processor using average measurements and further apply our method to noisy data from a superconducting quantum processor employing projection measurements. The results highlight the strong power of overlapping tomography, paving the way for advanced quantum system characterization and state property learning in t

Atomic electron tomography (AET) enables the determination of 3D atomic structures by acquiring a sequence of 2D tomographic projection measurements of a particle and then computationally solving for its underlying 3D representation. Classical tomography algorithms solve for an intermediate volumetric representation that is post-processed into the atomic structure of interest. In this paper, we reformulate the tomographic inverse problem to solve directly for the locations and properties of individual atoms. We parameterize an atomic structure as a collection of Gaussians, whose positions and properties are learnable. This representation imparts a strong physical prior on the learned structure, which we show yields improved robustness to real-world imaging artifacts. Simulated experiments and a proof-of-concept result on experimentally-acquired data confirm our method's potential for practical applications in materials characterization and analysis with Transmission Electron Microscopy (TEM). Our code is available at https://github.com/nalinimsingh/gaussian-atoms.

I present a concise, first principles metrological framework for imaging dielectric biomaterials by probing the full phase space (Wigner) distribution of a quantum electromagnetic field. Building on a rigorous multilayer Maxwell and Cole Cole model for stratified tissue, my method (Quantum Phase space Tomography, QPST) couples analytical forward theory with quantum metrology and Bayesian inference. I prepare a structured quantum EM probe (e.g. a squeezed microwave pulse) that interacts with tissue and then perform full quantum state tomography of the outgoing field. The recovered Wigner quasi probability reveals subwavelength and non classical features lost in classical imaging. By projecting the measurement onto the analytically derived tissue response manifold, I recover key physiological parameters (e.g. layer thickness, dispersion). I further define a Dielectric Anaplasia Metric (DAM) that quantifies tissue microstructural heterogeneity (e.g. malignancy) via deviations in Cole Cole parameters. My design leverages state of the art quantum sensors (e.g. NV diamond magnetometers) and advanced inverse algorithms (physics informed neural networks, diffusion priors). Numerical exampl

In this paper, we introduce silhouette tomography, a novel formulation of X-ray computed tomography that relies only on the geometry of the imaging system. We formulate silhouette tomography mathematically and provide a simple method for obtaining a particular solution to the problem, assuming that any solution exists. We then propose a supervised reconstruction approach that uses a deep neural network to solve the silhouette tomography problem. We present experimental results on a synthetic dataset that demonstrate the effectiveness of the proposed method.

Advances in quantum technology require scalable techniques to efficiently extract information from a quantum system, such as expectation values of observables or its entropy. Traditional tomography is limited to a handful of qubits and shadow tomography has been suggested as a scalable replacement for larger systems. Shadow tomography is conventionally analysed based on outcomes of ideal projective measurements on the system upon application of randomised unitaries. Here, we suggest that shadow tomography can be much more straightforwardly formulated for generalised measurements, or positive operator valued measures. Based on the idea of the least-square estimator, shadow tomography with generalised measurements is both more general and simpler than the traditional formulation with randomisation of unitaries. In particular, this formulation allows us to analyse theoretical aspects of shadow tomography in detail. For example, we provide a detailed study of the implication of symmetries in shadow tomography. Shadow tomography with generalised measurements is also indispensable in realistic implementation of quantum mechanical measurements, when noise is unavoidable. Moreover, we also

We present SA-DSVN, a 3D convolutional architecture for voxel-level segmentation of structural defects in reinforced concrete using cosmic-ray muon tomography. Unlike conventional reconstruction methods (POCA, MLSD) that rely solely on muon scattering angles, our approach jointly processes scattering kinematics (9 channels) and secondary electromagnetic shower multiplicities (40 channels) through independent encoder streams fused via cross-attention. Training data were generated using Vega, a cloud-native Geant4 simulation framework, producing 4.5 million muon events across 900 volumes containing four defect types - honeycombing, shear fracture, corrosion voids, and delamination - embedded within a dense 7x7 rebar cage. A five-variant ablation study demonstrates that the shower multiplicity stream alone accounts for the majority of discriminative power, raising defect-mean Dice from 0.535 (scattering only) to 0.685 (shower only). On 60 independently simulated validation volumes, the model achieves 96.3% voxel accuracy, per-defect Dice scores of 0.59-0.81, and 100% volume-level detection sensitivity at 10 ms inference per volume. These results establish secondary shower multiplicity

Quantum process tomography is a critical capability for building quantum computers, enabling quantum networks, and understanding quantum sensors. Like quantum state tomography, the process tomography of an arbitrary quantum channel requires a number of measurements that scale exponentially in the number of quantum bits affected. However, the recent field of shadow tomography, applied to quantum states, has demonstrated the ability to extract key information about a state with only polynomially many measurements. In this work, we apply the concepts of shadow state tomography to the challenge of characterizing quantum processes. We make use of the Choi isomorphism to directly apply rigorous bounds from shadow state tomography to shadow process tomography, and we find additional bounds on the number of measurements that are unique to process tomography. Our results, which include algorithms for implementing shadow process tomography enable new techniques including evaluation of channel concatenation and the application of channels to shadows of quantum states. This provides a dramatic improvement for understanding large-scale quantum systems.

This study compares two statistical approaches to image reconstruction in single-photon emission computed tomography (SPECT). We evaluated the widely used Ordered Subset Expectation Maximization (OSEM) algorithm and the newer Maximum a Posteriori approach with Entropy prior (MAP-Ent) approach in the context of quantifying radiopharmaceutical uptake in pathological lesions. Numerical experiments were performed using a digital twin of the standardized NEMA IEC phantom, which contains six spheres of varying diameters to simulate lesions. Quantitative accuracy was assessed using the maximum recovery coefficient (RCmax), defined as the ratio of the reconstructed maximum activity to the true value. The study shows that OSEM exhibits unstable convergence during iterations, leading to noise and edge artifacts in lesion images. Post-filtering stabilizes the reconstruction and ensures convergence, producing RCmax-size curves that could be used as correction factors in clinical evaluations. However, this approach significantly underestimates uptake in small lesions and may even lead to the complete loss of small lesions on reconstructed images. In contrast, MAP-Ent demonstrates fundamentally

Advances in instrumentation and computation have enabled increasingly sophisticated tomographic reconstruction methods. However, existing evaluation practices -- often based on simple phantoms and global image metrics -- are limited in their ability to differentiate among modern high-fidelity reconstructions. A standardized, quantitative framework capable of revealing subtle yet meaningful differences is therefore required. We introduce such a framework, built upon two core components. The first is a set of four standardized reference images -- Source, Detector, Ideal, and Realistic -- each derived from physical modeling and representing a distinct stage in the imaging and reconstruction chain. The second is a suite of diagnostic and quantitative tools that remain sensitive in regimes where conventional metrics (e.g., SSIM, PSNR, NMSE, CC) tend to saturate. These include pixel-wise $χ^2$ and difference maps, their quantitative characterization, spectral decomposition of intensity distributions, and Region-of-Interest (RoI)-based metrics. Application of this framework to MLEM and RISE-1 reconstructions using software phantoms demonstrates its ability to expose discrepancies that mig

Quantum tomography is one of the major challenges of large-scale quantum information research due to the exponential time complexity. In this work, we develop and apply a Bayesian state estimation method to experimentally demonstrate quantum overlapping tomography [Phys. Rev. Lett. \textbf{124}, 100401 (2020)], a scheme intent on characterizing critical information of a many-body quantum system in logarithmic time complexity. By comparing the measurement results of full state tomography and overlapping tomography, we show that overlapping tomography gives accurate information of the system with much fewer state measurements than full state tomography.

Precise reconstruction of unknown quantum states from measurement data, a process commonly called quantum state tomography, is a crucial component in the development of quantum information processing technologies. Many different tomography methods have been proposed over the years. Maximum likelihood estimation is a prominent example, being the most popular method for a long period of time. Recently, more advanced neural network methods have started to emerge. Here, we go back to basics and present a method for continuous variable state reconstruction that is both conceptually and practically simple, based on convex optimization. Convex optimization has been used for process tomography and qubit state tomography, but seems to have been overlooked for continuous variable quantum state tomography. We demonstrate high-fidelity reconstruction of an underlying state from data corrupted by thermal noise and imperfect detection, for both homodyne and heterodyne measurements. A major advantage over other methods is that convex optimization algorithms are guaranteed to converge to the optimal solution.

Ultrasound computed tomography (USCT) quantifies acoustic tissue properties such as the speed-of-sound (SOS). Although full-waveform inversion (FWI) is an effective method for accurate SOS reconstruction, it can be computationally challenging for large-scale problems. Deep learning-based image-to-image learned reconstruction (IILR) methods can offer computationally efficient alternatives. This study investigates the impact of the chosen input modalities on IILR methods for high-resolution SOS reconstruction in USCT. The selected modalities are traveltime tomography (TT) and reflection tomography (RT), which produce a low-resolution SOS map and a reflectivity map, respectively. These modalities have been chosen for their lower computational cost relative to FWI and their capacity to provide complementary information: TT offers a direct SOS measure, while RT reveals tissue boundary information. Systematic analyses were facilitated by employing a virtual USCT imaging system with anatomically realistic numerical breast phantoms. Within this testbed, a supervised convolutional neural network (CNN) was trained to map dual-channel (TT and RT images) to a high-resolution SOS map. Single-in

Tomography is the three-dimensional reconstruction of an object from images taken at different angles. The term classical tomography is used, when the imaging beam travels in straight lines through the object. This assumption is valid for light with short wavelengths, for example in x-ray tomography. For classical tomography, a commonly used reconstruction method is the filtered back-projection algorithm which yields fast and stable object reconstructions. In the context of single-cell imaging, the back-projection algorithm has been used to investigate the cell structure or to quantify the refractive index distribution within single cells using light from the visible spectrum. Nevertheless, these approaches, commonly summarized as optical projection tomography, do not take into account diffraction. Diffraction tomography with the Rytov approximation resolves this issue. The explicit incorporation of the wave nature of light results in an enhanced reconstruction of the object's refractive index distribution. Here, we present a full literature review of diffraction tomography. We derive the theory starting from the wave equation and discuss its validity with the focus on applications

Standard quantum process tomography on a $d$-dimensional input is performed by preparing several states of an input probe that then evolve under the action of the quantum channel corresponding to the progress. The final states of the probe are reconstructed by means of state tomography. An alternative is offered by ancilla-assisted process tomography: a single probe-ancilla state is used, and the correlations existing between probe and ancilla are exploited to fully reconstruct the information on the channel. In order for ancilla-assisted process tomography to be possible, the probe-ancilla input state does not need to be entangled, but still needs to have maximal operator Schmidt rank. Here we establish and analyze a framework for process tomography that interpolates between these two methods, aiming at exploiting any correlations that may exist between probe and ancilla to allow process tomography with as few input preparations as possible, when the probe-ancilla state may be operator-Schmidt-rank deficient. The main object of our investigation is the minimal number of initial local operations on the input probe for a given starting probe-ancilla state that are needed to allow pr

The overhead of internal network monitoring motivates techniques of network tomography. Network coding (NC) presents a new opportunity for network tomography as NC introduces topology-dependent correlation that can be further exploited in topology estimation. Compared with traditional methods, network tomography with NC has many advantages such as the improvement of tomography accuracy and the reduction of complexity in choosing monitoring paths. In this paper we first introduce the problem of tomography with NC and then propose the taxonomy criteria to classify various methods. We also present existing solutions and future trend. We expect that our comprehensive review on network tomography with NC can serve as a good reference for researchers and practitioners working in the area.

Whereas in standard quantum state tomography one estimates an unknown state by performing various measurements with known devices, and whereas in detector tomography one estimates the POVM elements of a measurement device by subjecting to it various known states, we consider here the case of SPAM (state preparation and measurement) tomography where neither the states nor the measurement device are assumed known. For $d$-dimensional systems measured by $d$-outcome detectors, we find there are at most $d^2(d^2-1)$ "gauge" parameters that can never be determined by any such experiment, irrespective of the number of unknown states and unknown devices. For the case $d=2$ we find new gauge-invariant quantities that can be accessed directly experimentally and that can be used to detect and describe SPAM errors. In particular, we identify conditions whose violations detect the presence of correlations between SPAM errors. From the perspective of SPAM tomography, standard quantum state tomography and detector tomography are protocols that fix the gauge parameters through the assumption that some set of fiducial measurements is known or that some set of fiducial states is known, respectively

We investigate the redshift dependence of the Hubble tension by comparing the luminosity distances obtained using an up-to-date BAO dataset (including the latest DESI data) calibrated with the CMB-inferred sound horizon, and the Pantheon+ SnIa distances calibrated with Cepheids. Using a redshift tomography method, we find: 1) The BAO-inferred distances are discrepant with the Pantheon+ SnIa distances across all redshift bins considered, with the discrepancy level varying with redshift. 2) The distance discrepancy is more pronounced at lower redshifts ($z \in [0.1,0.8]$) compared to higher redshifts ($z\in [0.8,2.3]$). The consistency of $Λ$CDM best fit parameters obtained in high and low redshift bins of both BAO and SnIa samples is investigated and we confirm that the tension reduces at high redshifts. Also a mild tension between the redshift bins is identified at higher redshifts for both the BAO and Pantheon+ data with respect to the best fit value of $H_0$ in agreement with previous studies which find hints for an 'evolution' of $H_0$ in the context of $Λ$CDM. These results confirm that the low redshift BAO and SnIa distances can only become consistent through a re-evaluation o

Using the the convex semidefinite programming method and superoperator formalism we obtain the finite quantum tomography of some mixed quantum states such as: qudit tomography, N-qubit tomography, phase tomography and coherent spin state tomography, where that obtained results are in agreement with those of References \cite{schack,Pegg,Barnett,Buzek,Weigert}.